add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- ag_news
+metrics:
+- accuracy
+model-index:
+- name: bert-base-uncased-ag_news
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: ag_news
+      type: ag_news
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9375
+---
+
+<!-- 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. -->
+
+# bert-base-uncased-ag_news
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3284
+- Accuracy: 0.9375
+
+## 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: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 7425
+- training_steps: 74250
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.5773        | 0.13  | 2000  | 0.3627          | 0.8875   |
+| 0.3101        | 0.27  | 4000  | 0.2938          | 0.9208   |
+| 0.3076        | 0.4   | 6000  | 0.3114          | 0.9092   |
+| 0.3114        | 0.54  | 8000  | 0.4545          | 0.9008   |
+| 0.3154        | 0.67  | 10000 | 0.3875          | 0.9083   |
+| 0.3095        | 0.81  | 12000 | 0.3390          | 0.9142   |
+| 0.2948        | 0.94  | 14000 | 0.3341          | 0.9133   |
+| 0.2557        | 1.08  | 16000 | 0.4573          | 0.9092   |
+| 0.258         | 1.21  | 18000 | 0.3356          | 0.9217   |
+| 0.2455        | 1.35  | 20000 | 0.3348          | 0.9283   |
+| 0.2361        | 1.48  | 22000 | 0.3218          | 0.93     |
+| 0.254         | 1.62  | 24000 | 0.3814          | 0.9033   |
+| 0.2528        | 1.75  | 26000 | 0.3628          | 0.9158   |
+| 0.2282        | 1.89  | 28000 | 0.3302          | 0.9308   |
+| 0.224         | 2.02  | 30000 | 0.3967          | 0.9225   |
+| 0.174         | 2.15  | 32000 | 0.3669          | 0.9333   |
+| 0.1848        | 2.29  | 34000 | 0.3435          | 0.9283   |
+| 0.19          | 2.42  | 36000 | 0.3552          | 0.93     |
+| 0.1865        | 2.56  | 38000 | 0.3996          | 0.9258   |
+| 0.1877        | 2.69  | 40000 | 0.3749          | 0.9258   |
+| 0.1951        | 2.83  | 42000 | 0.3963          | 0.9258   |
+| 0.1702        | 2.96  | 44000 | 0.3655          | 0.9317   |
+| 0.1488        | 3.1   | 46000 | 0.3942          | 0.9292   |
+| 0.1231        | 3.23  | 48000 | 0.3998          | 0.9267   |
+| 0.1319        | 3.37  | 50000 | 0.4292          | 0.9242   |
+| 0.1334        | 3.5   | 52000 | 0.4904          | 0.9192   |
+
+
+### Framework versions
+
+- Transformers 4.10.2
+- Pytorch 1.7.1
+- Datasets 1.6.1
+- Tokenizers 0.10.3

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,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3
+  },
+  "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": "4.10.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}

training_args.bin

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