Text Classification
Transformers
Safetensors
English
firstname_gender
feature-extraction
gender-classification
first-name
tiny-models
spiceechat
causal-lm
custom_code
Instructions to use SpiceeChat/FirstName-Genre-Classifier-30M-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpiceeChat/FirstName-Genre-Classifier-30M-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SpiceeChat/FirstName-Genre-Classifier-30M-SFT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpiceeChat/FirstName-Genre-Classifier-30M-SFT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "[BOS]", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "[EOS]", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "lowercase": false, | |
| "mask_token": "[MASK]", | |
| "max_length": null, | |
| "model_max_length": 20, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "right", | |
| "unk_token": "[UNK]", | |
| "vocab_size": 32768 | |
| } | |