End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: albert/albert-base-v1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- rouge
+model-index:
+- name: 1a7d904346e7c4adfb1de13434e74811
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# 1a7d904346e7c4adfb1de13434e74811
+
+This model is a fine-tuned version of [albert/albert-base-v1](https://huggingface.co/albert/albert-base-v1) on the google/boolq dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8393
+- Data Size: 1.0
+- Epoch Runtime: 11.5957
+- Accuracy: 0.7365
+- F1 Macro: 0.7092
+- Rouge1: 0.7365
+- Rouge2: 0.0
+- Rougel: 0.7359
+- Rougelsum: 0.7362
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
+| No log        | 0     | 0    | 0.7204          | 0         | 1.6779        | 0.4779   | 0.4482   | 0.4779 | 0.0    | 0.4782 | 0.4773    |
+| No log        | 1     | 294  | 0.7127          | 0.0078    | 3.0497        | 0.4605   | 0.4594   | 0.4602 | 0.0    | 0.4608 | 0.4602    |
+| No log        | 2     | 588  | 0.6631          | 0.0156    | 1.8627        | 0.6213   | 0.3832   | 0.6213 | 0.0    | 0.6207 | 0.6210    |
+| No log        | 3     | 882  | 0.6623          | 0.0312    | 2.0432        | 0.6222   | 0.4283   | 0.6222 | 0.0    | 0.6213 | 0.6225    |
+| 0.0272        | 4     | 1176 | 0.6547          | 0.0625    | 2.3096        | 0.6219   | 0.3850   | 0.6219 | 0.0    | 0.6215 | 0.6216    |
+| 0.0546        | 5     | 1470 | 0.6508          | 0.125     | 2.9116        | 0.6385   | 0.4884   | 0.6382 | 0.0    | 0.6382 | 0.6385    |
+| 0.0898        | 6     | 1764 | 0.6159          | 0.25      | 4.1202        | 0.6615   | 0.5994   | 0.6615 | 0.0    | 0.6612 | 0.6615    |
+| 0.5742        | 7     | 2058 | 0.5887          | 0.5       | 6.5760        | 0.6893   | 0.6685   | 0.6890 | 0.0    | 0.6893 | 0.6893    |
+| 0.5151        | 8.0   | 2352 | 0.5455          | 1.0       | 11.4274       | 0.7230   | 0.6932   | 0.7237 | 0.0    | 0.7233 | 0.7233    |
+| 0.4331        | 9.0   | 2646 | 0.5818          | 1.0       | 11.3375       | 0.7307   | 0.7003   | 0.7310 | 0.0    | 0.7304 | 0.7307    |
+| 0.2983        | 10.0  | 2940 | 0.6985          | 1.0       | 11.3204       | 0.7374   | 0.7160   | 0.7381 | 0.0    | 0.7374 | 0.7374    |
+| 0.2542        | 11.0  | 3234 | 0.7486          | 1.0       | 11.4927       | 0.7405   | 0.7218   | 0.7405 | 0.0    | 0.7405 | 0.7405    |
+| 0.1905        | 12.0  | 3528 | 0.8393          | 1.0       | 11.5957       | 0.7365   | 0.7092   | 0.7365 | 0.0    | 0.7359 | 0.7362    |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "dtype": "float32",
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "[SEP]",
+  "unk_token": "<unk>"
+}

spiece.model

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tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "keep_accents": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "remove_space": true,
+  "sep_token": "[SEP]",
+  "tokenizer_class": "AlbertTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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