metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- maccrobat_biomedical_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: maccrobat_biomedical_ner
type: maccrobat_biomedical_ner
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0
- name: Recall
type: recall
value: 0
- name: F1
type: f1
value: 0
- name: Accuracy
type: accuracy
value: 0.41848218895198763
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the maccrobat_biomedical_ner dataset. It achieves the following results on the evaluation set:
- Loss: 2.4960
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.4185
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 10 | 2.9692 | 0.0 | 0.0 | 0.0 | 0.4185 |
| No log | 2.0 | 20 | 2.5900 | 0.0 | 0.0 | 0.0 | 0.4185 |
| No log | 3.0 | 30 | 2.4960 | 0.0 | 0.0 | 0.0 | 0.4185 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2