metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
base_model: distilbert-base-cased
model-index:
- name: bert-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- type: precision
value: 0.7311211804904578
name: Precision
- type: recall
value: 0.7298750848074663
name: Recall
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: 1.1658
- Precision: 0.7311
- Recall: 0.7299
- Fscore: 0.7299
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 |
|---|---|---|---|---|---|---|
| 0.8562 | 1.0 | 815 | 0.7859 | 0.7527 | 0.6006 | 0.6173 |
| 0.5352 | 2.0 | 1630 | 0.9248 | 0.7545 | 0.7188 | 0.7293 |
| 0.2543 | 3.0 | 2445 | 1.1658 | 0.7311 | 0.7299 | 0.7299 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2