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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-expense-ner", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilbert-expense-ner
======================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1