Instructions to use l3cube-pune/hindi-topic-all-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-topic-all-doc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/hindi-topic-all-doc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-topic-all-doc") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/hindi-topic-all-doc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from l3cube-pune/hindi-topic-all-doc: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/l3cube-pune/hindi-topic-all-doc/resolve/main/config.json
- Command line
-
hf download hf://l3cube-pune/hindi-topic-all-doc/config.json
-
curl -L -o config.json https://huggingface.co/l3cube-pune/hindi-topic-all-doc/resolve/main/config.json
1.28 kB
| { | |
| "_name_or_path": "l3cube-pune/hindi-topic-all-doc", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "embedding_size": 768, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "politics", | |
| "1": "health-news-hindi", | |
| "2": "international", | |
| "3": "business", | |
| "4": "technology-news", | |
| "5": "national", | |
| "6": "khel", | |
| "7": "entertainment", | |
| "8": "crime-news-hindi", | |
| "9": "education", | |
| "10": "automobile" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "automobile": 10, | |
| "business": 3, | |
| "crime-news-hindi": 8, | |
| "education": 9, | |
| "entertainment": 7, | |
| "health-news-hindi": 1, | |
| "international": 2, | |
| "khel": 6, | |
| "national": 5, | |
| "politics": 0, | |
| "technology-news": 4 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 197285 | |
| } | |