metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7412691902027423
- name: Recall
type: recall
value: 0.7200253439873575
bert-emotion
This model is a fine-tuned version of distilbert-base-cased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 1.2007
- Precision: 0.7413
- Recall: 0.7200
- Fscore: 0.7268
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: 4
- eval_batch_size: 4
- 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 | Fscore |
|---|---|---|---|---|---|---|
| 0.8416 | 1.0 | 815 | 0.7683 | 0.7000 | 0.7141 | 0.7062 |
| 0.5465 | 2.0 | 1630 | 0.8561 | 0.7640 | 0.6735 | 0.6979 |
| 0.2747 | 3.0 | 2445 | 1.2007 | 0.7413 | 0.7200 | 0.7268 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0