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---
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
|