Upload latest checkpoint with model card

README.md

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+
+# Model Card for BERT-base Sentiment Analysis Model
+
+## Model Details
+This model is a fine-tuned version of BERT-base for sentiment analysis tasks.
+
+## Training Data
+The model was trained on the Rotten Tomatoes dataset.
+
+## Training Procedure
+- **Learning Rate**: 2e-5
+- **Epochs**: 3
+- **Batch Size**: 16
+가능하면 다른 사람들도 똑같은 버트모델, 로튼 토메토를 이용했을 때 재현가능하도록 하는 모든 하이퍼 파라미터들을 다 적어라
+
+## How to Use 허깅 페이스 쓸 때 어떤 것을 쓰면 된다는 걸 알려주는 것
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+
+input_text = "The movie was fantastic with a gripping storyline!"
+inputs = tokenizer.encode(input_text, return_tensors="pt")
+outputs = model(inputs)
+print(outputs.logits)
+```
+
+## Evaluation 평가 결과
+- **Accuracy**: 81.97%
+
+## Limitations 약점은 뭐가 있다는 것
+The model may generate biased or inappropriate content
+due to the nature of the training data.
+It is recommended to use the model with caution and apply necessary filters.
+
+## Ethical Considerations
+- **Bias**: The model may inherit biases present in the training data.
+- **Misuse**: The model can be misused to generate misleading or harmful content.
+
+## Copyright and License
+This model is licensed under the MIT License.

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "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]"
+}