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GIST-small-c4-en-classla_ParlaCAP

Text Classification
Safetensors
English
bert
sequence-classification
Eval Results (legacy)
Model card Files
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  1. README.md +684 -0
  2. all_results.json +15 -0
  3. config.json +74 -0
  4. eval_results.json +9 -0
  5. model.safetensors +3 -0
  6. predict_results.txt +0 -0
  7. report.md +35 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +58 -0
  11. train_results.json +9 -0
  12. trainer_state.json +3745 -0
  13. training_args.bin +3 -0
  14. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ language: en
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+ license: mit
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+ base_model:
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+ - avsolatorio/GIST-small-Embedding-v0
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+ datasets:
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+ - agentlans/en-document-classification
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+ tags:
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+ - bert
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+ - text-classification
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+ - sequence-classification
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+ metrics:
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+ - f1
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+ - loss
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+ model-index:
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+ - name: GIST-small-c4-en-classla_ParlaCAP
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ metrics:
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+ - type: f1
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+ value: 0.7949
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+ name: Evaluation F1
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+ - type: loss
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+ value: 0.7073
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+ name: Evaluation Loss
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+ ---
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+ # GIST-small-c4-en-classla_ParlaCAP
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+
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+ A fine-tuned version of the **bert** architecture (`BertForSequenceClassification`) optimized for the `text-classification` task.
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+
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+ - **Model type:** bert
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+ - **Problem Type:** single_label_classification
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+ - **Number of Labels:** 22
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+ - **Vocabulary Size:** 30522
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+ - **License:** MIT
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+
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+ ## Use
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+
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+ To get started with this model in Python using the Hugging Face Transformers library, run the following code:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+
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+ model_id = "agentlans/GIST-small-c4-en-classla_ParlaCAP"
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForSequenceClassification.from_pretrained(model_id)
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+
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+ text = "Replace this with your input text."
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+ inputs = tokenizer(text, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ logits = model(**inputs).logits
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+
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+ predicted_class_id = logits.argmax().item()
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+ predicted_class_name = model.config.id2label[predicted_class_id]
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+
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+ print(f"Predicted Class ID: {predicted_class_id}")
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+ print(f"Predicted Class Name: {predicted_class_name}")
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+ ```
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+
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+ ## Intended Uses & Limitations
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+
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+ ### Intended Use
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+ This model is designed for sequence classification tasks. Below are the specific class labels mapped to their corresponding IDs:
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+
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+ | Label ID | Label Name |
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+ |---|---|
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+ | 0 | Agriculture |
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+ | 1 | Civil Rights |
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+ | 2 | Culture |
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+ | 3 | Defense |
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+ | 4 | Domestic Commerce |
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+ | 5 | Education |
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+ | 6 | Energy |
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+ | 7 | Environment |
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+ | 8 | Foreign Trade |
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+ | 9 | Government Operations |
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+ | 10 | Health |
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+ | 11 | Housing |
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+ | 12 | Immigration |
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+ | 13 | International Affairs |
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+ | 14 | Labor |
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+ | 15 | Law and Crime |
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+ | 16 | Macroeconomics |
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+ | 17 | Other |
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+ | 18 | Public Lands |
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+ | 19 | Social Welfare |
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+ | 20 | Technology |
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+ | 21 | Transportation |
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+
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+ ## Training Details
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+
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+ ### Hyperparameters
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+ The following hyperparameters were used during fine-tuning:
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+ - **Learning Rate:** 5e-05
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+ - **Train Batch Size:** 8
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+ - **Eval Batch Size:** 8
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+ - **Optimizer:** OptimizerNames.ADAMW_TORCH_FUSED
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+ - **Number of Epochs:** 3.0
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+ - **Mixed Precision:** BF16
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+
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+ <details>
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+ <summary><b>Show Advanced Training Configuration</b></summary>
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+
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+ #### Optimization & Regularization
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+ - **Gradient Accumulation Steps:** 1
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+ - **Learning Rate Scheduler:** SchedulerType.LINEAR
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+ - **Warmup Steps:** 0
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+ - **Warmup Ratio:** None
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+ - **Weight Decay:** 0.0
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+ - **Max Gradient Norm:** 1.0
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+
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+ #### Hardware & Reproducibility
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+ - **Number of GPUs:** 1
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+ - **Seed:** 42
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+
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+ </details>
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+
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+ ## Training Results & Evaluation
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+
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+ During fine-tuning, the model achieved the following results on the evaluation set:
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+
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+ | Metric | Value |
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+ |---|---|
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+ | **Train Loss** | 0.5913 |
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+ | **Validation Loss** | 0.7073 |
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+ | **Validation F1 Score** | 0.7949 |
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+ | **Total FLOPs** | 3.4623e+16 |
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+
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+ For performance on the test set, [click here](report.md).
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+
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+ ### Speed Performance
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+ - **Training Runtime:** 4548.9417 seconds
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+ - **Train Samples per Second:** 462.006
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+ - **Evaluation Runtime:** 49.5493 seconds
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+ - **Eval Samples per Second:** 1767.349
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+
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+
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+ <details>
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+ <summary><b>Show Detailed Training Logs</b></summary>
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+
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+ ### Training Logs History
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+
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+ | Step | Epoch | Learning Rate | Training Loss | Validation Loss | Validation F1 |
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+ |---|---|---|---|---|---|
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+ | 500 | 0.006 | 4.9905e-05 | 2.168 | N/A | N/A |
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+ | 1000 | 0.011 | 4.9810e-05 | 1.4219 | N/A | N/A |
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+ | 1500 | 0.017 | 4.9715e-05 | 1.2196 | N/A | N/A |
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+ | 2000 | 0.023 | 4.9620e-05 | 1.1147 | N/A | N/A |
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+ | 2500 | 0.029 | 4.9524e-05 | 1.0641 | N/A | N/A |
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+ | 3000 | 0.034 | 4.9429e-05 | 1.0098 | N/A | N/A |
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+ | 3500 | 0.04 | 4.9334e-05 | 0.96 | N/A | N/A |
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+ | 4000 | 0.046 | 4.9239e-05 | 0.9432 | N/A | N/A |
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+ | 4500 | 0.051 | 4.9144e-05 | 0.9315 | N/A | N/A |
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+ | 5000 | 0.057 | 4.9049e-05 | 0.8722 | N/A | N/A |
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+ | 5500 | 0.063 | 4.8953e-05 | 0.9085 | N/A | N/A |
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+ | 6000 | 0.069 | 4.8858e-05 | 0.8721 | N/A | N/A |
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+ | 6500 | 0.074 | 4.8763e-05 | 0.8691 | N/A | N/A |
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+ | 7000 | 0.08 | 4.8668e-05 | 0.8439 | N/A | N/A |
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+ | 7500 | 0.086 | 4.8573e-05 | 0.8559 | N/A | N/A |
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+ | 8000 | 0.091 | 4.8478e-05 | 0.8378 | N/A | N/A |
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+ | 8500 | 0.097 | 4.8382e-05 | 0.8318 | N/A | N/A |
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+ | 9000 | 0.103 | 4.8287e-05 | 0.803 | N/A | N/A |
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+ | 9500 | 0.108 | 4.8192e-05 | 0.8421 | N/A | N/A |
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+ | 10000 | 0.114 | 4.8097e-05 | 0.7915 | N/A | N/A |
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+ | 10500 | 0.12 | 4.8002e-05 | 0.8655 | N/A | N/A |
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+ | 11000 | 0.126 | 4.7907e-05 | 0.8285 | N/A | N/A |
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+ | 11500 | 0.131 | 4.7811e-05 | 0.803 | N/A | N/A |
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+ | 12000 | 0.137 | 4.7716e-05 | 0.8267 | N/A | N/A |
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+ | 12500 | 0.143 | 4.7621e-05 | 0.8086 | N/A | N/A |
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+ | 13000 | 0.148 | 4.7526e-05 | 0.8017 | N/A | N/A |
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+ | 13500 | 0.154 | 4.7431e-05 | 0.7598 | N/A | N/A |
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+ | 14000 | 0.16 | 4.7336e-05 | 0.8151 | N/A | N/A |
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+ | 14500 | 0.166 | 4.7240e-05 | 0.8126 | N/A | N/A |
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+ | 15000 | 0.171 | 4.7145e-05 | 0.79 | N/A | N/A |
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+ | 15500 | 0.177 | 4.7050e-05 | 0.7979 | N/A | N/A |
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+ | 16000 | 0.183 | 4.6955e-05 | 0.7982 | N/A | N/A |
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+ | 16500 | 0.188 | 4.6860e-05 | 0.7546 | N/A | N/A |
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+ | 17000 | 0.194 | 4.6765e-05 | 0.7905 | N/A | N/A |
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+ | 17500 | 0.2 | 4.6669e-05 | 0.7748 | N/A | N/A |
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+ | 18000 | 0.206 | 4.6574e-05 | 0.8059 | N/A | N/A |
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+ | 18500 | 0.211 | 4.6479e-05 | 0.7644 | N/A | N/A |
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+ | 19000 | 0.217 | 4.6384e-05 | 0.761 | N/A | N/A |
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+ | 19500 | 0.223 | 4.6289e-05 | 0.7862 | N/A | N/A |
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+ | 20000 | 0.228 | 4.6194e-05 | 0.798 | N/A | N/A |
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+ | 20500 | 0.234 | 4.6099e-05 | 0.7795 | N/A | N/A |
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+ | 21000 | 0.24 | 4.6003e-05 | 0.7909 | N/A | N/A |
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+ | 21500 | 0.246 | 4.5908e-05 | 0.7736 | N/A | N/A |
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+ | 22000 | 0.251 | 4.5813e-05 | 0.7658 | N/A | N/A |
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+ | 22500 | 0.257 | 4.5718e-05 | 0.795 | N/A | N/A |
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+ | 23000 | 0.263 | 4.5623e-05 | 0.7768 | N/A | N/A |
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+ | 23500 | 0.268 | 4.5528e-05 | 0.7508 | N/A | N/A |
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+ | 24000 | 0.274 | 4.5432e-05 | 0.7733 | N/A | N/A |
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+ | 24500 | 0.28 | 4.5337e-05 | 0.756 | N/A | N/A |
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+ | 25000 | 0.285 | 4.5242e-05 | 0.7606 | N/A | N/A |
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+ | 25500 | 0.291 | 4.5147e-05 | 0.7789 | N/A | N/A |
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+ | 26000 | 0.297 | 4.5052e-05 | 0.7524 | N/A | N/A |
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+ | 26500 | 0.303 | 4.4957e-05 | 0.7697 | N/A | N/A |
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+ | 27000 | 0.308 | 4.4861e-05 | 0.7455 | N/A | N/A |
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+ | 27500 | 0.314 | 4.4766e-05 | 0.7428 | N/A | N/A |
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+ | 28000 | 0.32 | 4.4671e-05 | 0.7638 | N/A | N/A |
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+ | 28500 | 0.325 | 4.4576e-05 | 0.7694 | N/A | N/A |
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+ | 29000 | 0.331 | 4.4481e-05 | 0.7291 | N/A | N/A |
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+ | 29500 | 0.337 | 4.4386e-05 | 0.7478 | N/A | N/A |
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+ | 30000 | 0.343 | 4.4290e-05 | 0.7272 | N/A | N/A |
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+ | 30500 | 0.348 | 4.4195e-05 | 0.7767 | N/A | N/A |
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+ | 31000 | 0.354 | 4.4100e-05 | 0.7185 | N/A | N/A |
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+ | 31500 | 0.36 | 4.4005e-05 | 0.7378 | N/A | N/A |
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+ | 32000 | 0.365 | 4.3910e-05 | 0.7756 | N/A | N/A |
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+ | 32500 | 0.371 | 4.3815e-05 | 0.7361 | N/A | N/A |
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+ | 33000 | 0.377 | 4.3719e-05 | 0.7526 | N/A | N/A |
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+ | 33500 | 0.383 | 4.3624e-05 | 0.757 | N/A | N/A |
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+ | 34000 | 0.388 | 4.3529e-05 | 0.727 | N/A | N/A |
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+ | 34500 | 0.394 | 4.3434e-05 | 0.737 | N/A | N/A |
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+ | 35000 | 0.4 | 4.3339e-05 | 0.7168 | N/A | N/A |
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+ | 35500 | 0.405 | 4.3244e-05 | 0.7498 | N/A | N/A |
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+ | 36000 | 0.411 | 4.3148e-05 | 0.7494 | N/A | N/A |
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+ | 36500 | 0.417 | 4.3053e-05 | 0.7659 | N/A | N/A |
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+ | 37000 | 0.423 | 4.2958e-05 | 0.7228 | N/A | N/A |
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+ | 37500 | 0.428 | 4.2863e-05 | 0.7327 | N/A | N/A |
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+ | 38000 | 0.434 | 4.2768e-05 | 0.7412 | N/A | N/A |
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+ | 38500 | 0.44 | 4.2673e-05 | 0.7206 | N/A | N/A |
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+ | 39000 | 0.445 | 4.2577e-05 | 0.7424 | N/A | N/A |
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+ | 39500 | 0.451 | 4.2482e-05 | 0.6976 | N/A | N/A |
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+ | 40000 | 0.457 | 4.2387e-05 | 0.7318 | N/A | N/A |
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+ | 40500 | 0.462 | 4.2292e-05 | 0.7323 | N/A | N/A |
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+ | 41000 | 0.468 | 4.2197e-05 | 0.7521 | N/A | N/A |
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+ | 41500 | 0.474 | 4.2102e-05 | 0.7467 | N/A | N/A |
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+ | 42000 | 0.48 | 4.2006e-05 | 0.7085 | N/A | N/A |
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+ | 42500 | 0.485 | 4.1911e-05 | 0.7381 | N/A | N/A |
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+ | 43000 | 0.491 | 4.1816e-05 | 0.7613 | N/A | N/A |
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+ | 43500 | 0.497 | 4.1721e-05 | 0.7585 | N/A | N/A |
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+ | 44000 | 0.502 | 4.1626e-05 | 0.7329 | N/A | N/A |
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+ | 44500 | 0.508 | 4.1531e-05 | 0.7399 | N/A | N/A |
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+ | 45000 | 0.514 | 4.1436e-05 | 0.7304 | N/A | N/A |
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+ | 45500 | 0.52 | 4.1340e-05 | 0.7255 | N/A | N/A |
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+ | 46000 | 0.525 | 4.1245e-05 | 0.7293 | N/A | N/A |
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+ | 46500 | 0.531 | 4.1150e-05 | 0.7136 | N/A | N/A |
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+ | 47000 | 0.537 | 4.1055e-05 | 0.6997 | N/A | N/A |
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+ | 47500 | 0.542 | 4.0960e-05 | 0.7479 | N/A | N/A |
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+ | 48000 | 0.548 | 4.0865e-05 | 0.7297 | N/A | N/A |
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+ | 48500 | 0.554 | 4.0769e-05 | 0.7399 | N/A | N/A |
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+ | 49000 | 0.56 | 4.0674e-05 | 0.7186 | N/A | N/A |
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+ | 49500 | 0.565 | 4.0579e-05 | 0.7562 | N/A | N/A |
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+ | 50000 | 0.571 | 4.0484e-05 | 0.7324 | N/A | N/A |
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+ | 50500 | 0.577 | 4.0389e-05 | 0.7227 | N/A | N/A |
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+ | 51000 | 0.582 | 4.0294e-05 | 0.708 | N/A | N/A |
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+ | 51500 | 0.588 | 4.0198e-05 | 0.7355 | N/A | N/A |
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+ | 52000 | 0.594 | 4.0103e-05 | 0.7153 | N/A | N/A |
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+ | 52500 | 0.6 | 4.0008e-05 | 0.6838 | N/A | N/A |
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+ | 53000 | 0.605 | 3.9913e-05 | 0.7382 | N/A | N/A |
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+ | 53500 | 0.611 | 3.9818e-05 | 0.72 | N/A | N/A |
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+ | 54000 | 0.617 | 3.9723e-05 | 0.744 | N/A | N/A |
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+ | 54500 | 0.622 | 3.9627e-05 | 0.7272 | N/A | N/A |
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+ | 55000 | 0.628 | 3.9532e-05 | 0.731 | N/A | N/A |
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+ | 55500 | 0.634 | 3.9437e-05 | 0.7319 | N/A | N/A |
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+ | 56000 | 0.639 | 3.9342e-05 | 0.6995 | N/A | N/A |
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+ | 56500 | 0.645 | 3.9247e-05 | 0.7203 | N/A | N/A |
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+ | 57000 | 0.651 | 3.9152e-05 | 0.6999 | N/A | N/A |
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+ | 57500 | 0.657 | 3.9056e-05 | 0.7119 | N/A | N/A |
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+ | 58000 | 0.662 | 3.8961e-05 | 0.7101 | N/A | N/A |
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+ | 58500 | 0.668 | 3.8866e-05 | 0.7286 | N/A | N/A |
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+ | 59000 | 0.674 | 3.8771e-05 | 0.7263 | N/A | N/A |
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+ | 59500 | 0.679 | 3.8676e-05 | 0.7177 | N/A | N/A |
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+ | 60000 | 0.685 | 3.8581e-05 | 0.7 | N/A | N/A |
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+ | 60500 | 0.691 | 3.8485e-05 | 0.725 | N/A | N/A |
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+ | 61000 | 0.697 | 3.8390e-05 | 0.7139 | N/A | N/A |
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+ | 61500 | 0.702 | 3.8295e-05 | 0.6962 | N/A | N/A |
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+ | 62000 | 0.708 | 3.8200e-05 | 0.6938 | N/A | N/A |
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+ | 62500 | 0.714 | 3.8105e-05 | 0.7016 | N/A | N/A |
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+ | 63000 | 0.719 | 3.8010e-05 | 0.7139 | N/A | N/A |
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+ | 63500 | 0.725 | 3.7914e-05 | 0.7131 | N/A | N/A |
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+ | 64000 | 0.731 | 3.7819e-05 | 0.7049 | N/A | N/A |
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+ | 64500 | 0.737 | 3.7724e-05 | 0.7383 | N/A | N/A |
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+ | 65000 | 0.742 | 3.7629e-05 | 0.7052 | N/A | N/A |
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+ | 65500 | 0.748 | 3.7534e-05 | 0.7217 | N/A | N/A |
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+ | 66000 | 0.754 | 3.7439e-05 | 0.7184 | N/A | N/A |
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+ | 66500 | 0.759 | 3.7344e-05 | 0.7133 | N/A | N/A |
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+ | 67000 | 0.765 | 3.7248e-05 | 0.6955 | N/A | N/A |
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+ | 67500 | 0.771 | 3.7153e-05 | 0.7008 | N/A | N/A |
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+ | 68000 | 0.777 | 3.7058e-05 | 0.7094 | N/A | N/A |
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+ | 68500 | 0.782 | 3.6963e-05 | 0.7096 | N/A | N/A |
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+ | 69000 | 0.788 | 3.6868e-05 | 0.7208 | N/A | N/A |
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+ | 69500 | 0.794 | 3.6773e-05 | 0.7224 | N/A | N/A |
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+ | 70000 | 0.799 | 3.6677e-05 | 0.6675 | N/A | N/A |
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+ | 70500 | 0.805 | 3.6582e-05 | 0.7072 | N/A | N/A |
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+ | 71000 | 0.811 | 3.6487e-05 | 0.6947 | N/A | N/A |
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+ | 71500 | 0.816 | 3.6392e-05 | 0.6986 | N/A | N/A |
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+ | 72000 | 0.822 | 3.6297e-05 | 0.6974 | N/A | N/A |
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+ | 72500 | 0.828 | 3.6202e-05 | 0.7135 | N/A | N/A |
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+ | 73000 | 0.834 | 3.6106e-05 | 0.6985 | N/A | N/A |
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+ | 73500 | 0.839 | 3.6011e-05 | 0.7115 | N/A | N/A |
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+ | 74000 | 0.845 | 3.5916e-05 | 0.704 | N/A | N/A |
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+ | 74500 | 0.851 | 3.5821e-05 | 0.7202 | N/A | N/A |
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+ | 75000 | 0.856 | 3.5726e-05 | 0.706 | N/A | N/A |
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+ | 75500 | 0.862 | 3.5631e-05 | 0.6748 | N/A | N/A |
300
+ | 76000 | 0.868 | 3.5535e-05 | 0.6864 | N/A | N/A |
301
+ | 76500 | 0.874 | 3.5440e-05 | 0.7139 | N/A | N/A |
302
+ | 77000 | 0.879 | 3.5345e-05 | 0.6659 | N/A | N/A |
303
+ | 77500 | 0.885 | 3.5250e-05 | 0.6743 | N/A | N/A |
304
+ | 78000 | 0.891 | 3.5155e-05 | 0.6896 | N/A | N/A |
305
+ | 78500 | 0.896 | 3.5060e-05 | 0.7106 | N/A | N/A |
306
+ | 79000 | 0.902 | 3.4964e-05 | 0.6904 | N/A | N/A |
307
+ | 79500 | 0.908 | 3.4869e-05 | 0.707 | N/A | N/A |
308
+ | 80000 | 0.914 | 3.4774e-05 | 0.6718 | N/A | N/A |
309
+ | 80500 | 0.919 | 3.4679e-05 | 0.7243 | N/A | N/A |
310
+ | 81000 | 0.925 | 3.4584e-05 | 0.6743 | N/A | N/A |
311
+ | 81500 | 0.931 | 3.4489e-05 | 0.6893 | N/A | N/A |
312
+ | 82000 | 0.936 | 3.4393e-05 | 0.708 | N/A | N/A |
313
+ | 82500 | 0.942 | 3.4298e-05 | 0.6703 | N/A | N/A |
314
+ | 83000 | 0.948 | 3.4203e-05 | 0.6472 | N/A | N/A |
315
+ | 83500 | 0.954 | 3.4108e-05 | 0.6885 | N/A | N/A |
316
+ | 84000 | 0.959 | 3.4013e-05 | 0.6996 | N/A | N/A |
317
+ | 84500 | 0.965 | 3.3918e-05 | 0.731 | N/A | N/A |
318
+ | 85000 | 0.971 | 3.3822e-05 | 0.6775 | N/A | N/A |
319
+ | 85500 | 0.976 | 3.3727e-05 | 0.6504 | N/A | N/A |
320
+ | 86000 | 0.982 | 3.3632e-05 | 0.7029 | N/A | N/A |
321
+ | 86500 | 0.988 | 3.3537e-05 | 0.7008 | N/A | N/A |
322
+ | 87000 | 0.994 | 3.3442e-05 | 0.7123 | N/A | N/A |
323
+ | 87500 | 0.999 | 3.3347e-05 | 0.6963 | N/A | N/A |
324
+ | 87569 | 1.0 | N/A | N/A | 0.658 | 0.7762 |
325
+ | 88000 | 1.005 | 3.3251e-05 | 0.5787 | N/A | N/A |
326
+ | 88500 | 1.011 | 3.3156e-05 | 0.5938 | N/A | N/A |
327
+ | 89000 | 1.016 | 3.3061e-05 | 0.5645 | N/A | N/A |
328
+ | 89500 | 1.022 | 3.2966e-05 | 0.554 | N/A | N/A |
329
+ | 90000 | 1.028 | 3.2871e-05 | 0.6066 | N/A | N/A |
330
+ | 90500 | 1.033 | 3.2776e-05 | 0.5736 | N/A | N/A |
331
+ | 91000 | 1.039 | 3.2681e-05 | 0.6027 | N/A | N/A |
332
+ | 91500 | 1.045 | 3.2585e-05 | 0.582 | N/A | N/A |
333
+ | 92000 | 1.051 | 3.2490e-05 | 0.5683 | N/A | N/A |
334
+ | 92500 | 1.056 | 3.2395e-05 | 0.5863 | N/A | N/A |
335
+ | 93000 | 1.062 | 3.2300e-05 | 0.5708 | N/A | N/A |
336
+ | 93500 | 1.068 | 3.2205e-05 | 0.5839 | N/A | N/A |
337
+ | 94000 | 1.073 | 3.2110e-05 | 0.5776 | N/A | N/A |
338
+ | 94500 | 1.079 | 3.2014e-05 | 0.5881 | N/A | N/A |
339
+ | 95000 | 1.085 | 3.1919e-05 | 0.599 | N/A | N/A |
340
+ | 95500 | 1.091 | 3.1824e-05 | 0.5918 | N/A | N/A |
341
+ | 96000 | 1.096 | 3.1729e-05 | 0.5725 | N/A | N/A |
342
+ | 96500 | 1.102 | 3.1634e-05 | 0.5652 | N/A | N/A |
343
+ | 97000 | 1.108 | 3.1539e-05 | 0.5684 | N/A | N/A |
344
+ | 97500 | 1.113 | 3.1443e-05 | 0.5895 | N/A | N/A |
345
+ | 98000 | 1.119 | 3.1348e-05 | 0.5977 | N/A | N/A |
346
+ | 98500 | 1.125 | 3.1253e-05 | 0.6005 | N/A | N/A |
347
+ | 99000 | 1.131 | 3.1158e-05 | 0.5902 | N/A | N/A |
348
+ | 99500 | 1.136 | 3.1063e-05 | 0.5661 | N/A | N/A |
349
+ | 100000 | 1.142 | 3.0968e-05 | 0.5708 | N/A | N/A |
350
+ | 100500 | 1.148 | 3.0872e-05 | 0.5811 | N/A | N/A |
351
+ | 101000 | 1.153 | 3.0777e-05 | 0.5587 | N/A | N/A |
352
+ | 101500 | 1.159 | 3.0682e-05 | 0.5687 | N/A | N/A |
353
+ | 102000 | 1.165 | 3.0587e-05 | 0.5907 | N/A | N/A |
354
+ | 102500 | 1.171 | 3.0492e-05 | 0.5673 | N/A | N/A |
355
+ | 103000 | 1.176 | 3.0397e-05 | 0.5744 | N/A | N/A |
356
+ | 103500 | 1.182 | 3.0301e-05 | 0.5847 | N/A | N/A |
357
+ | 104000 | 1.188 | 3.0206e-05 | 0.5779 | N/A | N/A |
358
+ | 104500 | 1.193 | 3.0111e-05 | 0.6032 | N/A | N/A |
359
+ | 105000 | 1.199 | 3.0016e-05 | 0.5734 | N/A | N/A |
360
+ | 105500 | 1.205 | 2.9921e-05 | 0.5685 | N/A | N/A |
361
+ | 106000 | 1.21 | 2.9826e-05 | 0.5715 | N/A | N/A |
362
+ | 106500 | 1.216 | 2.9730e-05 | 0.5711 | N/A | N/A |
363
+ | 107000 | 1.222 | 2.9635e-05 | 0.565 | N/A | N/A |
364
+ | 107500 | 1.228 | 2.9540e-05 | 0.5612 | N/A | N/A |
365
+ | 108000 | 1.233 | 2.9445e-05 | 0.5898 | N/A | N/A |
366
+ | 108500 | 1.239 | 2.9350e-05 | 0.5794 | N/A | N/A |
367
+ | 109000 | 1.245 | 2.9255e-05 | 0.5816 | N/A | N/A |
368
+ | 109500 | 1.25 | 2.9159e-05 | 0.5384 | N/A | N/A |
369
+ | 110000 | 1.256 | 2.9064e-05 | 0.5571 | N/A | N/A |
370
+ | 110500 | 1.262 | 2.8969e-05 | 0.5662 | N/A | N/A |
371
+ | 111000 | 1.268 | 2.8874e-05 | 0.563 | N/A | N/A |
372
+ | 111500 | 1.273 | 2.8779e-05 | 0.5801 | N/A | N/A |
373
+ | 112000 | 1.279 | 2.8684e-05 | 0.5839 | N/A | N/A |
374
+ | 112500 | 1.285 | 2.8589e-05 | 0.5562 | N/A | N/A |
375
+ | 113000 | 1.29 | 2.8493e-05 | 0.5897 | N/A | N/A |
376
+ | 113500 | 1.296 | 2.8398e-05 | 0.5899 | N/A | N/A |
377
+ | 114000 | 1.302 | 2.8303e-05 | 0.6035 | N/A | N/A |
378
+ | 114500 | 1.308 | 2.8208e-05 | 0.5509 | N/A | N/A |
379
+ | 115000 | 1.313 | 2.8113e-05 | 0.5773 | N/A | N/A |
380
+ | 115500 | 1.319 | 2.8018e-05 | 0.6098 | N/A | N/A |
381
+ | 116000 | 1.325 | 2.7922e-05 | 0.5773 | N/A | N/A |
382
+ | 116500 | 1.33 | 2.7827e-05 | 0.5803 | N/A | N/A |
383
+ | 117000 | 1.336 | 2.7732e-05 | 0.5811 | N/A | N/A |
384
+ | 117500 | 1.342 | 2.7637e-05 | 0.5614 | N/A | N/A |
385
+ | 118000 | 1.348 | 2.7542e-05 | 0.5871 | N/A | N/A |
386
+ | 118500 | 1.353 | 2.7447e-05 | 0.6058 | N/A | N/A |
387
+ | 119000 | 1.359 | 2.7351e-05 | 0.5617 | N/A | N/A |
388
+ | 119500 | 1.365 | 2.7256e-05 | 0.5849 | N/A | N/A |
389
+ | 120000 | 1.37 | 2.7161e-05 | 0.5624 | N/A | N/A |
390
+ | 120500 | 1.376 | 2.7066e-05 | 0.5967 | N/A | N/A |
391
+ | 121000 | 1.382 | 2.6971e-05 | 0.5629 | N/A | N/A |
392
+ | 121500 | 1.387 | 2.6876e-05 | 0.6049 | N/A | N/A |
393
+ | 122000 | 1.393 | 2.6780e-05 | 0.5736 | N/A | N/A |
394
+ | 122500 | 1.399 | 2.6685e-05 | 0.5622 | N/A | N/A |
395
+ | 123000 | 1.405 | 2.6590e-05 | 0.5822 | N/A | N/A |
396
+ | 123500 | 1.41 | 2.6495e-05 | 0.5735 | N/A | N/A |
397
+ | 124000 | 1.416 | 2.6400e-05 | 0.5866 | N/A | N/A |
398
+ | 124500 | 1.422 | 2.6305e-05 | 0.5726 | N/A | N/A |
399
+ | 125000 | 1.427 | 2.6209e-05 | 0.582 | N/A | N/A |
400
+ | 125500 | 1.433 | 2.6114e-05 | 0.5697 | N/A | N/A |
401
+ | 126000 | 1.439 | 2.6019e-05 | 0.5959 | N/A | N/A |
402
+ | 126500 | 1.445 | 2.5924e-05 | 0.5826 | N/A | N/A |
403
+ | 127000 | 1.45 | 2.5829e-05 | 0.5625 | N/A | N/A |
404
+ | 127500 | 1.456 | 2.5734e-05 | 0.5817 | N/A | N/A |
405
+ | 128000 | 1.462 | 2.5638e-05 | 0.5629 | N/A | N/A |
406
+ | 128500 | 1.467 | 2.5543e-05 | 0.5587 | N/A | N/A |
407
+ | 129000 | 1.473 | 2.5448e-05 | 0.5655 | N/A | N/A |
408
+ | 129500 | 1.479 | 2.5353e-05 | 0.5782 | N/A | N/A |
409
+ | 130000 | 1.485 | 2.5258e-05 | 0.592 | N/A | N/A |
410
+ | 130500 | 1.49 | 2.5163e-05 | 0.569 | N/A | N/A |
411
+ | 131000 | 1.496 | 2.5067e-05 | 0.5683 | N/A | N/A |
412
+ | 131500 | 1.502 | 2.4972e-05 | 0.5642 | N/A | N/A |
413
+ | 132000 | 1.507 | 2.4877e-05 | 0.5586 | N/A | N/A |
414
+ | 132500 | 1.513 | 2.4782e-05 | 0.5695 | N/A | N/A |
415
+ | 133000 | 1.519 | 2.4687e-05 | 0.5629 | N/A | N/A |
416
+ | 133500 | 1.525 | 2.4592e-05 | 0.5864 | N/A | N/A |
417
+ | 134000 | 1.53 | 2.4496e-05 | 0.5835 | N/A | N/A |
418
+ | 134500 | 1.536 | 2.4401e-05 | 0.5684 | N/A | N/A |
419
+ | 135000 | 1.542 | 2.4306e-05 | 0.5829 | N/A | N/A |
420
+ | 135500 | 1.547 | 2.4211e-05 | 0.5544 | N/A | N/A |
421
+ | 136000 | 1.553 | 2.4116e-05 | 0.5934 | N/A | N/A |
422
+ | 136500 | 1.559 | 2.4021e-05 | 0.572 | N/A | N/A |
423
+ | 137000 | 1.564 | 2.3926e-05 | 0.5602 | N/A | N/A |
424
+ | 137500 | 1.57 | 2.3830e-05 | 0.5445 | N/A | N/A |
425
+ | 138000 | 1.576 | 2.3735e-05 | 0.5512 | N/A | N/A |
426
+ | 138500 | 1.582 | 2.3640e-05 | 0.558 | N/A | N/A |
427
+ | 139000 | 1.587 | 2.3545e-05 | 0.5767 | N/A | N/A |
428
+ | 139500 | 1.593 | 2.3450e-05 | 0.5804 | N/A | N/A |
429
+ | 140000 | 1.599 | 2.3355e-05 | 0.5588 | N/A | N/A |
430
+ | 140500 | 1.604 | 2.3259e-05 | 0.5465 | N/A | N/A |
431
+ | 141000 | 1.61 | 2.3164e-05 | 0.5659 | N/A | N/A |
432
+ | 141500 | 1.616 | 2.3069e-05 | 0.572 | N/A | N/A |
433
+ | 142000 | 1.622 | 2.2974e-05 | 0.5773 | N/A | N/A |
434
+ | 142500 | 1.627 | 2.2879e-05 | 0.575 | N/A | N/A |
435
+ | 143000 | 1.633 | 2.2784e-05 | 0.5578 | N/A | N/A |
436
+ | 143500 | 1.639 | 2.2688e-05 | 0.5852 | N/A | N/A |
437
+ | 144000 | 1.644 | 2.2593e-05 | 0.5639 | N/A | N/A |
438
+ | 144500 | 1.65 | 2.2498e-05 | 0.5616 | N/A | N/A |
439
+ | 145000 | 1.656 | 2.2403e-05 | 0.5963 | N/A | N/A |
440
+ | 145500 | 1.662 | 2.2308e-05 | 0.5515 | N/A | N/A |
441
+ | 146000 | 1.667 | 2.2213e-05 | 0.5362 | N/A | N/A |
442
+ | 146500 | 1.673 | 2.2117e-05 | 0.5767 | N/A | N/A |
443
+ | 147000 | 1.679 | 2.2022e-05 | 0.5392 | N/A | N/A |
444
+ | 147500 | 1.684 | 2.1927e-05 | 0.5736 | N/A | N/A |
445
+ | 148000 | 1.69 | 2.1832e-05 | 0.5794 | N/A | N/A |
446
+ | 148500 | 1.696 | 2.1737e-05 | 0.5756 | N/A | N/A |
447
+ | 149000 | 1.702 | 2.1642e-05 | 0.6041 | N/A | N/A |
448
+ | 149500 | 1.707 | 2.1546e-05 | 0.5554 | N/A | N/A |
449
+ | 150000 | 1.713 | 2.1451e-05 | 0.5743 | N/A | N/A |
450
+ | 150500 | 1.719 | 2.1356e-05 | 0.5841 | N/A | N/A |
451
+ | 151000 | 1.724 | 2.1261e-05 | 0.5639 | N/A | N/A |
452
+ | 151500 | 1.73 | 2.1166e-05 | 0.5626 | N/A | N/A |
453
+ | 152000 | 1.736 | 2.1071e-05 | 0.5692 | N/A | N/A |
454
+ | 152500 | 1.741 | 2.0975e-05 | 0.5763 | N/A | N/A |
455
+ | 153000 | 1.747 | 2.0880e-05 | 0.581 | N/A | N/A |
456
+ | 153500 | 1.753 | 2.0785e-05 | 0.5762 | N/A | N/A |
457
+ | 154000 | 1.759 | 2.0690e-05 | 0.5663 | N/A | N/A |
458
+ | 154500 | 1.764 | 2.0595e-05 | 0.5935 | N/A | N/A |
459
+ | 155000 | 1.77 | 2.0500e-05 | 0.553 | N/A | N/A |
460
+ | 155500 | 1.776 | 2.0404e-05 | 0.5615 | N/A | N/A |
461
+ | 156000 | 1.781 | 2.0309e-05 | 0.5705 | N/A | N/A |
462
+ | 156500 | 1.787 | 2.0214e-05 | 0.554 | N/A | N/A |
463
+ | 157000 | 1.793 | 2.0119e-05 | 0.5826 | N/A | N/A |
464
+ | 157500 | 1.799 | 2.0024e-05 | 0.5841 | N/A | N/A |
465
+ | 158000 | 1.804 | 1.9929e-05 | 0.566 | N/A | N/A |
466
+ | 158500 | 1.81 | 1.9834e-05 | 0.5888 | N/A | N/A |
467
+ | 159000 | 1.816 | 1.9738e-05 | 0.5374 | N/A | N/A |
468
+ | 159500 | 1.821 | 1.9643e-05 | 0.5716 | N/A | N/A |
469
+ | 160000 | 1.827 | 1.9548e-05 | 0.5691 | N/A | N/A |
470
+ | 160500 | 1.833 | 1.9453e-05 | 0.5647 | N/A | N/A |
471
+ | 161000 | 1.839 | 1.9358e-05 | 0.5449 | N/A | N/A |
472
+ | 161500 | 1.844 | 1.9263e-05 | 0.5762 | N/A | N/A |
473
+ | 162000 | 1.85 | 1.9167e-05 | 0.5301 | N/A | N/A |
474
+ | 162500 | 1.856 | 1.9072e-05 | 0.5759 | N/A | N/A |
475
+ | 163000 | 1.861 | 1.8977e-05 | 0.5654 | N/A | N/A |
476
+ | 163500 | 1.867 | 1.8882e-05 | 0.5698 | N/A | N/A |
477
+ | 164000 | 1.873 | 1.8787e-05 | 0.5473 | N/A | N/A |
478
+ | 164500 | 1.879 | 1.8692e-05 | 0.5387 | N/A | N/A |
479
+ | 165000 | 1.884 | 1.8596e-05 | 0.5483 | N/A | N/A |
480
+ | 165500 | 1.89 | 1.8501e-05 | 0.5461 | N/A | N/A |
481
+ | 166000 | 1.896 | 1.8406e-05 | 0.5906 | N/A | N/A |
482
+ | 166500 | 1.901 | 1.8311e-05 | 0.5802 | N/A | N/A |
483
+ | 167000 | 1.907 | 1.8216e-05 | 0.5522 | N/A | N/A |
484
+ | 167500 | 1.913 | 1.8121e-05 | 0.5605 | N/A | N/A |
485
+ | 168000 | 1.918 | 1.8025e-05 | 0.5693 | N/A | N/A |
486
+ | 168500 | 1.924 | 1.7930e-05 | 0.57 | N/A | N/A |
487
+ | 169000 | 1.93 | 1.7835e-05 | 0.5473 | N/A | N/A |
488
+ | 169500 | 1.936 | 1.7740e-05 | 0.5657 | N/A | N/A |
489
+ | 170000 | 1.941 | 1.7645e-05 | 0.5733 | N/A | N/A |
490
+ | 170500 | 1.947 | 1.7550e-05 | 0.5817 | N/A | N/A |
491
+ | 171000 | 1.953 | 1.7454e-05 | 0.5575 | N/A | N/A |
492
+ | 171500 | 1.958 | 1.7359e-05 | 0.5819 | N/A | N/A |
493
+ | 172000 | 1.964 | 1.7264e-05 | 0.5663 | N/A | N/A |
494
+ | 172500 | 1.97 | 1.7169e-05 | 0.5294 | N/A | N/A |
495
+ | 173000 | 1.976 | 1.7074e-05 | 0.5552 | N/A | N/A |
496
+ | 173500 | 1.981 | 1.6979e-05 | 0.5467 | N/A | N/A |
497
+ | 174000 | 1.987 | 1.6883e-05 | 0.5476 | N/A | N/A |
498
+ | 174500 | 1.993 | 1.6788e-05 | 0.5315 | N/A | N/A |
499
+ | 175000 | 1.998 | 1.6693e-05 | 0.5378 | N/A | N/A |
500
+ | 175138 | 2.0 | N/A | N/A | 0.6544 | 0.7881 |
501
+ | 175500 | 2.004 | 1.6598e-05 | 0.4238 | N/A | N/A |
502
+ | 176000 | 2.01 | 1.6503e-05 | 0.4607 | N/A | N/A |
503
+ | 176500 | 2.016 | 1.6408e-05 | 0.4443 | N/A | N/A |
504
+ | 177000 | 2.021 | 1.6312e-05 | 0.4287 | N/A | N/A |
505
+ | 177500 | 2.027 | 1.6217e-05 | 0.4477 | N/A | N/A |
506
+ | 178000 | 2.033 | 1.6122e-05 | 0.4308 | N/A | N/A |
507
+ | 178500 | 2.038 | 1.6027e-05 | 0.4438 | N/A | N/A |
508
+ | 179000 | 2.044 | 1.5932e-05 | 0.4576 | N/A | N/A |
509
+ | 179500 | 2.05 | 1.5837e-05 | 0.4561 | N/A | N/A |
510
+ | 180000 | 2.056 | 1.5741e-05 | 0.4469 | N/A | N/A |
511
+ | 180500 | 2.061 | 1.5646e-05 | 0.4444 | N/A | N/A |
512
+ | 181000 | 2.067 | 1.5551e-05 | 0.4427 | N/A | N/A |
513
+ | 181500 | 2.073 | 1.5456e-05 | 0.4312 | N/A | N/A |
514
+ | 182000 | 2.078 | 1.5361e-05 | 0.4677 | N/A | N/A |
515
+ | 182500 | 2.084 | 1.5266e-05 | 0.4768 | N/A | N/A |
516
+ | 183000 | 2.09 | 1.5171e-05 | 0.4672 | N/A | N/A |
517
+ | 183500 | 2.095 | 1.5075e-05 | 0.4518 | N/A | N/A |
518
+ | 184000 | 2.101 | 1.4980e-05 | 0.4251 | N/A | N/A |
519
+ | 184500 | 2.107 | 1.4885e-05 | 0.4393 | N/A | N/A |
520
+ | 185000 | 2.113 | 1.4790e-05 | 0.4283 | N/A | N/A |
521
+ | 185500 | 2.118 | 1.4695e-05 | 0.4364 | N/A | N/A |
522
+ | 186000 | 2.124 | 1.4600e-05 | 0.4601 | N/A | N/A |
523
+ | 186500 | 2.13 | 1.4504e-05 | 0.4343 | N/A | N/A |
524
+ | 187000 | 2.135 | 1.4409e-05 | 0.463 | N/A | N/A |
525
+ | 187500 | 2.141 | 1.4314e-05 | 0.4358 | N/A | N/A |
526
+ | 188000 | 2.147 | 1.4219e-05 | 0.4735 | N/A | N/A |
527
+ | 188500 | 2.153 | 1.4124e-05 | 0.4427 | N/A | N/A |
528
+ | 189000 | 2.158 | 1.4029e-05 | 0.4441 | N/A | N/A |
529
+ | 189500 | 2.164 | 1.3933e-05 | 0.4184 | N/A | N/A |
530
+ | 190000 | 2.17 | 1.3838e-05 | 0.4768 | N/A | N/A |
531
+ | 190500 | 2.175 | 1.3743e-05 | 0.4208 | N/A | N/A |
532
+ | 191000 | 2.181 | 1.3648e-05 | 0.4363 | N/A | N/A |
533
+ | 191500 | 2.187 | 1.3553e-05 | 0.4382 | N/A | N/A |
534
+ | 192000 | 2.193 | 1.3458e-05 | 0.4487 | N/A | N/A |
535
+ | 192500 | 2.198 | 1.3362e-05 | 0.4512 | N/A | N/A |
536
+ | 193000 | 2.204 | 1.3267e-05 | 0.4401 | N/A | N/A |
537
+ | 193500 | 2.21 | 1.3172e-05 | 0.4524 | N/A | N/A |
538
+ | 194000 | 2.215 | 1.3077e-05 | 0.4545 | N/A | N/A |
539
+ | 194500 | 2.221 | 1.2982e-05 | 0.4623 | N/A | N/A |
540
+ | 195000 | 2.227 | 1.2887e-05 | 0.4352 | N/A | N/A |
541
+ | 195500 | 2.233 | 1.2791e-05 | 0.4368 | N/A | N/A |
542
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543
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544
+ | 197000 | 2.25 | 1.2506e-05 | 0.4544 | N/A | N/A |
545
+ | 197500 | 2.255 | 1.2411e-05 | 0.447 | N/A | N/A |
546
+ | 198000 | 2.261 | 1.2316e-05 | 0.4746 | N/A | N/A |
547
+ | 198500 | 2.267 | 1.2220e-05 | 0.4691 | N/A | N/A |
548
+ | 199000 | 2.272 | 1.2125e-05 | 0.471 | N/A | N/A |
549
+ | 199500 | 2.278 | 1.2030e-05 | 0.4489 | N/A | N/A |
550
+ | 200000 | 2.284 | 1.1935e-05 | 0.4414 | N/A | N/A |
551
+ | 200500 | 2.29 | 1.1840e-05 | 0.4566 | N/A | N/A |
552
+ | 201000 | 2.295 | 1.1745e-05 | 0.4331 | N/A | N/A |
553
+ | 201500 | 2.301 | 1.1649e-05 | 0.453 | N/A | N/A |
554
+ | 202000 | 2.307 | 1.1554e-05 | 0.4626 | N/A | N/A |
555
+ | 202500 | 2.312 | 1.1459e-05 | 0.4475 | N/A | N/A |
556
+ | 203000 | 2.318 | 1.1364e-05 | 0.408 | N/A | N/A |
557
+ | 203500 | 2.324 | 1.1269e-05 | 0.4635 | N/A | N/A |
558
+ | 204000 | 2.33 | 1.1174e-05 | 0.4075 | N/A | N/A |
559
+ | 204500 | 2.335 | 1.1079e-05 | 0.4559 | N/A | N/A |
560
+ | 205000 | 2.341 | 1.0983e-05 | 0.4449 | N/A | N/A |
561
+ | 205500 | 2.347 | 1.0888e-05 | 0.4021 | N/A | N/A |
562
+ | 206000 | 2.352 | 1.0793e-05 | 0.4424 | N/A | N/A |
563
+ | 206500 | 2.358 | 1.0698e-05 | 0.4515 | N/A | N/A |
564
+ | 207000 | 2.364 | 1.0603e-05 | 0.4545 | N/A | N/A |
565
+ | 207500 | 2.37 | 1.0508e-05 | 0.4689 | N/A | N/A |
566
+ | 208000 | 2.375 | 1.0412e-05 | 0.4431 | N/A | N/A |
567
+ | 208500 | 2.381 | 1.0317e-05 | 0.4212 | N/A | N/A |
568
+ | 209000 | 2.387 | 1.0222e-05 | 0.4812 | N/A | N/A |
569
+ | 209500 | 2.392 | 1.0127e-05 | 0.4512 | N/A | N/A |
570
+ | 210000 | 2.398 | 1.0032e-05 | 0.4328 | N/A | N/A |
571
+ | 210500 | 2.404 | 9.9365e-06 | 0.4666 | N/A | N/A |
572
+ | 211000 | 2.41 | 9.8414e-06 | 0.4666 | N/A | N/A |
573
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574
+ | 212000 | 2.421 | 9.6511e-06 | 0.4097 | N/A | N/A |
575
+ | 212500 | 2.427 | 9.5559e-06 | 0.4341 | N/A | N/A |
576
+ | 213000 | 2.432 | 9.4607e-06 | 0.4557 | N/A | N/A |
577
+ | 213500 | 2.438 | 9.3656e-06 | 0.428 | N/A | N/A |
578
+ | 214000 | 2.444 | 9.2704e-06 | 0.4288 | N/A | N/A |
579
+ | 214500 | 2.449 | 9.1752e-06 | 0.4736 | N/A | N/A |
580
+ | 215000 | 2.455 | 9.0801e-06 | 0.437 | N/A | N/A |
581
+ | 215500 | 2.461 | 8.9849e-06 | 0.4292 | N/A | N/A |
582
+ | 216000 | 2.467 | 8.8898e-06 | 0.4652 | N/A | N/A |
583
+ | 216500 | 2.472 | 8.7946e-06 | 0.4395 | N/A | N/A |
584
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585
+ | 217500 | 2.484 | 8.6043e-06 | 0.4705 | N/A | N/A |
586
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587
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588
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589
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590
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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608
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610
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611
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612
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614
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630
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632
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641
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650
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658
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668
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669
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670
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671
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672
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673
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674
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675
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676
+ | 262707 | 3.0 | N/A | N/A | 0.7073 | 0.7949 |
677
+
678
+ </details>
679
+
680
+
681
+ ## Framework Versions
682
+
683
+ - **Transformers:** 5.0.0.dev0
684
+ - **PyTorch:** 2.6.0+cu124
all_results.json ADDED
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report.md ADDED
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1
+ # Model Performance Report
2
+
3
+ **Overall Accuracy:** 79.55%
4
+
5
+ Note: Zeros in the following table indicate lack of test samples for those classes.
6
+
7
+ ### Per-Class Performance
8
+ | Class | Precision | Recall (Accuracy) | F1-Score | Support |
9
+ | :--- | :--- | :--- | :--- | :--- |
10
+ | Agriculture | 0.80 | 0.78 | 0.79 | 3147.0 |
11
+ | Civil Rights | 0.72 | 0.73 | 0.72 | 2965.0 |
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+ | Culture | 0.78 | 0.80 | 0.79 | 7190.0 |
13
+ | Defense | 0.74 | 0.71 | 0.72 | 1094.0 |
14
+ | Domestic Commerce | 0.80 | 0.79 | 0.79 | 13205.0 |
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+ | Education | 0.82 | 0.82 | 0.82 | 4569.0 |
16
+ | Energy | 0.77 | 0.79 | 0.78 | 2032.0 |
17
+ | Environment | 0.74 | 0.75 | 0.74 | 2925.0 |
18
+ | Foreign Trade | 0.70 | 0.61 | 0.65 | 311.0 |
19
+ | Government Operations | 0.63 | 0.61 | 0.62 | 899.0 |
20
+ | Health | 0.89 | 0.88 | 0.88 | 7352.0 |
21
+ | Housing | 0.87 | 0.88 | 0.88 | 5419.0 |
22
+ | Immigration | 0.81 | 0.71 | 0.75 | 402.0 |
23
+ | International Affairs | 0.72 | 0.70 | 0.71 | 1731.0 |
24
+ | Labor | 0.74 | 0.74 | 0.74 | 1022.0 |
25
+ | Law and Crime | 0.80 | 0.79 | 0.80 | 1970.0 |
26
+ | Macroeconomics | 0.73 | 0.68 | 0.70 | 847.0 |
27
+ | Other | 0.78 | 0.78 | 0.78 | 14470.0 |
28
+ | Public Lands | 0.69 | 0.70 | 0.69 | 2351.0 |
29
+ | Social Welfare | 0.67 | 0.63 | 0.65 | 903.0 |
30
+ | Technology | 0.84 | 0.85 | 0.84 | 9444.0 |
31
+ | Transportation | 0.80 | 0.82 | 0.81 | 3316.0 |
32
+
33
+ ### Summary Statistics
34
+ - **Macro Avg F1:** 0.76
35
+ - **Weighted Avg F1:** 0.80
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