---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_column_bert-base-NER
  results: []
language:
- en
widget:
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- other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti 6204699090_BD 55L Toaster Oven with Double Glass
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---

<!-- 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. -->

# ner_column_bert-base-NER

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1855
- Precision: 0.7651
- Recall: 0.7786
- F1: 0.7718
- Accuracy: 0.9026

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 702   | 0.7382          | 0.2576    | 0.1887 | 0.2178 | 0.7127   |
| 0.9356        | 2.0   | 1404  | 0.4405          | 0.5139    | 0.4331 | 0.4700 | 0.8157   |
| 0.5445        | 3.0   | 2106  | 0.3608          | 0.5712    | 0.5143 | 0.5413 | 0.8404   |
| 0.5445        | 4.0   | 2808  | 0.3226          | 0.6188    | 0.5840 | 0.6009 | 0.8550   |
| 0.4316        | 5.0   | 3510  | 0.2757          | 0.6788    | 0.6569 | 0.6676 | 0.8728   |
| 0.3605        | 6.0   | 4212  | 0.2828          | 0.6584    | 0.6346 | 0.6463 | 0.8697   |
| 0.3605        | 7.0   | 4914  | 0.2456          | 0.7108    | 0.6926 | 0.7015 | 0.8820   |
| 0.3153        | 8.0   | 5616  | 0.2385          | 0.7055    | 0.6986 | 0.7021 | 0.8855   |
| 0.282         | 9.0   | 6318  | 0.2345          | 0.7044    | 0.6961 | 0.7002 | 0.8853   |
| 0.2587        | 10.0  | 7020  | 0.2313          | 0.7081    | 0.7049 | 0.7065 | 0.8862   |
| 0.2587        | 11.0  | 7722  | 0.2026          | 0.7734    | 0.7537 | 0.7634 | 0.8968   |
| 0.239         | 12.0  | 8424  | 0.1980          | 0.7651    | 0.7687 | 0.7669 | 0.8991   |
| 0.2241        | 13.0  | 9126  | 0.2091          | 0.7368    | 0.7423 | 0.7395 | 0.8936   |
| 0.2241        | 14.0  | 9828  | 0.1954          | 0.7693    | 0.7684 | 0.7689 | 0.8987   |
| 0.2124        | 15.0  | 10530 | 0.1916          | 0.7668    | 0.7749 | 0.7708 | 0.9008   |
| 0.2025        | 16.0  | 11232 | 0.1841          | 0.7699    | 0.7794 | 0.7746 | 0.9024   |
| 0.2025        | 17.0  | 11934 | 0.1938          | 0.7527    | 0.7626 | 0.7576 | 0.8992   |
| 0.193         | 18.0  | 12636 | 0.1849          | 0.7705    | 0.7841 | 0.7772 | 0.9040   |
| 0.1877        | 19.0  | 13338 | 0.1927          | 0.7510    | 0.7649 | 0.7579 | 0.9005   |
| 0.1821        | 20.0  | 14040 | 0.1855          | 0.7651    | 0.7786 | 0.7718 | 0.9026   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3