Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("audreyvasconcelos/iag-class")
model = AutoModelForSequenceClassification.from_pretrained("audreyvasconcelos/iag-class")Quick Links
iag-class
This model is a fine-tuned version of roberta-base on the ag_news dataset. It achieves the following results on the evaluation set:
- Loss: 0.2206
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: 1e-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
- lr_scheduler_warmup_steps: 600
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0053 | 1.0 | 15000 | 0.2253 |
| 0.2434 | 2.0 | 30000 | 0.2206 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for audreyvasconcelos/iag-class
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="audreyvasconcelos/iag-class")