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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-expense-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-expense-ner
This model is a fine-tuned version of [renjithks/distilbert-cord-ner](https://huggingface.co/renjithks/distilbert-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930
- Precision: 0.5096
- Recall: 0.4852
- F1: 0.4971
- Accuracy: 0.9275
## 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 | 22 | 0.3635 | 0.2888 | 0.0945 | 0.1424 | 0.8866 |
| No log | 2.0 | 44 | 0.2795 | 0.3213 | 0.3018 | 0.3113 | 0.8982 |
| No log | 3.0 | 66 | 0.2432 | 0.4243 | 0.4034 | 0.4136 | 0.9161 |
| No log | 4.0 | 88 | 0.2446 | 0.4615 | 0.4654 | 0.4635 | 0.9193 |
| No log | 5.0 | 110 | 0.2410 | 0.5143 | 0.4810 | 0.4971 | 0.9293 |
| No log | 6.0 | 132 | 0.2598 | 0.5283 | 0.4612 | 0.4925 | 0.9305 |
| No log | 7.0 | 154 | 0.2963 | 0.5230 | 0.4485 | 0.4829 | 0.9268 |
| No log | 8.0 | 176 | 0.2753 | 0.4928 | 0.4838 | 0.4883 | 0.9283 |
| No log | 9.0 | 198 | 0.2897 | 0.5194 | 0.4725 | 0.4948 | 0.9295 |
| No log | 10.0 | 220 | 0.2930 | 0.5096 | 0.4852 | 0.4971 | 0.9275 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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