mbert-base-uncased-pcm is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
- Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
- Not intended for practical purposes.
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.
This model was trained on a single NVIDIA P5000 from Paperspace
- Learning Rate: 5e-5
- Batch Size: 32
- Maximum Sequence Length: 164
- Epochs: 30
We evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.
- 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.
from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-pcm") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-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)
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This model can be loaded on the Inference API on-demand.