File size: 2,434 Bytes
aa35437 |
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 72 73 74 75 76 77 |
---
base_model: microsoft/mdeberta-v3-base
library_name: peft
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base-finetuned-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. -->
# mdeberta-v3-base-finetuned-ner
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1094
- Precision: 0.9101
- Recall: 0.9567
- F1: 0.9328
- Accuracy: 0.9757
## 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: 0.0002
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2289 | 1.0 | 1167 | 0.1496 | 0.8186 | 0.8964 | 0.8557 | 0.9524 |
| 0.1466 | 2.0 | 2334 | 0.1193 | 0.8771 | 0.9312 | 0.9033 | 0.9677 |
| 0.1064 | 3.0 | 3501 | 0.1143 | 0.8768 | 0.9451 | 0.9097 | 0.9673 |
| 0.0808 | 4.0 | 4668 | 0.0978 | 0.8968 | 0.9491 | 0.9222 | 0.9724 |
| 0.0668 | 5.0 | 5835 | 0.1111 | 0.8889 | 0.9533 | 0.9200 | 0.9713 |
| 0.0559 | 6.0 | 7002 | 0.1168 | 0.9054 | 0.9561 | 0.9301 | 0.9746 |
| 0.0459 | 7.0 | 8169 | 0.1085 | 0.9174 | 0.9457 | 0.9313 | 0.9749 |
| 0.0421 | 8.0 | 9336 | 0.1077 | 0.9141 | 0.9516 | 0.9325 | 0.9759 |
| 0.0324 | 9.0 | 10503 | 0.1084 | 0.9148 | 0.9575 | 0.9357 | 0.9766 |
| 0.03 | 10.0 | 11670 | 0.1094 | 0.9101 | 0.9567 | 0.9328 | 0.9757 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1 |