Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wbigger/sentiment-analysis-test-rob")
model = AutoModelForSequenceClassification.from_pretrained("wbigger/sentiment-analysis-test-rob")Quick Links
sentiment-analysis-test-rob
This model is a fine-tuned version of cardiffnlp/xlm-roberta-base-tweet-sentiment-it on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9764
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wbigger/sentiment-analysis-test-rob")