readme: # BERT based temporal tagged 
  
  Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
  
  # Model description
  BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. We use BERT for token classification to tag the tokens in text with classes: 
  ```
  O -- outside of a tag 
  I-TIME -- inside tag of time 
  B-TIME -- beginning tag of time
  I-DATE -- inside tag of date 
  B-DATE -- beginning tag of date
  I-DURATION -- inside tag of duration 
  B-DURATION -- beginning tag of duration
  I-SET -- inside tag of the set 
  B-SET -- beginning tag of the set
  ```
  
  
  # Intended uses & limitations
  This model is best used accompanied with code from the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). Especially for inference, the direct output might be noisy and hard to decipher, in the repository we provide alignment functions and voting strategies for the final output.
  
  # How to use
  you can load the model as follows:
  ```
      tokenizer = AutoTokenizer.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier", use_fast=False)
      model = BertForTokenClassification.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier")
  
  ```
  for inference use: 
  ```
  processed_text = tokenizer(input_text, return_tensors="pt")
  result = model(**processed_text)
  classification= result[0]
  
  ```
  for an example with post-processing, refer to the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). 
  We provide a function `merge_tokens` to decipher the output. 
  to further fine-tune, use the `Trainer` from hugginface. An example of a similar fine-tuning can be found [here](https://github.com/satya77/Transformer_Temporal_Tagger/blob/master/run_token_classifier.py).
  
  #Training data
  We use 3 data sources: 
  [Tempeval-3](https://www.cs.york.ac.uk/semeval-2013/task1/index.php%3Fid=data.html), Wikiwars, Tweets datasets. For the correct data versions please refer to our [repository](https://github.com/satya77/Transformer_Temporal_Tagger). 
  
  #Training procedure
  The model is trained from publicly available checkpoints on huggingface (`bert-base-uncased`), with a batch size of 34. We use a learning rate of 5e-05 with an Adam optimizer and linear weight decay.
  We fine-tune with 5 different random seeds, this version of the model is the only seed=4.
  For training, we use 2 NVIDIA A100 GPUs with 40GB of memory. 
  
   
  
  
  