End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-large-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- rouge
+model-index:
+- name: 9a7204d89a25d13ca2666ebd39ccaa26
+  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. -->
+
+# 9a7204d89a25d13ca2666ebd39ccaa26
+
+This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the nyu-mll/glue [qnli] dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6982
+- Data Size: 0.5
+- Epoch Runtime: 155.2430
+- Accuracy: 0.4943
+- F1 Macro: 0.3308
+- Rouge1: 0.4947
+- Rouge2: 0.0
+- Rougel: 0.4943
+- Rougelsum: 0.4944
+
+## 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     | 0.6991          | 0         | 4.6711        | 0.5011   | 0.3471   | 0.5010 | 0.0    | 0.5011 | 0.5009    |
+| No log        | 1     | 3273  | 0.6225          | 0.0078    | 7.2597        | 0.6678   | 0.6362   | 0.6676 | 0.0    | 0.6675 | 0.6676    |
+| 0.01          | 2     | 6546  | 0.5602          | 0.0156    | 10.7662       | 0.7197   | 0.7008   | 0.7196 | 0.0    | 0.7193 | 0.7191    |
+| 0.5558        | 3     | 9819  | 0.4554          | 0.0312    | 14.6729       | 0.8176   | 0.8175   | 0.8175 | 0.0    | 0.8176 | 0.8176    |
+| 0.7018        | 4     | 13092 | 0.6981          | 0.0625    | 24.2939       | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
+| 0.7081        | 5     | 16365 | 0.6929          | 0.125     | 43.2903       | 0.5057   | 0.3359   | 0.5053 | 0.0    | 0.5057 | 0.5056    |
+| 0.7022        | 6     | 19638 | 0.6912          | 0.25      | 80.3264       | 0.5057   | 0.3359   | 0.5053 | 0.0    | 0.5057 | 0.5056    |
+| 0.7029        | 7     | 22911 | 0.6982          | 0.5       | 155.2430      | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.3.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:243ada8c90277fe4e0eb6dd5254bea969554190d98000937edd5c52302b0e19e
+size 1340622760

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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+size 5969