---
tags:
- generated_from_trainer
model-index:
- name: tim_expression_identify.2
  results: []
---

## Model description

This model is a fine-tuned version of RoBERTa.

## Intended uses & limitations

For identifying time expressions in text. This model works in a NER-like manner but only focuses on time expressions.


- You may try an example sentence using the hosted inference API on HuggingFace: 

  *In Generation VII, Pokémon Sun and Moon were released worldwide for the 3DS on November 18, 2016 and on November 23, 2016 in Europe.*
  
  The JSON output would be like:
  ```
  [
    {
      "entity_group": "TIME",
      "score": 0.9959897994995117,
      "word": " November 18",
      "start": 79,
      "end": 90
    },
    {
      "entity_group": "TIME",
      "score": 0.996467113494873,
      "word": " 2016",
      "start": 92,
      "end": 96
    },
    {
      "entity_group": "TIME",
      "score": 0.9942433834075928,
      "word": " November 23, 2016",
      "start": 104,
      "end": 121
    }
  ]
  ```

## Training and evaluation data

TimeBank 1.2

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2