metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- precision
- recall
base_model: google/electra-small-discriminator
model-index:
- name: electra-5-epoch-sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: sentiment
split: test
args: sentiment
metrics:
- type: accuracy
value: 0.6893520026050146
name: Accuracy
- type: precision
value: 0.6913776305729754
name: Precision
- type: recall
value: 0.6893520026050146
name: Recall
TOKENIZER & TRAINER CORRUPTED
electra-5-epoch-sentiment
This model is a fine-tuned version of google/electra-small-discriminator on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.7949
- Accuracy: 0.6894
- Precision: 0.6914
- Recall: 0.6894
- Micro-avg-recall: 0.6894
- Micro-avg-precision: 0.6894
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|---|---|---|---|---|---|---|---|---|
| 0.5949 | 1.0 | 2851 | 0.6963 | 0.6926 | 0.6943 | 0.6926 | 0.6926 | 0.6926 |
| 0.6502 | 2.0 | 5702 | 0.7348 | 0.6911 | 0.6929 | 0.6911 | 0.6911 | 0.6911 |
| 0.556 | 3.0 | 8553 | 0.7322 | 0.6943 | 0.6952 | 0.6943 | 0.6943 | 0.6943 |
| 0.4561 | 4.0 | 11404 | 0.7601 | 0.6895 | 0.6916 | 0.6895 | 0.6895 | 0.6895 |
| 0.471 | 5.0 | 14255 | 0.7949 | 0.6894 | 0.6914 | 0.6894 | 0.6894 | 0.6894 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3