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
+model-index:
+- name: a58af9b811decb15de671183c4b54b07
+  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. -->
+
+# a58af9b811decb15de671183c4b54b07
+
+This model is a fine-tuned version of [albert/albert-base-v1](https://huggingface.co/albert/albert-base-v1) on the contemmcm/amazon_reviews_2013 [cell-phone] dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8621
+- Data Size: 1.0
+- Epoch Runtime: 72.8094
+- Accuracy: 0.6829
+- F1 Macro: 0.6121
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|
+| No log        | 0     | 0     | 1.7944          | 0         | 5.8050        | 0.1102   | 0.0716   |
+| No log        | 1     | 1973  | 1.4652          | 0.0078    | 6.7679        | 0.3875   | 0.1904   |
+| 0.0319        | 2     | 3946  | 1.3011          | 0.0156    | 7.0154        | 0.4623   | 0.2680   |
+| 1.1769        | 3     | 5919  | 1.1409          | 0.0312    | 7.9874        | 0.5376   | 0.3510   |
+| 0.9719        | 4     | 7892  | 0.9420          | 0.0625    | 9.9755        | 0.6041   | 0.4954   |
+| 0.8939        | 5     | 9865  | 0.8626          | 0.125     | 14.2018       | 0.6356   | 0.5305   |
+| 0.8639        | 6     | 11838 | 0.8189          | 0.25      | 22.6114       | 0.6580   | 0.5508   |
+| 0.8509        | 7     | 13811 | 0.7834          | 0.5       | 39.4226       | 0.6698   | 0.6019   |
+| 0.7676        | 8.0   | 15784 | 0.7777          | 1.0       | 73.1966       | 0.6729   | 0.6116   |
+| 0.6876        | 9.0   | 17757 | 0.7524          | 1.0       | 74.0192       | 0.6952   | 0.6125   |
+| 0.6258        | 10.0  | 19730 | 0.7636          | 1.0       | 75.1704       | 0.6802   | 0.6244   |
+| 0.6055        | 11.0  | 21703 | 0.7913          | 1.0       | 73.2826       | 0.6909   | 0.6220   |
+| 0.5316        | 12.0  | 23676 | 0.7981          | 1.0       | 73.7851       | 0.6908   | 0.6192   |
+| 0.4656        | 13.0  | 25649 | 0.8621          | 1.0       | 72.8094       | 0.6829   | 0.6121   |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.2.0
+- Tokenizers 0.22.1

config.json

@@ -0,0 +1,47 @@
+{
+  "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,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "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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+{
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+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": {
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+    "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": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
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+  },
+  "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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