metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- trl
- sft
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-wnut_17-full
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: 0.6038781163434903
- name: Recall
type: recall
value: 0.4040778498609824
- name: F1
type: f1
value: 0.484175458078845
- name: Accuracy
type: accuracy
value: 0.9478859390363815
distilbert-base-uncased-wnut_17-full
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3599
- Precision: 0.6039
- Recall: 0.4041
- F1: 0.4842
- Accuracy: 0.9479
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: 5e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2609 | 0.5911 | 0.3309 | 0.4242 | 0.9420 |
| No log | 2.0 | 426 | 0.2808 | 0.5679 | 0.3373 | 0.4233 | 0.9447 |
| 0.133 | 3.0 | 639 | 0.3328 | 0.6591 | 0.3244 | 0.4348 | 0.9461 |
| 0.133 | 4.0 | 852 | 0.3302 | 0.5976 | 0.3689 | 0.4562 | 0.9465 |
| 0.0224 | 5.0 | 1065 | 0.3142 | 0.4955 | 0.4041 | 0.4451 | 0.9445 |
| 0.0224 | 6.0 | 1278 | 0.3599 | 0.6039 | 0.4041 | 0.4842 | 0.9479 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1