Instructions to use Nargizi/screeve-postagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nargizi/screeve-postagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Nargizi/screeve-postagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Nargizi/screeve-postagger") model = AutoModelForTokenClassification.from_pretrained("Nargizi/screeve-postagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Nargizi/screeve-postagger: direct link, hf CLI and curl.
- Browser
- Download file 945 Bytes
-
https://huggingface.co/Nargizi/screeve-postagger/resolve/main/config.json
- Command line
-
hf download hf://Nargizi/screeve-postagger/config.json
-
curl -L -o config.json https://huggingface.co/Nargizi/screeve-postagger/resolve/main/config.json
945 Bytes
| { | |
| "_name_or_path": "models/transformer_f", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForTokenClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 64, | |
| "dropout": 0.1, | |
| "hidden_dim": 128, | |
| "id2label": { | |
| "0": "A", | |
| "1": "Adv", | |
| "2": "Cj", | |
| "3": "Interj", | |
| "4": "N", | |
| "5": "Num", | |
| "6": "Other", | |
| "7": "Pp", | |
| "8": "Pron", | |
| "9": "Punct", | |
| "10": "V" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "A": "0", | |
| "Adv": "1", | |
| "Cj": "2", | |
| "Interj": "3", | |
| "N": "4", | |
| "Num": "5", | |
| "Other": "6", | |
| "Pp": "7", | |
| "Pron": "8", | |
| "Punct": "9", | |
| "V": "10" | |
| }, | |
| "max_position_embeddings": 200, | |
| "model_type": "distilbert", | |
| "n_classes": 11, | |
| "n_heads": 1, | |
| "n_layers": 1, | |
| "pad_token_id": 1, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.31.0", | |
| "vocab_size": 6000 | |
| } | |