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