Text Classification
Transformers
Safetensors
English
emoaxis
feature-extraction
multi-label-classification
fine-grained
emotion-classification
custom_code
Eval Results (legacy)
Instructions to use Hidden-States/roberta-base-emopillars-contextless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hidden-States/roberta-base-emopillars-contextless with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hidden-States/roberta-base-emopillars-contextless", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hidden-States/roberta-base-emopillars-contextless", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download configuration_emoaxis.py from Hidden-States/roberta-base-emopillars-contextless: direct link, hf CLI and curl.
- Browser
- Download file 366 Bytes
-
https://huggingface.co/Hidden-States/roberta-base-emopillars-contextless/resolve/main/configuration_emoaxis.py
- Command line
-
hf download hf://Hidden-States/roberta-base-emopillars-contextless/configuration_emoaxis.py
-
curl -L -o configuration_emoaxis.py https://huggingface.co/Hidden-States/roberta-base-emopillars-contextless/resolve/main/configuration_emoaxis.py
366 Bytes
| from transformers import RobertaConfig | |
| class EmoAxisConfig(RobertaConfig): | |
| model_type = "emoaxis" | |
| def __init__(self, num_classes=28, freeze_upto=0, **kwargs): | |
| kwargs["max_position_embeddings"] = 514 | |
| kwargs["type_vocab_size"] = 1 | |
| super().__init__(**kwargs) | |
| self.num_classes = num_classes | |
| self.freeze_upto = freeze_upto | |