Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling") model = AutoModelForSequenceClassification.from_pretrained("mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1bb3d64514774ae2ac9d728960db2a982fd584fd8e820967fa35011fba230a7
- Size of remote file:
- 117 MB
- SHA256:
- 50e6e2c457eb54c2f2754f80ea1f5f53e5ad673b09e678fa59f86f85da2fdfc8
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