|
|
--- |
|
|
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 |
|
|
|
