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-masked-ner-more-training
  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-masked-ner-more-training

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: 0.0371
- Precision: 0.9386
- Recall: 0.9456
- F1: 0.9421
- Accuracy: 0.9920
- Per-precision: 0.9960
- Per-recall: 0.9961
- Per-f1: 0.9961
- Org-precision: 0.9364
- Org-recall: 0.9419
- Org-f1: 0.9392
- Loc-precision: 0.9719
- Loc-recall: 0.9752
- Loc-f1: 0.9735
- Dis-precision: 0.7059
- Dis-recall: 0.7363
- Dis-f1: 0.7208
- Anim-precision: 0.7033
- Anim-recall: 0.7275
- Anim-f1: 0.7152

## 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: 5

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0243        | 1.0   | 8205  | 0.0271          | 0.9184    | 0.9458 | 0.9319 | 0.9906   | 0.9927        | 0.9963     | 0.9945 | 0.8838        | 0.9499     | 0.9157 | 0.9586        | 0.9707     | 0.9646 | 0.6719        | 0.7274     | 0.6986 | 0.6659         | 0.7557      | 0.7080  |
| 0.0159        | 2.0   | 16410 | 0.0263          | 0.9324    | 0.9448 | 0.9386 | 0.9917   | 0.9913        | 0.9965     | 0.9939 | 0.9237        | 0.9507     | 0.9370 | 0.9613        | 0.9739     | 0.9675 | 0.7128        | 0.7233     | 0.7180 | 0.6990         | 0.7187      | 0.7087  |
| 0.0101        | 3.0   | 24615 | 0.0295          | 0.9396    | 0.9426 | 0.9411 | 0.9919   | 0.9935        | 0.9963     | 0.9949 | 0.9352        | 0.9386     | 0.9369 | 0.9704        | 0.9732     | 0.9718 | 0.7168        | 0.7286     | 0.7226 | 0.7143         | 0.7055      | 0.7098  |
| 0.0057        | 4.0   | 32820 | 0.0335          | 0.9375    | 0.9465 | 0.9420 | 0.9919   | 0.9967        | 0.9955     | 0.9961 | 0.9341        | 0.9470     | 0.9405 | 0.9694        | 0.9739     | 0.9717 | 0.7065        | 0.7499     | 0.7275 | 0.7052         | 0.7257      | 0.7153  |
| 0.004         | 5.0   | 41025 | 0.0371          | 0.9386    | 0.9456 | 0.9421 | 0.9920   | 0.9960        | 0.9961     | 0.9961 | 0.9364        | 0.9419     | 0.9392 | 0.9719        | 0.9752     | 0.9735 | 0.7059        | 0.7363     | 0.7208 | 0.7033         | 0.7275      | 0.7152  |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
