---
base_model: ''
tags:
- generated_from_trainer
datasets:
- few-nerd
model-index:
- name: span-marker-bert-base-fewnerd-coarse-super
  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. -->

# span-marker-bert-base-fewnerd-coarse-super

This model is a fine-tuned version of [](https://huggingface.co/) on the few-nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Overall Precision: 0.7817
- Overall Recall: 0.7683
- Overall F1: 0.7749
- Overall Accuracy: 0.9394

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0393        | 0.16  | 200  | 0.0348          | 0.7084            | 0.6377         | 0.6712     | 0.9082           |
| 0.0311        | 0.33  | 400  | 0.0233          | 0.7744            | 0.6994         | 0.7350     | 0.9225           |
| 0.0242        | 0.49  | 600  | 0.0214          | 0.7725            | 0.7293         | 0.7503     | 0.9323           |
| 0.0238        | 0.65  | 800  | 0.0204          | 0.7744            | 0.7663         | 0.7703     | 0.9359           |
| 0.0212        | 0.81  | 1000 | 0.0193          | 0.7878            | 0.7617         | 0.7746     | 0.9379           |
| 0.0181        | 0.98  | 1200 | 0.0190          | 0.7830            | 0.7671         | 0.7750     | 0.9391           |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3