Instructions to use seduerr/paiintent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seduerr/paiintent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="seduerr/paiintent")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("seduerr/paiintent", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "embedding_size": 768, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "max_position_embeddings": 512, | |
| "model_type": "squeezebert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "type_vocab_size": 2, | |
| "vocab_size": 30528, | |
| "q_groups": 4, | |
| "k_groups": 4, | |
| "v_groups": 4, | |
| "post_attention_groups": 1, | |
| "intermediate_groups": 4, | |
| "output_groups": 4, | |
| "num_labels": 3, | |
| "label2id": { | |
| "ENTAILMENT": 2, | |
| "NEUTRAL": 1, | |
| "CONTRADICTION": 0 | |
| }, | |
| "id2label": { | |
| "2": "ENTAILMENT", | |
| "1": "NEUTRAL", | |
| "0": "CONTRADICTION" | |
| } | |
| } | |