metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- jxner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: medicine-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: jxner
type: jxner
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0
- name: Recall
type: recall
value: 0
- name: F1
type: f1
value: 0
- name: Accuracy
type: accuracy
value: 0.859375
medicine-ner
This model is a fine-tuned version of 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