Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mdosama39/xlm-roberta-base-Stress-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mdosama39/xlm-roberta-base-Stress-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mdosama39/xlm-roberta-base-Stress-identification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mdosama39/xlm-roberta-base-Stress-identification") model = AutoModelForSequenceClassification.from_pretrained("mdosama39/xlm-roberta-base-Stress-identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-Stress-identification
This model is a fine-tuned version of xlm-roberta-base on the None 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
- Downloads last month
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Model tree for mdosama39/xlm-roberta-base-Stress-identification
Base model
FacebookAI/xlm-roberta-base