Instructions to use redwoodresearch/classifier-18aug-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redwoodresearch/classifier-18aug-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="redwoodresearch/classifier-18aug-train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("redwoodresearch/classifier-18aug-train") model = AutoModelForSequenceClassification.from_pretrained("redwoodresearch/classifier-18aug-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/tmp/rr-comet-model-cache/redwood/classifier-18aug-train-val/43e987980bb545178984353849ca2014/classifier", | |
| "architectures": [ | |
| "DebertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 1024, | |
| "pos_att_type": [ | |
| "c2p", | |
| "p2c" | |
| ], | |
| "position_biased_input": false, | |
| "relative_attention": true, | |
| "transformers_version": "4.8.2", | |
| "type_vocab_size": 0, | |
| "vocab_size": 50265 | |
| } | |