commit

README.md

@@ -1,3 +1,52 @@
 ---
+language: 
+  - zh
 license: apache-2.0
+
+tags:
+- bert
+- NLU
+- NLI
+
+inference: true
+
+widget:
+- text: "今天心情不好[SEP]今天很开心"
+
 ---
+# Erlangshen-Roberta-110M-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
+We collect 20  paraphrace datasets in the Chinese domain for finetune, with a total of 2773880 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
+
+## Usage
+```python
+from transformers import BertForSequenceClassification
+from transformers import BertTokenizer
+import torch
+
+tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
+model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
+
+texta='今天的饭不好吃'
+textb='今天心情不好'
+
+output=model(torch.tensor([tokenizer.encode(texta,textb)]))
+print(torch.nn.functional.softmax(output.logits,dim=-1))
+
+```
+## Scores on downstream chinese tasks（The dev datasets of BUSTM and AFQMC may exist in the train set） 
+|    Model   | BQ    |  BUSTM  | AFQMC    |
+| :--------:    | :-----:  | :----:  | :-----:   | 
+| Erlangshen-Roberta-110M-Similarity | 85.41     |   95.18    | 81.72     |
+| Erlangshen-Roberta-330M-Similarity | 86.21      |   99.29    | 93.89      |  
+| Erlangshen-MegatronBert-1.3B-Similarity | 86.31      |   -    | -      |   
+
+## Citation
+If you find the resource is useful, please cite the following website in your paper.
+```
+@misc{Fengshenbang-LM,
+  title={Fengshenbang-LM},
+  author={IDEA-CCNL},
+  year={2021},
+  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label":{"1":"similar","0":"not similar"},
+  "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,
+  "output_past": true,
+  "pad_token_id": 1,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

pytorch_model.bin

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