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:
- 43f85530f838fc564f2ba5a518d01b5680d373fac407b0e683702f4cc6fc6b9d
- Size of remote file:
- 1.11 GB
- SHA256:
- af81fb952da006fafdad17d30c0b3c2c019f6ad3d4912aa93f8ceb52a87c2ecb
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