metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: base-vanilla-target-tweet
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.7780748663101604
- name: F1
type: f1
value: 0.7772664883136655
base-vanilla-target-tweet
This model is a fine-tuned version of google/bert_uncased_L-12_H-768_A-12 on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 1.8380
- Accuracy: 0.7781
- F1: 0.7773
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.3831 | 4.9 | 500 | 0.9800 | 0.7807 | 0.7785 |
0.0414 | 9.8 | 1000 | 1.4175 | 0.7754 | 0.7765 |
0.015 | 14.71 | 1500 | 1.6411 | 0.7754 | 0.7708 |
0.0166 | 19.61 | 2000 | 1.5930 | 0.7941 | 0.7938 |
0.0175 | 24.51 | 2500 | 1.3934 | 0.7888 | 0.7852 |
0.0191 | 29.41 | 3000 | 1.9407 | 0.7647 | 0.7658 |
0.0137 | 34.31 | 3500 | 1.8380 | 0.7781 | 0.7773 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2