Text Classification
Transformers
Safetensors
English
havelock-orality-regressor
feature-extraction
regression
modernbert
orality
linguistics
rhetorical-analysis
custom_code
Eval Results (legacy)
Instructions to use HavelockAI/bert-orality-regressor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HavelockAI/bert-orality-regressor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HavelockAI/bert-orality-regressor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HavelockAI/bert-orality-regressor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +7 -7
- model.safetensors +1 -1
config.json
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 50281,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"sparse_prediction": false,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"vocab_size": 50368
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"AutoConfig": "modeling_havelock.HavelockOralityConfig",
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"AutoModel": "modeling_havelock.HavelockOralityRegressor",
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"AutoModelForSequenceClassification": "modeling_havelock.HavelockOralityRegressor"
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}
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}
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "modeling_havelock.HavelockOralityConfig",
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"AutoModel": "modeling_havelock.HavelockOralityRegressor",
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"AutoModelForSequenceClassification": "modeling_havelock.HavelockOralityRegressor"
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},
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"bos_token_id": 50281,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"sparse_prediction": false,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"vocab_size": 50368
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1037272580
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