transformers tensorboard safetensors distilbert token-classification generated_from_trainer dataset:transformer_dataset_ner base_model:dslim/distilbert-NER base_model:finetune:dslim/distilbert-NER license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: dslim/distilbert-NER
datasets:
- transformer_dataset_ner
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: huner_ncbi_disease_dslim
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: transformer_dataset_ner
      type: transformer_dataset_ner
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.8325183374083129
      name: Precision
    - type: recall
      value: 0.8653113087674714
      name: Recall
    - type: f1
      value: 0.8485981308411215
      name: F1
    - type: accuracy
      value: 0.9849891909996041
      name: Accuracy
---

<!-- 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. -->

# huner_ncbi_disease_dslim

This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the transformer_dataset_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
- Precision: 0.8325
- Recall: 0.8653
- F1: 0.8486
- Accuracy: 0.9850

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1243        | 1.0   | 667   | 0.0669          | 0.7013    | 0.8412 | 0.7649 | 0.9787   |
| 0.0512        | 2.0   | 1334  | 0.0656          | 0.7825    | 0.8412 | 0.8108 | 0.9818   |
| 0.0221        | 3.0   | 2001  | 0.0744          | 0.7908    | 0.8501 | 0.8194 | 0.9822   |
| 0.0107        | 4.0   | 2668  | 0.1022          | 0.7940    | 0.8475 | 0.8199 | 0.9808   |
| 0.008         | 5.0   | 3335  | 0.1055          | 0.7818    | 0.8602 | 0.8191 | 0.9816   |
| 0.0057        | 6.0   | 4002  | 0.1173          | 0.8067    | 0.8590 | 0.832  | 0.9830   |
| 0.0027        | 7.0   | 4669  | 0.1188          | 0.8188    | 0.8501 | 0.8342 | 0.9834   |
| 0.0022        | 8.0   | 5336  | 0.1229          | 0.8080    | 0.8450 | 0.8261 | 0.9826   |
| 0.0019        | 9.0   | 6003  | 0.1341          | 0.8007    | 0.8526 | 0.8258 | 0.9834   |
| 0.0019        | 10.0  | 6670  | 0.1360          | 0.8045    | 0.8628 | 0.8326 | 0.9822   |
| 0.0011        | 11.0  | 7337  | 0.1376          | 0.8163    | 0.8640 | 0.8395 | 0.9838   |
| 0.0008        | 12.0  | 8004  | 0.1447          | 0.8007    | 0.8577 | 0.8282 | 0.9833   |
| 0.0006        | 13.0  | 8671  | 0.1381          | 0.8139    | 0.8615 | 0.8370 | 0.9839   |
| 0.0005        | 14.0  | 9338  | 0.1398          | 0.8297    | 0.8666 | 0.8477 | 0.9843   |
| 0.0004        | 15.0  | 10005 | 0.1404          | 0.8232    | 0.8640 | 0.8431 | 0.9842   |
| 0.0003        | 16.0  | 10672 | 0.1486          | 0.8329    | 0.8551 | 0.8439 | 0.9838   |
| 0.0           | 17.0  | 11339 | 0.1469          | 0.8114    | 0.8691 | 0.8393 | 0.9837   |
| 0.0002        | 18.0  | 12006 | 0.1500          | 0.8297    | 0.8602 | 0.8447 | 0.9843   |
| 0.0001        | 19.0  | 12673 | 0.1489          | 0.8315    | 0.8653 | 0.8481 | 0.9849   |
| 0.0           | 20.0  | 13340 | 0.1484          | 0.8325    | 0.8653 | 0.8486 | 0.9850   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
