add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imdb
+metrics:
+- accuracy
+model-index:
+- name: bert-base-uncased-imdb
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: imdb
+      type: imdb
+      args: plain_text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.91264
+---
+
+<!-- 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-imdb
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4942
+- Accuracy: 0.9126
+
+## 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: 1546
+- training_steps: 15468
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.3952        | 0.65  | 2000  | 0.4012          | 0.86     |
+| 0.2954        | 1.29  | 4000  | 0.4535          | 0.892    |
+| 0.2595        | 1.94  | 6000  | 0.4320          | 0.892    |
+| 0.1516        | 2.59  | 8000  | 0.5309          | 0.896    |
+| 0.1167        | 3.23  | 10000 | 0.4070          | 0.928    |
+| 0.0624        | 3.88  | 12000 | 0.5055          | 0.908    |
+| 0.0329        | 4.52  | 14000 | 0.4342          | 0.92     |
+
+
+### 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,
+  "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": "4.10.2",
+  "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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