transformers tensorboard safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: en-multinerd-ner-upsampled
  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. -->

# en-multinerd-ner-upsampled

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0339
- Precision: 0.9370
- Recall: 0.9459
- F1: 0.9414
- Accuracy: 0.9919
- Per-precision: 0.9951
- Per-recall: 0.9967
- Per-f1: 0.9959
- Org-precision: 0.9359
- Org-recall: 0.9445
- Org-f1: 0.9402
- Loc-precision: 0.9708
- Loc-recall: 0.9752
- Loc-f1: 0.9730
- Dis-precision: 0.7131
- Dis-recall: 0.7404
- Dis-f1: 0.7265
- Anim-precision: 0.6809
- Anim-recall: 0.7187
- Anim-f1: 0.6993

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0293        | 1.0   | 9223  | 0.0262          | 0.9302    | 0.9363 | 0.9333 | 0.9911   | 0.9928        | 0.9951     | 0.9939 | 0.9256        | 0.9357     | 0.9306 | 0.9673        | 0.9622     | 0.9647 | 0.6819        | 0.7286     | 0.7045 | 0.6710         | 0.6799      | 0.6754  |
| 0.0149        | 2.0   | 18446 | 0.0282          | 0.9353    | 0.9466 | 0.9409 | 0.9918   | 0.9948        | 0.9969     | 0.9959 | 0.9322        | 0.9448     | 0.9385 | 0.9687        | 0.9725     | 0.9706 | 0.7190        | 0.7457     | 0.7321 | 0.675          | 0.7381      | 0.7051  |
| 0.0084        | 3.0   | 27669 | 0.0339          | 0.9370    | 0.9459 | 0.9414 | 0.9919   | 0.9951        | 0.9967     | 0.9959 | 0.9359        | 0.9445     | 0.9402 | 0.9708        | 0.9752     | 0.9730 | 0.7131        | 0.7404     | 0.7265 | 0.6809         | 0.7187      | 0.6993  |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
