---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-cord-ner
  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. -->

# distilbert-cord-ner

This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-de-no-da-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-de-no-da-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1670
- Precision: 0.9128
- Recall: 0.9242
- F1: 0.9185
- Accuracy: 0.9656

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 113  | 0.1814          | 0.8480    | 0.8618 | 0.8548 | 0.9393   |
| No log        | 2.0   | 226  | 0.1755          | 0.8669    | 0.9002 | 0.8832 | 0.9427   |
| No log        | 3.0   | 339  | 0.1499          | 0.8800    | 0.8935 | 0.8867 | 0.9533   |
| No log        | 4.0   | 452  | 0.1340          | 0.8975    | 0.9079 | 0.9027 | 0.9596   |
| 0.1812        | 5.0   | 565  | 0.1553          | 0.8999    | 0.9146 | 0.9072 | 0.9592   |
| 0.1812        | 6.0   | 678  | 0.1474          | 0.8961    | 0.9021 | 0.8991 | 0.9562   |
| 0.1812        | 7.0   | 791  | 0.1682          | 0.9135    | 0.9223 | 0.9179 | 0.9622   |
| 0.1812        | 8.0   | 904  | 0.1663          | 0.8960    | 0.9175 | 0.9066 | 0.9613   |
| 0.0199        | 9.0   | 1017 | 0.1753          | 0.9061    | 0.9261 | 0.9160 | 0.9635   |
| 0.0199        | 10.0  | 1130 | 0.1670          | 0.9128    | 0.9242 | 0.9185 | 0.9656   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1