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bert-base-finetuned-nli

Text Classification Transformers PyTorch
bert generated_from_trainer Inference Endpoints
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  1. .gitignore +1 -0
  2. README.md +75 -0
  3. config.json +36 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +1 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +1 -0
  8. training_args.bin +3 -0
  9. vocab.txt +0 -0
.gitignore ADDED
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+ checkpoint-*/
README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - klue
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+ metrics:
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+ - accuracy
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+ model_index:
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+ - name: bert-base-finetuned-nli
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: klue
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+ type: klue
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+ args: nli
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+ metric:
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+ name: Accuracy
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+ type: accuracy
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+ value: 0.18566666666666667
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-finetuned-nli
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+
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+ This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9683
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+ - Accuracy: 0.1857
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 196 | 0.9683 | 0.1857 |
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+ | No log | 2.0 | 392 | 0.6651 | 0.0997 |
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+ | 0.7562 | 3.0 | 588 | 0.6010 | 0.094 |
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+ | 0.7562 | 4.0 | 784 | 0.6062 | 0.091 |
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+ | 0.7562 | 5.0 | 980 | 0.6124 | 0.0877 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.9.2
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+ - Pytorch 1.9.0+cu102
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+ - Datasets 1.11.0
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+ - Tokenizers 0.10.3
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+ {
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+ "_name_or_path": "klue/bert-base",
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_1": 1,
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+ "LABEL_2": 2
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.9.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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