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
Update modeling_firstname_gender.py
Browse files
modeling_firstname_gender.py
CHANGED
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@@ -184,7 +184,8 @@ class FirstNameGenderForCausalLM(PreTrainedModel):
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config_class = FirstNameGenderConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = False
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def __init__(self, config):
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super().__init__(config)
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config_class = FirstNameGenderConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = False
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all_tied_weights_keys = {}
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def __init__(self, config):
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super().__init__(config)
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