Instructions to use gayatrividhate/sentiment_analysis_SetFit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gayatrividhate/sentiment_analysis_SetFit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gayatrividhate/sentiment_analysis_SetFit") 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] - setfit
How to use gayatrividhate/sentiment_analysis_SetFit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("gayatrividhate/sentiment_analysis_SetFit") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/home/gayatri/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-albert-small-v2/", | |
| "architectures": [ | |
| "AlbertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0, | |
| "bos_token_id": 2, | |
| "classifier_dropout_prob": 0.1, | |
| "down_scale_factor": 1, | |
| "embedding_size": 128, | |
| "eos_token_id": 3, | |
| "gap_size": 0, | |
| "hidden_act": "gelu_new", | |
| "hidden_dropout_prob": 0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "negative", | |
| "1": "neutral", | |
| "2": "positive" | |
| }, | |
| "initializer_range": 0.02, | |
| "inner_group_num": 1, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "negative": 0, | |
| "neutral": 1, | |
| "positive": 2 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "albert", | |
| "net_structure_type": 0, | |
| "num_attention_heads": 12, | |
| "num_hidden_groups": 1, | |
| "num_hidden_layers": 6, | |
| "num_memory_blocks": 0, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.21.3", | |
| "type_vocab_size": 2, | |
| "vocab_size": 30000 | |
| } | |