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# silviacamplani/distilbert-finetuned-dapt-ner-ai

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9448
- Validation Loss: 0.9212
- Train Precision: 0.3164
- Train Recall: 0.3186
- Train F1: 0.3175
- Train Accuracy: 0.7524
- Epoch: 6

## 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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 2.6857     | 1.8199          | 0.0             | 0.0          | 0.0      | 0.6480         | 0     |
| 1.6775     | 1.4868          | 0.0             | 0.0          | 0.0      | 0.6480         | 1     |
| 1.3847     | 1.2452          | 0.0938          | 0.0102       | 0.0184   | 0.6565         | 2     |
| 1.2067     | 1.1198          | 0.1659          | 0.1244       | 0.1422   | 0.7077         | 3     |
| 1.0946     | 1.0321          | 0.2255          | 0.1925       | 0.2077   | 0.7225         | 4     |
| 1.0057     | 0.9640          | 0.2835          | 0.2777       | 0.2806   | 0.7433         | 5     |
| 0.9448     | 0.9212          | 0.3164          | 0.3186       | 0.3175   | 0.7524         | 6     |


### Framework versions

- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-finetuned-dapt-ner-ai", "results": []}]}
transformers tf tensorboard distilbert token-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 

silviacamplani/distilbert-finetuned-dapt-ner-ai
===============================================


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.9448
* Validation Loss: 0.9212
* Train Precision: 0.3164
* Train Recall: 0.3186
* Train F1: 0.3175
* Train Accuracy: 0.7524
* Epoch: 6


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:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 350, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.20.1
* TensorFlow 2.6.4
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 350, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.6.4
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 350, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.6.4
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 350, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16### Training results### Framework versions


* Transformers 4.20.1
* TensorFlow 2.6.4
* Datasets 2.1.0
* Tokenizers 0.12.1