Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use San-Analytics/classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use San-Analytics/classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="San-Analytics/classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("San-Analytics/classifier") model = AutoModelForSequenceClassification.from_pretrained("San-Analytics/classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,959 Bytes
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library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0090
- Accuracy: 1.0
- F1 Macro: 1.0
- F1 Weighted: 1.0
## 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: 32
- eval_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 1.2992 | 1.0 | 63 | 0.1976 | 1.0 | 1.0 | 1.0 |
| 0.1725 | 2.0 | 126 | 0.0234 | 1.0 | 1.0 | 1.0 |
| 0.0314 | 3.0 | 189 | 0.0133 | 1.0 | 1.0 | 1.0 |
| 0.0143 | 4.0 | 252 | 0.0101 | 1.0 | 1.0 | 1.0 |
| 0.0124 | 5.0 | 315 | 0.0093 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 5.8.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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