Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Finnish-actions/SetFit-FinBERT1-Avg-request with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Finnish-actions/SetFit-FinBERT1-Avg-request with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Finnish-actions/SetFit-FinBERT1-Avg-request") - sentence-transformers
How to use Finnish-actions/SetFit-FinBERT1-Avg-request with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Finnish-actions/SetFit-FinBERT1-Avg-request") 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": "TurkuNLP/bert-base-finnish-cased-v1", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "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", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 50105 | |
| } | |