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# albert-base-v2-finetuned-ner

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0626
- Precision: 0.9252
- Recall: 0.9330
- F1: 0.9291
- Accuracy: 0.9848

## Model description

More information needed

## limitations

#### Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 


#### How to use

You can use this model with Transformers *pipeline* for NER.

```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")

model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Scott and I live in Ohio"
ner_results = nlp(example)
print(ner_results)
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 220  | 0.0863          | 0.8827    | 0.8969 | 0.8898 | 0.9773   |
| No log        | 2.0   | 440  | 0.0652          | 0.8951    | 0.9199 | 0.9073 | 0.9809   |
| 0.1243        | 3.0   | 660  | 0.0626          | 0.9191    | 0.9208 | 0.9200 | 0.9827   |
| 0.1243        | 4.0   | 880  | 0.0585          | 0.9227    | 0.9281 | 0.9254 | 0.9843   |
| 0.0299        | 5.0   | 1100 | 0.0626          | 0.9252    | 0.9330 | 0.9291 | 0.9848   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.8.1+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "My name is Scott and I live in Columbus."}, {"text": "Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne."}], "base_model": "albert-base-v2", "model-index": [{"name": "albert-base-v2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9252213840603477, "name": "Precision"}, {"type": "recall", "value": 0.9329732113328189, "name": "Recall"}, {"type": "f1", "value": 0.9290811285541773, "name": "F1"}, {"type": "accuracy", "value": 0.9848205157332728, "name": "Accuracy"}]}]}]}
transformers pytorch albert token-classification generated_from_trainer en dataset:conll2003 base_model:albert-base-v2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-albert-base-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

albert-base-v2-finetuned-ner
============================


This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0626
* Precision: 0.9252
* Recall: 0.9330
* F1: 0.9291
* Accuracy: 0.9848


Model description
-----------------


More information needed


limitations
-----------


#### Limitations and bias


This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.


#### How to use


You can use this model with Transformers *pipeline* for NER.


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0



#### Limitations and bias


This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. #### How to use


You can use this model with Transformers *pipeline* for NER.


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------ ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #albert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-albert-base-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 #### Limitations and bias


This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. #### How to use


You can use this model with Transformers *pipeline* for NER.


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------ ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #albert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-albert-base-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
#### Limitations and bias


This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.#### How to use


You can use this model with Transformers *pipeline* for NER.


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.8.1+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0