blanchefort
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Browse files- README.md +44 -0
- config.json +39 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tf_model.h5 +3 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
README.md
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---
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language:
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- ru
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tags:
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- sentiment
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- text-classification
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datasets:
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- RuReviews
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---
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# RuBERT for Sentiment Analysis of Product Reviews
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This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuReviews](https://github.com/sismetanin/rureviews).
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## Labels
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0: NEUTRAL
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1: POSITIVE
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2: NEGATIVE
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## How to use
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```python
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import torch
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from transformers import AutoModelForSequenceClassification
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from transformers import BertTokenizerFast
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tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-rurewiews')
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model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-rurewiews', return_dict=True)
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@torch.no_grad()
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def predict(text):
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inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
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outputs = model(**inputs)
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predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
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predicted = torch.argmax(predicted, dim=1).numpy()
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return predicted
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```
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## Dataset used for model training
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**[RuReviews](https://github.com/sismetanin/rureviews)**
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> RuReviews: An Automatically Annotated Sentiment Analysis Dataset for Product Reviews in Russian.
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config.json
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{
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"_name_or_path": "blanchefort/rubert-base-cased-sentiment-rurewiews",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "NEUTRAL",
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"1": "POSITIVE",
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"2": "NEGATIVE"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"NEUTRAL": 0,
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"POSITIVE": 1,
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"NEGATIVE": 2
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"return_dict": true,
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"type_vocab_size": 2,
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"vocab_size": 119547
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b9e725c0eb435114f2084c2d4323939c432f355ce314049cf8ccbd8df5995381
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size 711509513
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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]"}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:11ba250a07bf59210d6f7f9512d8607c67da70a8ecf03d0331285dd82ba40a3b
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size 711693676
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tokenizer_config.json
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{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "/home/igor/.cache/torch/transformers/1f428acdde727eed5de979d6856ce350a470be2a64e134a1fdae04af78a27301.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "blanchefort/rubert-base-cased-sentiment", "do_basic_tokenize": true, "never_split": null}
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vocab.txt
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