Instructions to use KevSun/Personality_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevSun/Personality_LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KevSun/Personality_LM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM") model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +3 -3
config.json
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0",
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"position_embedding_type": "absolute",
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"problem_type": "regression",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0",
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"position_embedding_type": "absolute",
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"problem_type": "regression",
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"torch_dtype": "float32",
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"transformers_version": "4.30.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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