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---
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
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