metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5227272727272727
- name: Recall
type: recall
value: 0.29842446709916587
- name: F1
type: f1
value: 0.3799410029498525
- name: Accuracy
type: accuracy
value: 0.9395493993416271
my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2758
- Precision: 0.5227
- Recall: 0.2984
- F1: 0.3799
- Accuracy: 0.9395
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 | 213 | 0.2952 | 0.5110 | 0.2159 | 0.3036 | 0.9366 |
| No log | 2.0 | 426 | 0.2758 | 0.5227 | 0.2984 | 0.3799 | 0.9395 |
Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3