File size: 2,290 Bytes
f679fc1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
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
|