Text Classification
Transformers
Safetensors
bert
classification
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jonruida/clasificador-rotten-tomatoes")
model = AutoModelForSequenceClassification.from_pretrained("jonruida/clasificador-rotten-tomatoes", device_map="auto")Quick Links
clasificador-rotten-tomatoes
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8027
- Accuracy: 0.8621
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4029 | 1.0 | 1067 | 0.3717 | 0.8565 |
| 0.2381 | 2.0 | 2134 | 0.6918 | 0.8480 |
| 0.0812 | 3.0 | 3201 | 0.8027 | 0.8621 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for jonruida/clasificador-rotten-tomatoes
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonruida/clasificador-rotten-tomatoes")