Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ludo33/e5_EC_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/e5_EC_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ludo33/e5_EC_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_EC_v2") model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_EC_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Ludo33/e5_EC_v2: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/config.json
- Command line
-
hf download hf://Ludo33/e5_EC_v2/config.json
-
curl -L -o config.json https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/config.json
1.63 kB
| { | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "Aucun th\u00e8me", | |
| "1": "Economie circulaire - Achats locaux", | |
| "2": "Economie circulaire - Alimentation", | |
| "3": "Economie circulaire - Collaboration locale", | |
| "4": "Economie circulaire - Construction durable", | |
| "5": "Economie circulaire - Plan d'actions en faveur de l'\u00e9conomie circulaire", | |
| "6": "Economie circulaire - R\u00e9emploi", | |
| "7": "Economie circulaire - Sensibilisation \u00e0 l'\u00e9conomie circulaire" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "Aucun th\u00e8me": "0", | |
| "Economie circulaire - Achats locaux": "1", | |
| "Economie circulaire - Alimentation": "2", | |
| "Economie circulaire - Collaboration locale": "3", | |
| "Economie circulaire - Construction durable": "4", | |
| "Economie circulaire - Plan d'actions en faveur de l'\u00e9conomie circulaire": "5", | |
| "Economie circulaire - R\u00e9emploi": "6", | |
| "Economie circulaire - Sensibilisation \u00e0 l'\u00e9conomie circulaire": "7" | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |