Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nli-MiniLM2-L6-H768-answerable-or-not-augmented
This model is a fine-tuned version of cross-encoder/nli-MiniLM2-L6-H768 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2847
- Accuracy: 0.8943
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3594 | 1.0 | 1367 | 0.3341 | 0.8771 |
| 0.2777 | 2.0 | 2734 | 0.2861 | 0.8954 |
| 0.1927 | 3.0 | 4101 | 0.2930 | 0.9195 |
| 0.1680 | 4.0 | 5468 | 0.3255 | 0.9228 |
| 0.1006 | 5.0 | 6835 | 0.3663 | 0.9202 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
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