Text Classification
setfit
TensorBoard
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use cbpuschmann/MiniLM-klimacoder_v0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use cbpuschmann/MiniLM-klimacoder_v0.4 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("cbpuschmann/MiniLM-klimacoder_v0.4") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use cbpuschmann/MiniLM-klimacoder_v0.4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cbpuschmann/MiniLM-klimacoder_v0.4") 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
File size: 885 Bytes
175a9d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"data_path": "autotrain-uezr0-uoiv8/autotrain-data",
"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"lr": 5e-05,
"epochs": 3,
"max_seq_length": 128,
"batch_size": 8,
"warmup_ratio": 0.1,
"gradient_accumulation": 1,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.0,
"max_grad_norm": 1.0,
"seed": 42,
"train_split": "train",
"valid_split": "validation",
"text_column": "autotrain_text",
"target_column": "autotrain_label",
"logging_steps": -1,
"project_name": "autotrain-uezr0-uoiv8",
"auto_find_batch_size": false,
"mixed_precision": "fp16",
"save_total_limit": 1,
"push_to_hub": true,
"eval_strategy": "epoch",
"username": "cbpuschmann",
"log": "tensorboard",
"early_stopping_patience": 5,
"early_stopping_threshold": 0.01
} |