|
--- |
|
language: |
|
- pcm |
|
tags: |
|
- NER |
|
datasets: |
|
- masakhaner |
|
metrics: |
|
- f1 |
|
- precision |
|
- recall |
|
license: apache-2.0 |
|
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." |
|
--- |
|
|
|
# Model description |
|
**roberta-base-pcm** is a model based on the fine-tuned RoBERTa base model. 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 |
|
-|-|-|- |
|
**roberta-base-pcm**| 88.55 | 82.45 | 85.39 |
|
|
|
# Usage |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForTokenClassification |
|
from transformers import pipeline |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("arnolfokam/roberta-base-pcm") |
|
model = AutoModelForTokenClassification.from_pretrained("arnolfokam/roberta-base-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) |
|
``` |
