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README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: HsscBERT_abs_and_full
+  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. -->
+
+# HsscBERT_abs_and_full
+
+This model is a fine-tuned version of [/home/hscrc/pretrained_models/bert-base-chinese](https://huggingface.co//home/hscrc/pretrained_models/bert-base-chinese) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6037
+- Accuracy: 0.8504
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 9
+- total_train_batch_size: 288
+- total_eval_batch_size: 144
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
+|:-------------:|:-----:|:-----:|:--------:|:---------------:|
+| 0.8163        | 0.19  | 5000  | 0.8326   | 0.6971          |
+| 0.7942        | 0.38  | 10000 | 0.8364   | 0.6761          |
+| 0.7817        | 0.57  | 15000 | 0.8384   | 0.6651          |
+| 0.7751        | 0.75  | 20000 | 0.8402   | 0.6563          |
+| 0.7654        | 0.94  | 25000 | 0.8415   | 0.6490          |
+| 0.7546        | 1.13  | 30000 | 0.8427   | 0.6441          |
+| 0.7527        | 1.32  | 35000 | 0.8434   | 0.6398          |
+| 0.7484        | 1.51  | 40000 | 0.8444   | 0.6345          |
+| 0.7443        | 1.7   | 45000 | 0.8450   | 0.6318          |
+| 0.74          | 1.88  | 50000 | 0.8456   | 0.6292          |
+| 0.738         | 2.07  | 55000 | 0.8460   | 0.6268          |
+| 0.734         | 2.26  | 60000 | 0.8464   | 0.6246          |
+| 0.7335        | 2.45  | 65000 | 0.8467   | 0.6229          |
+| 0.7299        | 2.64  | 70000 | 0.8470   | 0.6212          |
+| 0.7291        | 2.83  | 75000 | 0.8473   | 0.6201          |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.10.0+cu113
+- Datasets 2.9.0
+- Tokenizers 0.13.2

config.json

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+{
+  "_name_or_path": "/home/hscrc/pretrained_models/bert-base-chinese",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 27430
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7f60e3dce0cf05e8c665006355b95ec3387ca0a3c1ecae1420d514afa798ac2
+size 428617835

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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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "data/hssc_abs_and_full/tokenizer",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "data/hssc_abs_and_full/tokenizer/special_tokens_map.json",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}