Text Classification
Transformers
ONNX
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Harsh-ag26/living-stories-tone-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsh-ag26/living-stories-tone-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsh-ag26/living-stories-tone-encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsh-ag26/living-stories-tone-encoder") model = AutoModelForSequenceClassification.from_pretrained("Harsh-ag26/living-stories-tone-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
living-stories-tone-encoder
This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4853
- Micro F1: 0.4952
- Macro F1: 0.2983
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 |
|---|---|---|---|---|---|
| 0.6708 | 1.0 | 249 | 0.5906 | 0.4572 | 0.2872 |
| 0.5266 | 2.0 | 498 | 0.5050 | 0.4844 | 0.2947 |
| 0.4891 | 3.0 | 747 | 0.4853 | 0.4952 | 0.2983 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for Harsh-ag26/living-stories-tone-encoder
Base model
nreimers/MiniLM-L6-H384-uncased Quantized
sentence-transformers/all-MiniLM-L6-v2