Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("danielribeiro/google-play-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("danielribeiro/google-play-sentiment-analysis")Quick Links
google-play-sentiment-analysis
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7830
- Accuracy: 0.6571
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8207 | 1.0 | 1200 | 0.7830 | 0.6571 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Model tree for danielribeiro/google-play-sentiment-analysis
Base model
neuralmind/bert-large-portuguese-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danielribeiro/google-play-sentiment-analysis")