Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pachequinho/sentiment_bert_imdb_10")
model = AutoModelForSequenceClassification.from_pretrained("pachequinho/sentiment_bert_imdb_10")Quick Links
sentiment_bert_imdb_10
This model is a fine-tuned version of bert-base-cased on the imdb dataset with only 10% of the training data. It achieves the following results on the evaluation set:
- Loss: 0.2735
- Accuracy: 0.8984
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 157 | 0.2957 | 0.8820 |
| No log | 2.0 | 314 | 0.2735 | 0.8984 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for pachequinho/sentiment_bert_imdb_10
Base model
google-bert/bert-base-casedDataset used to train pachequinho/sentiment_bert_imdb_10
Evaluation results
- Accuracy on imdbtest set self-reported0.898
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pachequinho/sentiment_bert_imdb_10")