Instructions to use Cheykong/HRVibeCheck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheykong/HRVibeCheck with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheykong/HRVibeCheck")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheykong/HRVibeCheck") model = AutoModelForSequenceClassification.from_pretrained("Cheykong/HRVibeCheck") - Notebooks
- Google Colab
- Kaggle
Upload 3 files
Browse files- config.json +24 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"dtype": "float32",
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.57.6",
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"vocab_size": 30522
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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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oid sha256:42982420c483e6c74b698449a195755ce5740e1bcfe4321698edfffc211e2805
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size 267832560
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:654d97f1a01bcda25d8240c1d2a737f552353d65842bc87586e933ccc0583f3a
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size 5777
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