File size: 2,071 Bytes
fe36acc | 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 | ---
library_name: transformers
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-base-cased-finetuned-ner-final
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. -->
# deberta-base-cased-finetuned-ner-final
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4915
- Precision: 0.8451
- Recall: 0.8570
- F1: 0.8510
- Accuracy: 0.9669
## 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: 4.331046950257529e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.022489239711791377
- num_epochs: 4
- label_smoothing_factor: 0.0628867621783132
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4906 | 1.0 | 4250 | 0.4915 | 0.8098 | 0.8309 | 0.8202 | 0.9619 |
| 0.4703 | 2.0 | 8500 | 0.4831 | 0.8368 | 0.8407 | 0.8387 | 0.9649 |
| 0.4488 | 3.0 | 12750 | 0.4850 | 0.8295 | 0.8531 | 0.8411 | 0.9651 |
| 0.4245 | 4.0 | 17000 | 0.4915 | 0.8451 | 0.8570 | 0.8510 | 0.9669 |
### Framework versions
- Transformers 4.50.1
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|