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Duplicate from wonrax/phobert-base-vietnamese-sentiment
Browse filesCo-authored-by: Ha Huy Long Hai <[email protected]>
- .gitattributes +27 -0
- README.md +47 -0
- added_tokens.json +1 -0
- bpe.codes +0 -0
- config.json +40 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
.gitattributes
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README.md
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---
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language:
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- vi
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tags:
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- sentiment
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- classification
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license: mit
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widget:
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- text: "Không thể nào đẹp hơn"
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- text: "Quá phí tiền, mà không đẹp"
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- text: "Cái này giá ổn không nhỉ?"
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---
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[**GitHub Homepage**](https://github.com/wonrax/phobert-base-vietnamese-sentiment)
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A model fine-tuned for sentiment analysis based on [vinai/phobert-base](https://huggingface.co/vinai/phobert-base).
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Labels:
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- NEG: Negative
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- POS: Positive
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- NEU: Neutral
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Dataset: [30K e-commerce reviews](https://www.kaggle.com/datasets/linhlpv/vietnamese-sentiment-analyst)
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## Usage
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```python
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import torch
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from transformers import RobertaForSequenceClassification, AutoTokenizer
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model = RobertaForSequenceClassification.from_pretrained("wonrax/phobert-base-vietnamese-sentiment")
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tokenizer = AutoTokenizer.from_pretrained("wonrax/phobert-base-vietnamese-sentiment", use_fast=False)
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# Just like PhoBERT: INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
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sentence = 'Đây là mô_hình rất hay , phù_hợp với điều_kiện và như cầu của nhiều người .'
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input_ids = torch.tensor([tokenizer.encode(sentence)])
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with torch.no_grad():
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out = model(input_ids)
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print(out.logits.softmax(dim=-1).tolist())
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# Output:
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# [[0.002, 0.988, 0.01]]
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# ^ ^ ^
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# NEG POS NEU
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```
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added_tokens.json
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{"<mask>": 64000}
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bpe.codes
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config.json
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{
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"_name_or_path": "/content/drive/MyDrive/thesis/models/ecomm-review-phobert-base-5epochs",
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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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": "NEG",
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"1": "POS",
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"2": "NEU"
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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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"NEG": 0,
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"NEU": 2,
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"POS": 1
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 258,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"tokenizer_class": "PhobertTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.18.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 64001
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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:c09addc0848350e599eaba524a84fadd1339d1be8b96e724338c5804f8eb8b40
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size 540069101
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": "<mask>"}
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tokenizer_config.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 256, "special_tokens_map_file": null, "name_or_path": "vinai/phobert-base", "tokenizer_class": "PhobertTokenizer"}
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vocab.txt
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