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Monti06
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ModeloNLPdemo

Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Metrics Community
1
How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="Monti06/ModeloNLPdemo")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Monti06/ModeloNLPdemo")
model = AutoModelForSequenceClassification.from_pretrained("Monti06/ModeloNLPdemo")
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ModeloNLPdemo

This model is a fine-tuned version of distilroberta-base on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6821
  • Accuracy: 0.8235
  • F1: 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

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5112 1.09 500 0.5707 0.8186 0.8693
0.3421 2.18 1000 0.6821 0.8235 0.8621

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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Dataset used to train Monti06/ModeloNLPdemo

Evaluation results