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:
- 60df44413550860cdc4b579bad84682dcf3bbffd36af348398e155e6290a3ea1
- Size of remote file:
- 5.05 kB
- SHA256:
- 6b0cf35159a87a40f7d00aac1283cee32991a88b237343acab3942b89e7e8ba8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.