README.md

---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: cetusian/ner-model-furniture-v2
  results: []
metrics:
- accuracy
- f1
- precision
- recall
datasets:
- cetusian/ner-furniture-names
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# cetusian/ner-model-furniture-v2

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3257
- Validation Loss: 0.3764
- Train Precision: 0.7369
- Train Recall: 0.7941
- Train F1: 0.7644
- Train Accuracy: 0.8553
- Epoch: 4

## Model description

The model was fine-tuned in order to recognize product names.
Ner tags: O, B-product, I-product.

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 348, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.6457     | 0.4855          | 0.6915          | 0.7054       | 0.6984   | 0.8105         | 0     |
| 0.4327     | 0.3963          | 0.7202          | 0.7764       | 0.7472   | 0.8445         | 1     |
| 0.3506     | 0.3764          | 0.7369          | 0.7941       | 0.7644   | 0.8553         | 2     |
| 0.3260     | 0.3764          | 0.7369          | 0.7941       | 0.7644   | 0.8553         | 3     |
| 0.3257     | 0.3764          | 0.7369          | 0.7941       | 0.7644   | 0.8553         | 4     |


### Framework versions

- Transformers 4.41.1
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1