Text Classification
Transformers
TensorBoard
Safetensors
Polish
xlm-roberta
emotion-classification
roberta
fine-tuned
polish
Eval Results (legacy)
text-embeddings-inference
Instructions to use visegradmedia-emotion/Emotion_RoBERTa_polish6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use visegradmedia-emotion/Emotion_RoBERTa_polish6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="visegradmedia-emotion/Emotion_RoBERTa_polish6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("visegradmedia-emotion/Emotion_RoBERTa_polish6") model = AutoModelForSequenceClassification.from_pretrained("visegradmedia-emotion/Emotion_RoBERTa_polish6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de4c2c54f0d34112a0fe52a3e6d3b0375b2c6416fbe76039ec67e7818adfb481
- Size of remote file:
- 14.2 kB
- SHA256:
- 5753c9c57b5ba512a320323bbacddbe4706b9614c657a6cae85eb014b47f8423
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.