Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use LKriesch/TwinTransitionMapper_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use LKriesch/TwinTransitionMapper_AI with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("LKriesch/TwinTransitionMapper_AI") - sentence-transformers
How to use LKriesch/TwinTransitionMapper_AI with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LKriesch/TwinTransitionMapper_AI") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "T:\\Trabbi\\sft_ai", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "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, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "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": "4.44.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |