Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_PRE_BABE_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_PRE_BABE_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_PRE_BABE_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_PRE_BABE_dataset") model = AutoModelForSequenceClassification.from_pretrained("franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_PRE_BABE_dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23c860b4a2bd3327f0fcb98573cf5a449d4cb6c3fb6082489cdfae4775ea079e
- Size of remote file:
- 268 MB
- SHA256:
- e2221364cec4045f67b73d89b1efee0d5b6fe9c35a3b64b9e580a5b8ec13adc5
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