File size: 2,202 Bytes
112feb6 8b0cd86 112feb6 8b0cd86 112feb6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
language:
- en
license: mit
base_model: distilbert-base-uncased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/distilbert-base-uncased-biored-augmented
results: []
---
<!-- 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. -->
# Dagobert42/distilbert-base-uncased-biored-augmented
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5843
- Accuracy: 0.7932
- Precision: 0.599
- Recall: 0.5407
- F1: 0.5617
- Weighted F1: 0.7888
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.6232 | 0.7797 | 0.5883 | 0.4544 | 0.5018 | 0.7583 |
| No log | 2.0 | 50 | 0.6072 | 0.7871 | 0.575 | 0.4951 | 0.5268 | 0.7726 |
| No log | 3.0 | 75 | 0.6059 | 0.7869 | 0.5665 | 0.5208 | 0.5389 | 0.7788 |
| No log | 4.0 | 100 | 0.6088 | 0.791 | 0.5756 | 0.5006 | 0.5282 | 0.7742 |
| No log | 5.0 | 125 | 0.6066 | 0.7916 | 0.5761 | 0.5067 | 0.5361 | 0.7781 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
|