Text Classification
Transformers
Safetensors
English
longformer_intent_test
feature-extraction
longformer
intent-classification
nlp
custom_code
Instructions to use organiz-1/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use organiz-1/test-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="organiz-1/test-model", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("organiz-1/test-model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Wilson commited on
Update config.json
Browse files- config.json +14 -14
config.json
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{
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"architectures": [
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"attention_mode": "longformer",
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"attention_probs_dropout_prob": 0.1,
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"attention_window": [
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512,
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"bos_token_id": 0,
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"dtype": "float32",
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 4098,
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"model_type": "longformer",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"onnx_export": false,
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{
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"architectures": [
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"LongformerIntentModel"
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],
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"auto_map": {
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"AutoConfig": "configuration_longformer.LongformerIntentConfig",
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"AutoModel": "modeling_longformer.LongformerIntentModel",
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"AutoModelForSequenceClassification": "modeling_longformer.LongformerIntentModel"
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},
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"intent_columns": [
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"navigational",
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"commercial",
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"informational",
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"transactional"
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],
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"model_type": "longformer_intent_test",
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"attention_mode": "longformer",
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"attention_probs_dropout_prob": 0.1,
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"attention_window": [
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512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512
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"bos_token_id": 0,
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"dtype": "float32",
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 4098,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"onnx_export": false,
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