Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
roberta
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("versae/gzipbert_imdb_roberta_lowlr")
model = AutoModelForSequenceClassification.from_pretrained("versae/gzipbert_imdb_roberta_lowlr")Quick Links
gzipbert_imdb_roberta_lowlr
This model is a fine-tuned version of versae/gzip-bert on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5
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 |
|---|---|---|---|---|
| 0.7068 | 1.0 | 1563 | 0.7024 | 0.5 |
| 0.7027 | 2.0 | 3126 | 0.6952 | 0.5 |
| 0.6999 | 3.0 | 4689 | 0.6943 | 0.5 |
| 0.6994 | 4.0 | 6252 | 0.6933 | 0.5 |
| 0.6976 | 5.0 | 7815 | 0.6932 | 0.5 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 4
Model tree for versae/gzipbert_imdb_roberta_lowlr
Base model
versae/gzip-bertDataset used to train versae/gzipbert_imdb_roberta_lowlr
Evaluation results
- Accuracy on imdbtest set self-reported0.500
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="versae/gzipbert_imdb_roberta_lowlr")