license: apache-2.0 | |
tags: | |
- generated_from_trainer | |
datasets: | |
- jxner | |
metrics: | |
- precision | |
- recall | |
- f1 | |
- accuracy | |
base_model: distilbert-base-uncased | |
model-index: | |
- name: medicine-ner | |
results: | |
- task: | |
type: token-classification | |
name: Token Classification | |
dataset: | |
name: jxner | |
type: jxner | |
config: wnut_17 | |
split: test | |
args: wnut_17 | |
metrics: | |
- type: precision | |
value: 0.0 | |
name: Precision | |
- type: recall | |
value: 0.0 | |
name: Recall | |
- type: f1 | |
value: 0.0 | |
name: F1 | |
- type: accuracy | |
value: 0.859375 | |
name: Accuracy | |
<!-- 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. --> | |
# medicine-ner | |
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jxner dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: 0.7996 | |
- Precision: 0.0 | |
- Recall: 0.0 | |
- F1: 0.0 | |
- Accuracy: 0.8594 | |
## Model description | |
More information needed | |
## Intended uses & limitations | |
More information needed | |
## Training and evaluation data | |
More information needed | |
## Training procedure | |
### Training hyperparameters | |
The following hyperparameters were used during training: | |
- learning_rate: 2e-05 | |
- train_batch_size: 16 | |
- eval_batch_size: 16 | |
- seed: 42 | |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
- lr_scheduler_type: linear | |
- num_epochs: 2 | |
### Training results | |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | |
| No log | 1.0 | 1 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.8594 | | |
| No log | 2.0 | 2 | 0.7996 | 0.0 | 0.0 | 0.0 | 0.8594 | | |
### Framework versions | |
- Transformers 4.27.3 | |
- Pytorch 1.13.1+cu116 | |
- Datasets 2.10.1 | |
- Tokenizers 0.13.2 | |