pytorch distilbert token-classification transformers generated_from_trainer license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: ner-leg
  results: []
---

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# ner-leg

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0656
- Precision: 0.8873
- Recall: 0.8565
- Macro F1: 0.8716
- Micro F1: 0.8716
- Accuracy: 0.9786
- Marker F1: 0.8993
- Marker Precision: 0.8701
- Marker Recall: 0.9306
- Reference F1: 0.9558
- Reference Precision: 0.9474
- Reference Recall: 0.9643
- Term F1: 0.8621
- Term Precision: 0.8843
- Term Recall: 0.8410

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Macro F1 | Micro F1 | Accuracy | Marker F1 | Marker Precision | Marker Recall | Reference F1 | Reference Precision | Reference Recall | Term F1 | Term Precision | Term Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|:--------:|:---------:|:----------------:|:-------------:|:------------:|:-------------------:|:----------------:|:-------:|:--------------:|:-----------:|
| No log        | 1.0   | 100  | 0.1980          | 0.5457    | 0.7472 | 0.6307   | 0.6307   | 0.9212   | 0.8557    | 0.7748           | 0.9556        | 0.8249       | 0.7228              | 0.9605           | 0.5782  | 0.4966         | 0.6919      |
| No log        | 2.0   | 200  | 0.1972          | 0.5982    | 0.8348 | 0.6970   | 0.6970   | 0.9253   | 0.8683    | 0.7739           | 0.9889        | 0.8503       | 0.7802              | 0.9342           | 0.6576  | 0.5578         | 0.8009      |
| No log        | 3.0   | 300  | 0.2805          | 0.6085    | 0.8248 | 0.7003   | 0.7003   | 0.9244   | 0.8279    | 0.712            | 0.9889        | 0.8118       | 0.7340              | 0.9079           | 0.6693  | 0.5799         | 0.7915      |
| No log        | 4.0   | 400  | 0.2992          | 0.5858    | 0.8461 | 0.6923   | 0.6923   | 0.9234   | 0.8381    | 0.7333           | 0.9778        | 0.8488       | 0.7604              | 0.9605           | 0.6556  | 0.5490         | 0.8136      |
| 0.1651        | 5.0   | 500  | 0.3312          | 0.5965    | 0.8048 | 0.6851   | 0.6851   | 0.9246   | 0.8476    | 0.7417           | 0.9889        | 0.8521       | 0.7742              | 0.9474           | 0.6435  | 0.5572         | 0.7615      |
| 0.1651        | 6.0   | 600  | 0.3450          | 0.6161    | 0.8335 | 0.7085   | 0.7085   | 0.9281   | 0.8911    | 0.8036           | 1.0           | 0.8757       | 0.7957              | 0.9737           | 0.6653  | 0.5731         | 0.7930      |
| 0.1651        | 7.0   | 700  | 0.3504          | 0.6263    | 0.8223 | 0.7110   | 0.7110   | 0.9284   | 0.8955    | 0.8108           | 1.0           | 0.8824       | 0.7979              | 0.9868           | 0.6662  | 0.5829         | 0.7773      |
| 0.1651        | 8.0   | 800  | 0.3739          | 0.6190    | 0.8173 | 0.7044   | 0.7044   | 0.9280   | 0.8955    | 0.8108           | 1.0           | 0.8876       | 0.8065              | 0.9868           | 0.6577  | 0.5734         | 0.7709      |
| 0.1651        | 9.0   | 900  | 0.4025          | 0.6164    | 0.8185 | 0.7032   | 0.7032   | 0.9284   | 0.89      | 0.8091           | 0.9889        | 0.8876       | 0.8065              | 0.9868           | 0.6573  | 0.5711         | 0.7741      |
| 0.0104        | 10.0  | 1000 | 0.3988          | 0.6240    | 0.8185 | 0.7082   | 0.7082   | 0.9285   | 0.8955    | 0.8108           | 1.0           | 0.8876       | 0.8065              | 0.9868           | 0.6622  | 0.5794         | 0.7725      |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
