
# Model description
**mbert-base-uncased-ner-pcm**  is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)

# Intended Use
- Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
- Not intended for practical purposes.

# Training Data
This model was fine-tuned on the Nigerian Pidgin corpus **(pcm)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups.

# Training procedure
This model was trained on a single NVIDIA P5000 from [Paperspace](https://www.paperspace.com)
#### Hyperparameters
- **Learning Rate:** 5e-5
- **Batch Size:** 32
- **Maximum Sequence Length:** 164
- **Epochs:** 30

# Evaluation Data
We evaluated this model on the test split of the Swahili corpus **(pcm)** present in the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) with no thresholding.

# Metrics
- Precision
- Recall
- F1-score

# Limitations
- The size of the pre-trained language model prevents its usage in anything other than research.
- Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
- The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.

# Caveats and Recommendations
- The topics in the dataset corpus are centered around **News**. Future training could be done with a more diverse corpus.

# Results
Model Name| Precision | Recall | F1-score
-|-|-|-
**mbert-base-uncased-ner-pcm**| 90.38 | 82.44 | 86.23

# Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-pcm")
model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-pcm")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."

ner_results = nlp(example)
print(ner_results)
```
{"language": ["pcm"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."}]}
transformers pytorch bert token-classification NER pcm dataset:masakhaner license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Model description
=================


mbert-base-uncased-ner-pcm is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:


* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)


Intended Use
============


* Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
* Not intended for practical purposes.


Training Data
=============


This model was fine-tuned on the Nigerian Pidgin corpus (pcm) of the MasakhaNER dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups.


Training procedure
==================


This model was trained on a single NVIDIA P5000 from Paperspace


#### Hyperparameters


* Learning Rate: 5e-5
* Batch Size: 32
* Maximum Sequence Length: 164
* Epochs: 30


Evaluation Data
===============


We evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.


Metrics
=======


* Precision
* Recall
* F1-score


Limitations
===========


* The size of the pre-trained language model prevents its usage in anything other than research.
* Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
* The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.


Caveats and Recommendations
===========================


* The topics in the dataset corpus are centered around News. Future training could be done with a more diverse corpus.


Results
=======



Usage
=====



#### Hyperparameters


* Learning Rate: 5e-5
* Batch Size: 32
* Maximum Sequence Length: 164
* Epochs: 30


Evaluation Data
===============


We evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.


Metrics
=======


* Precision
* Recall
* F1-score


Limitations
===========


* The size of the pre-trained language model prevents its usage in anything other than research.
* Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
* The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.


Caveats and Recommendations
===========================


* The topics in the dataset corpus are centered around News. Future training could be done with a more diverse corpus.


Results
=======



Usage
=====
TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### Hyperparameters


* Learning Rate: 5e-5
* Batch Size: 32
* Maximum Sequence Length: 164
* Epochs: 30


Evaluation Data
===============


We evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.


Metrics
=======


* Precision
* Recall
* F1-score


Limitations
===========


* The size of the pre-trained language model prevents its usage in anything other than research.
* Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
* The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.


Caveats and Recommendations
===========================


* The topics in the dataset corpus are centered around News. Future training could be done with a more diverse corpus.


Results
=======



Usage
=====
passage: TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### Hyperparameters


* Learning Rate: 5e-5
* Batch Size: 32
* Maximum Sequence Length: 164
* Epochs: 30


Evaluation Data
===============


We evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.


Metrics
=======


* Precision
* Recall
* F1-score


Limitations
===========


* The size of the pre-trained language model prevents its usage in anything other than research.
* Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
* The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.


Caveats and Recommendations
===========================


* The topics in the dataset corpus are centered around News. Future training could be done with a more diverse corpus.


Results
=======



Usage
=====