Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
Instructions to use SiMajid/xlm-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiMajid/xlm-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SiMajid/xlm-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SiMajid/xlm-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("SiMajid/xlm-roberta-base") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
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: 1.41e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25.0
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Downloads last month
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Model tree for SiMajid/xlm-roberta-base
Base model
FacebookAI/xlm-roberta-base