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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Multi-ling-BERT |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Multi-ling-BERT |
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. |
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## Usage |
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### In Transformers |
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```python |
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from transformers import pipeline,AutoTokenizer |
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model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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text = "I feel happy today!" |
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inputs = tokenizer(text,return_tensors="pt",padding=True, truncation=True) |
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{ |
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'input_ids': tensor([[ 101, 1045, 2514, 3407, 2651, 999, 102]]), |
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'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]]) |
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} |
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tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) |
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['[CLS]', 'i', 'feel', 'happy', 'today', '!', '[SEP]'] |
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tokenizer.decode(inputs["input_ids"][0]) |
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[CLS] i feel happy today! [SEP] |
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text = "This is the question" |
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query = "This is the context with lots of information. Some useless. The answer is here some more words." |
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inputs = tokenizer(text,query,return_tensors="pt",padding=True, truncation=True) |
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{ |
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'input_ids': tensor([ 101, 2023, 2003, 1996, 3160, 102, 2023, 2003, 1996, 6123, |
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2007, 7167, 1997, 2592, 1012, 2070, 11809, 1012, 1996, 3437, |
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2003, 2182, 2070, 2062, 2616, 1012, 102]) |
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} |
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tokenizer.decode(inputs ["input_ids"][0]) |
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text = "I feel happy today!" |
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# BertTokenizerFast |
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tokenizer = BertTokenizerFast.from_pretrained(model_name) |
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inputs_for_BertTokenizer = tokenizer(text, return_tensors="pt",padding=False, truncation=True, max_length=512, stride=256) |
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{ |
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'input_ids': tensor([[ 101, 100, 11297, 9200, 11262, 106, 102]]), |
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'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0]]), |
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'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]]) |
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} |
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# BartTokenizerFast |
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tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base") |
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inputs_for_BartTokenizerFast= tokenizer(text, return_tensors="pt",padding=False, truncation=True, max_length=512, stride=256) |
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{ |
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'input_ids': tensor([[ 0, 100, 619, 1372, 452, 328, 2]]), |
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'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]]) |
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} |
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# Model |
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from transformers import AutoModel |
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model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
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model = AutoModel.from_pretrained(model_name) |
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outputs = model(**inputs) |
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print(outputs.last_hidden_state.shape) |
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{ |
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torch.Size([1, 7, 768]) |
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} |
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from transformers import AutoModelForSequenceClassification |
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model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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outputs = model(**inputs) |
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print(outputs.logits) |
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{ |
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tensor([[-4.3450, 4.6878]], grad_fn=<AddmmBackward0>) |
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} |
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import torch |
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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print(predictions) |
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{ |
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tensor([[1.1942e-04, 9.9988e-01]], grad_fn=<SoftmaxBackward0>) |
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} |
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``` |
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