Text Classification
Transformers
Safetensors
bert
regression
Generated from Trainer
text-embeddings-inference
Instructions to use joheras/stsb-all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joheras/stsb-all-MiniLM-L6-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joheras/stsb-all-MiniLM-L6-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joheras/stsb-all-MiniLM-L6-v2") model = AutoModelForSequenceClassification.from_pretrained("joheras/stsb-all-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
stsb-all-MiniLM-L6-v2
This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0315
- Pearson: 0.8255
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|---|---|---|---|---|
| No log | 1.0 | 360 | 0.0377 | 0.7822 |
| 0.0459 | 2.0 | 720 | 0.0407 | 0.8071 |
| 0.0213 | 3.0 | 1080 | 0.0341 | 0.8171 |
| 0.0213 | 4.0 | 1440 | 0.0304 | 0.8264 |
| 0.0157 | 5.0 | 1800 | 0.0315 | 0.8255 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for joheras/stsb-all-MiniLM-L6-v2
Base model
sentence-transformers/all-MiniLM-L6-v2