metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- precision
- recall
model-index:
- name: bert-1-epoch-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: sentiment
split: test
args: sentiment
metrics:
- name: Accuracy
type: accuracy
value: 0.6895962227287529
- name: Precision
type: precision
value: 0.6932981822495374
- name: Recall
type: recall
value: 0.6895962227287529
bert-1-epoch-sentiment
This model is a fine-tuned version of bert-base-uncased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.6998
- Accuracy: 0.6896
- Precision: 0.6933
- Recall: 0.6896
- Micro-avg-recall: 0.6896
- Micro-avg-precision: 0.6896
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|---|---|---|---|---|---|---|---|---|
| 0.5756 | 1.0 | 2851 | 0.6998 | 0.6896 | 0.6933 | 0.6896 | 0.6896 | 0.6896 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3