Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use guess-winnow/bhasa-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guess-winnow/bhasa-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="guess-winnow/bhasa-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("guess-winnow/bhasa-sentiment") model = AutoModelForSequenceClassification.from_pretrained("guess-winnow/bhasa-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.5745776295661926
f1_macro: 0.7511620447376335
f1_micro: 0.8227914270778881
f1_weighted: 0.8191891943452733
precision_macro: 0.7875039783226351
precision_micro: 0.8227914270778881
precision_weighted: 0.8196484410562087
recall_macro: 0.7292605762364058
recall_micro: 0.8227914270778881
recall_weighted: 0.8227914270778881
accuracy: 0.8227914270778881
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Model tree for guess-winnow/bhasa-sentiment
Base model
FacebookAI/xlm-roberta-large