Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tuni/xlm-roberta-large-xnli-finetuned-mnli")
model = AutoModelForSequenceClassification.from_pretrained("tuni/xlm-roberta-large-xnli-finetuned-mnli")Quick Links
xlm-roberta-large-xnli-finetuned-mnli
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 1.2542
- Accuracy: 0.8549
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7468 | 1.0 | 2250 | 0.8551 | 0.8348 |
| 0.567 | 2.0 | 4500 | 0.8935 | 0.8377 |
| 0.318 | 3.0 | 6750 | 0.9892 | 0.8492 |
| 0.1146 | 4.0 | 9000 | 1.2373 | 0.8446 |
| 0.0383 | 5.0 | 11250 | 1.2542 | 0.8549 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
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Dataset used to train tuni/xlm-roberta-large-xnli-finetuned-mnli
Evaluation results
- Accuracy on glueself-reported0.855
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuni/xlm-roberta-large-xnli-finetuned-mnli")