| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 60.2112       | 0.74  | 500   | 64.8189         | 1.0    |
| 8.1128        | 1.48  | 1000  | 6.8997          | 1.0    |
| 6.0492        | 2.22  | 1500  | 5.9677          | 1.9495 |
| 5.9326        | 2.95  | 2000  | 5.8845          | 1.4092 |
| 5.8763        | 3.69  | 2500  | 5.8460          | 1.6126 |
| 5.7888        | 4.43  | 3000  | 5.7545          | 2.2034 |
| 5.735         | 5.17  | 3500  | 5.6777          | 2.3350 |
| 5.6861        | 5.91  | 4000  | 5.5179          | 2.2232 |
| 5.381         | 6.65  | 4500  | 5.1420          | 2.1816 |
| 4.625         | 7.39  | 5000  | 3.9020          | 2.0722 |
| 4.214         | 8.12  | 5500  | 3.3394          | 2.1430 |
| 3.8992        | 8.86  | 6000  | 2.9085          | 2.1534 |
| 3.6481        | 9.6   | 6500  | 2.6208          | 2.3538 |
| 3.4658        | 10.34 | 7000  | 2.3172          | 2.2271 |
| 3.257         | 11.08 | 7500  | 2.0916          | 2.1351 |
| 3.1294        | 11.82 | 8000  | 1.8954          | 2.2133 |
| 3.0266        | 12.56 | 8500  | 1.7673          | 2.0896 |
| 2.9451        | 13.29 | 9000  | 1.6659          | 2.1381 |
| 2.8802        | 14.03 | 9500  | 1.5637          | 2.1969 |
| 2.78          | 14.77 | 10000 | 1.4921          | 2.2335 |
| 2.7049        | 15.51 | 10500 | 1.4132          | 2.2217 |
| 2.6768        | 16.25 | 11000 | 1.3667          | 2.2232 |
| 2.6358        | 16.99 | 11500 | 1.3111          | 2.1286 |
| 2.5802        | 17.72 | 12000 | 1.2679          | 2.1430 |
| 2.5012        | 18.46 | 12500 | 1.2365          | 2.1153 |
| 2.458         | 19.2  | 13000 | 1.2118          | 2.1573 |
| 2.4433        | 19.94 | 13500 | 1.1992          | 2.1336 |
| 2.438         | 20.68 | 14000 | 1.1803          | 2.1509 |
| 2.418         | 21.42 | 14500 | 1.1601          | 2.1232 |
| 2.3322        | 22.16 | 15000 | 1.1418          | 2.1930 |
| 2.3387        | 22.89 | 15500 | 1.1172          | 2.2464 |
| 2.3349        | 23.63 | 16000 | 1.1144          | 2.1856 |
| 2.291         | 24.37 | 16500 | 1.1018          | 2.1930 |
| 2.2766        | 25.11 | 17000 | 1.0883          | 2.1762 |
| 2.2534        | 25.85 | 17500 | 1.0744          | 2.1875 |
| 2.2393        | 26.59 | 18000 | 1.0561          | 2.1846 |
| 2.2085        | 27.33 | 18500 | 1.0466          | 2.1445 |
| 2.1966        | 28.06 | 19000 | 1.0382          | 2.1089 |
| 2.1794        | 28.8  | 19500 | 1.0264          | 1.9861 |
| 2.1423        | 29.54 | 20000 | 1.0246          | 1.9678 |
| 2.1649        | 30.28 | 20500 | 0.9982          | 2.0005 |
| 2.143         | 31.02 | 21000 | 0.9985          | 2.0450 |
| 2.1338        | 31.76 | 21500 | 0.9932          | 2.0025 |
| 2.1076        | 32.5  | 22000 | 0.9903          | 2.0505 |
| 2.0519        | 33.23 | 22500 | 0.9834          | 2.0737 |
| 2.0534        | 33.97 | 23000 | 0.9756          | 2.0247 |
| 2.0121        | 34.71 | 23500 | 0.9688          | 2.1440 |
| 2.0161        | 35.45 | 24000 | 0.9582          | 2.1232 |
| 2.0178        | 36.19 | 24500 | 0.9480          | 2.0896 |
| 2.0154        | 36.93 | 25000 | 0.9483          | 2.0787 |
| 1.9966        | 37.67 | 25500 | 0.9406          | 2.0297 |
| 1.9753        | 38.4  | 26000 | 0.9419          | 2.0346 |
| 1.9524        | 39.14 | 26500 | 0.9274          | 2.0698 |
| 1.9427        | 39.88 | 27000 | 0.9233          | 2.0787 |
| 1.9258        | 40.62 | 27500 | 0.9182          | 2.0529 |
| 1.9031        | 41.36 | 28000 | 0.9150          | 2.0787 |
| 1.9297        | 42.1  | 28500 | 0.9040          | 2.0505 |
| 1.9041        | 42.84 | 29000 | 0.9009          | 2.0579 |
| 1.8929        | 43.57 | 29500 | 0.8968          | 2.0327 |
| 1.9077        | 44.31 | 30000 | 0.8954          | 2.0619 |
| 1.8504        | 45.05 | 30500 | 0.8922          | 2.0737 |
| 1.8732        | 45.79 | 31000 | 0.8898          | 2.0683 |
| 1.877         | 46.53 | 31500 | 0.8849          | 2.0589 |
| 1.8587        | 47.27 | 32000 | 0.8843          | 2.0450 |
| 1.8236        | 48.01 | 32500 | 0.8810          | 2.0554 |
| 1.8392        | 48.74 | 33000 | 0.8820          | 2.0574 |
| 1.8428        | 49.48 | 33500 | 0.8816          | 2.0668 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


#### Evaluation Commands


1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`


```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-zh-CN --dataset mozilla-foundation/common_voice_7_0 --config zh-CN --split test
```


2. To evaluate on `speech-recognition-community-v2/dev_data`


```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-zh-CN --dataset speech-recognition-community-v2/dev_data --config zh-CN --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```


model name: wav2vec2-xls-r-300m-zh-CN


model author: samitizerxu


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, zh-CN, dataset:common_voice, transformers, license:apache-2.0, common_voice, generated_from_trainer, robust-speech-event, zh, model-index


language:


- lg
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- common_voice
- lg
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-lg
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: sv-SE
metrics:
    - name: Test WER
type: wer
value: 78.89
    - name: Test CER
type: cer
value: 15.16


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-xls-r-300m-lg


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - LG dataset.
It achieves the following results on the evaluation set:


- Loss: 0.6989
- Wer: 0.8529


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9089        | 6.33  | 500  | 2.8983          | 1.0002 |
| 2.5754        | 12.66 | 1000 | 1.8710          | 1.0    |
| 1.4093        | 18.99 | 1500 | 0.7195          | 0.8547 |


### Framework versions


#### Evaluation Commands


```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-lg --dataset mozilla-foundation/common_voice_7_0 --config lg --split test
```


model name: wav2vec2-xls-r-300m-lg


model author: samitizerxu


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, lg, dataset:common_voice, transformers, license:apache-2.0, robust-speech-event, common_voice, generated_from_trainer, model-index


language:


- es
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- es
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-es
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: es
metrics:
    - name: Test WER
type: wer
value: 37.37
    - name: Test CER
type: cer
value: 7.11
  # - task:
  # name: Automatic Speech Recognition
  # type: automatic-speech-recognition
  # dataset:
  # name: Robust Speech Event - Dev Data
  # type: speech-recognition-community-v2/dev_data
  # args: es
  # metrics:
  # - name: Test WER
  # type: wer
  # value:
  # - name: Test CER
  # type: cer
  # value:


# wav2vec2-cls-r-300m-es


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - ES dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5160
- Wer: 0.4016


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1277        | 1.14  | 500  | 2.0259          | 0.9999 |
| 1.4111        | 2.28  | 1000 | 1.1251          | 0.8894 |
| 0.8461        | 3.42  | 1500 | 0.8205          | 0.7244 |
| 0.5042        | 4.57  | 2000 | 0.6116          | 0.5463 |
| 0.3072        | 5.71  | 2500 | 0.5507          | 0.4506 |
| 0.2181        | 6.85  | 3000 | 0.5213          | 0.4177 |
| 0.1608        | 7.99  | 3500 | 0.5161          | 0.4019 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


#### Evaluation Commands


1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`


```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-es --dataset mozilla-foundation/common_voice_7_0 --config es --split test
```


2. To evaluate on `speech-recognition-community-v2/dev_data`


```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-es --dataset speech-recognition-community-v2/dev_data --config es --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```


model name: wav2vec2-xls-r-300m-es


model author: samitizerxu


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, es, dataset:common_voice, transformers, license:apache-2.0, common_voice, generated_from_trainer, robust-speech-event, model-index


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
    - name: Precision
type: precision
value: 0.9290544285555925
    - name: Recall
type: recall
value: 0.9375769101689228
    - name: F1
type: f1
value: 0.9332962138084633
    - name: Accuracy
type: accuracy
value: 0.9841136193940935


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# distilbert-base-uncased-finetuned-ner


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0604
- Precision: 0.9291
- Recall: 0.9376
- F1: 0.9333
- Accuracy: 0.9841


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2412        | 1.0   | 878  | 0.0688          | 0.9178    | 0.9246 | 0.9212 | 0.9815   |
| 0.0514        | 2.0   | 1756 | 0.0608          | 0.9251    | 0.9344 | 0.9298 | 0.9832   |
| 0.0304        | 3.0   | 2634 | 0.0604          | 0.9291    | 0.9376 | 0.9333 | 0.9841   |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-ner


model author: Fiddi


task: token-classification


tags: pytorch, tensorboard, distilbert, token-classification, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


language: en
tags:


- glossbert
license: mit
datasets:
- SemCor3.0


## GlossBERT


A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: '[GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/pdf/1908.07245.pdf)'


Disclaimer: This model was built and trained by a group of researchers different than the repository's author. The original model code can be found on github: https://github.com/HSLCY/GlossBERT


## Usage


The following code loads GlossBERT:


```py
from transformers import AutoTokenizer, BertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('kanishka/GlossBERT')
model = BertForSequenceClassification.from_pretrained('kanishka/GlossBERT')
```


## Citation


If you use this model in any of your projects, please cite the original authors using the following bibtex:


```
@inproceedings{huang-etal-2019-glossbert,
    title = "{G}loss{BERT}: {BERT} for Word Sense Disambiguation with Gloss Knowledge",
    author = "Huang, Luyao  and
      Sun, Chi  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1355",
    doi = "10.18653/v1/D19-1355",
    pages = "3507--3512"
}
```


model name: GlossBERT


model author: kanishka


tags: pytorch, bert, en, dataset:SemCor3.0, arxiv:1908.07245, transformers, glossbert, license:mit, infinity_compatible


language: it
widget:


- text: "Quando nacque D'Annunzio?"
context: "D'Annunzio nacque nel 1863"


# Italian Bert Base Uncased on Squad-it


## Model description


This model is the uncased base version of the italian BERT (which you may find at `dbmdz/bert-base-italian-uncased`) trained on the question answering task.


#### How to use


```python
from transformers import pipeline

nlp = pipeline('question-answering', model='antoniocappiello/bert-base-italian-uncased-squad-it')

# nlp(context="D'Annunzio nacque nel 1863", question="Quando nacque D'Annunzio?")
# {'score': 0.9990354180335999, 'start': 22, 'end': 25, 'answer': '1863'}
```


## Training data


It has been trained on the question answering task using [SQuAD-it](http://sag.art.uniroma2.it/demo-software/squadit/), derived from the original SQuAD dataset and obtained through the semi-automatic translation of the SQuAD dataset in Italian.


## Training procedure


```bash
python ./examples/run_squad.py \
    --model_type bert \
    --model_name_or_path dbmdz/bert-base-italian-uncased \
    --do_train \
    --do_eval \
    --train_file ./squad_it_uncased/train-v1.1.json \
    --predict_file ./squad_it_uncased/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./models/bert-base-italian-uncased-squad-it/ \
    --per_gpu_eval_batch_size=3   \
    --per_gpu_train_batch_size=3   \
    --do_lower_case \
```


## Eval Results


| Metric | # Value   |
| ------ | --------- |
| **EM** | **63.8** |
| **F1** | **75.30** |


## Comparison


| Model                                                                                | EM        | F1 score  |
| -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- |
| [DrQA-it trained on SQuAD-it](https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it)  | 56.1      | 65.9      |
| This one                                                                                | **63.8** | **75.30** |


model name: bert-base-italian-uncased-squad-it


model author: antoniocappiello


task: question-answering


tags: pytorch, question-answering, it, transformers, autonlp_compatible


language:


- en
datasets:
- imdb
metrics:
- accuracy


# bert-imdb-1hidden


## Model description


A `bert-base-uncased` model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the `datasets` library.


## Intended uses & limitations


#### How to use


```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

pretrained = "lannelin/bert-imdb-1hidden"

tokenizer = AutoTokenizer.from_pretrained(pretrained)

model = AutoModelForSequenceClassification.from_pretrained(pretrained)

LABELS = ["negative", "positive"]

def get_sentiment(text: str):
    inputs = tokenizer.encode_plus(text, return_tensors='pt')

    output = model(**inputs)[0].squeeze()

    return LABELS[(output.argmax())]

print(get_sentiment("What a terrible film!"))
```


#### Limitations and bias


No special consideration given to limitations and bias.


Any bias held by the imdb dataset may be reflected in the model's output.


## Training data


Initialised with [bert-base-uncased](https://huggingface.co/bert-base-uncased)


Fine tuned on [imdb](https://huggingface.co/datasets/imdb)


## Training procedure


The model was fine-tuned for 1 epoch with a batch size of 64,
a learning rate of 5e-5, and a maximum sequence length of 512.


## Eval results


Accuracy on imdb test set: 0.87132


model name: bert-imdb-1hidden


model author: lannelin


task: text-classification


tags: pytorch, jax, bert, text-classification, en, dataset:imdb, transformers, infinity_compatible


## Hugging Face's logo


language: rw
datasets:


# bert-base-multilingual-cased-finetuned-kinyarwanda


## Model description


**bert-base-multilingual-cased-finetuned-kinyarwanda** is a **Kinyarwanda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Kinyarwanda language texts.  It provides **better performance** than the multilingual BERT on named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda')
>>> unmasker("Twabonye ko igihe mu [MASK] hazaba hari ikirango abantu bakunze")

```


#### 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.


## Training data


This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | rw_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 72.20 | 77.57


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-kinyarwanda


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, rw, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: wo
datasets:


# xlm-roberta-base-finetuned-wolof


## Model description


**xlm-roberta-base-finetuned-luganda** is a **Wolof RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Wolof language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Wolof corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-wolof')
>>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal <mask> ak Afrik.")

```


#### Limitations and bias


## Training data


This model was fine-tuned on [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)


## Training procedure


Dataset| XLM-R F1 | wo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 63.86 | 68.31


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-wolof


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, wo, transformers, autonlp_compatible


## Hugging Face's logo


language: wo
datasets:


# bert-base-multilingual-cased-finetuned-wolof


## Model description


**bert-base-multilingual-cased-finetuned-wolof** is a **Wolof BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Wolof language texts.  It provides **better performance** than the multilingual BERT on named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Wolof corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-wolof')
>>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal [MASK] ak Afrik.")

```


#### 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.


## Training data


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | wo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 64.52 | 69.43


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-wolof


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, wo, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: luo
datasets:


# bert-base-multilingual-cased-finetuned-luo


## Model description


**bert-base-multilingual-cased-finetuned-luo** is a **Luo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luo language texts.  It provides **better performance** than the multilingual BERT on named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luo')
>>> unmasker("Obila ma Changamwe [MASK] pedho achije angwen mag njore")

```


#### Limitations and bias


## Training data


This model was fine-tuned on JW300


## Training procedure


Dataset| mBERT F1 | luo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.22 | 75.59


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-luo


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, luo, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language:


- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual


datasets:


- masakhaner


# bert-base-multilingual-cased-masakhaner


## Model description


**bert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned  mBERT base model.  It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```


#### 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.


## Training data


This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.


## Eval results on Test set (F-score)


language|F1-score
-|-
hau |88.66
ibo |85.72
kin |71.94
lug |81.73
luo |77.39
pcm |88.96
swa |88.23
wol |66.27
yor |80.09


### BibTeX entry and citation info


```
@article{adelani21tacl,
    title = {Masakha{NER}: Named Entity Recognition for African Languages},
    author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    month = {},
    url = {https://arxiv.org/abs/2103.11811},
    year = {2021}
}
```


model name: bert-base-multilingual-cased-masakhaner


model author: Davlan


task: token-classification


tags: pytorch, bert, token-classification, ha, ig, rw, lg, luo, pcm, sw, wo, yo, multilingual, dataset:masakhaner, arxiv:2103.11811, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: am
datasets:


# bert-base-multilingual-cased-finetuned-amharic


## Model description


**bert-base-multilingual-cased-finetuned-amharic** is a **Amharic BERT** model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning **bert-base-multilingual-cased** model on Amharic language texts.  It provides **better performance** than the multilingual Amharic on named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Amharic corpus using Amharic vocabulary.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-amharic')
>>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን [MASK] መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።")

```


#### Limitations and bias


## Training data


This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | am_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 0.0 | 60.89


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-amharic


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, am, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language:


- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual


datasets:


- masakhaner


# bert-base-multilingual-cased-masakhaner


## Model description


**distilbert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned  BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```


#### 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.


## Training data


This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.


## Eval results on Test set (F-score)


language|F1-score
-|-
hau |88.88
ibo |84.87
kin |74.19
lug |78.43
luo |73.32
pcm |87.98
swa |86.20
wol |64.67
yor |78.10


### BibTeX entry and citation info


```
@article{adelani21tacl,
    title = {Masakha{NER}: Named Entity Recognition for African Languages},
    author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    month = {},
    url = {https://arxiv.org/abs/2103.11811},
    year = {2021}
}
```


model name: distilbert-base-multilingual-cased-masakhaner


model author: Davlan


task: token-classification


tags: pytorch, distilbert, token-classification, ha, ig, rw, lg, luo, pcm, sw, wo, yo, multilingual, dataset:masakhaner, arxiv:2103.11811, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# byt5-base-yor-eng-mt


## Model description


**byt5-base-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned  byt5-base  model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.


Specifically, this model is a *byt5-base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


#### Limitations and bias


This model is limited by its training dataset. This may not generalize well for all use cases in different domains.


## Training data


This model was fine-tuned on on  JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


Fine-tuning byt5-base achieves 14.05 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57


### BibTeX entry and citation info


By David Adelani


model name: byt5-base-yor-eng-mt


model author: Davlan


task: text2text-generation


tags: pytorch, t5, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language: ig
datasets:


# bert-base-multilingual-cased-finetuned-igbo


## Model description


**bert-base-multilingual-cased-finetuned-igbo** is a **Igbo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Igbo language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Igbo corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-igbo')
>>> unmasker("Reno Omokri na Gọọmentị [MASK] enweghị ihe ha ga-eji hiwe ya bụ mmachi.")

```


#### 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.


## Training data


This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | ig_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 85.11 | 86.75


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-igbo


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, ig, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language:


- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual


# xlm-roberta-base-ner-hrl


## Model description


**xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```


#### Limitations and bias


## Training data


The training data for the 10 languages are from:


Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.


model name: xlm-roberta-base-ner-hrl


model author: Davlan


task: token-classification


tags: pytorch, xlm-roberta, token-classification, ar, de, en, es, fr, it, lv, nl, pt, zh, multilingual, transformers, autonlp_compatible


## Hugging Face's logo


language: pcm
datasets:


# xlm-roberta-base-finetuned-naija


## Model description


**xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin  corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-naija')
>>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver")

```


#### 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.


## Training data


This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| XLM-R F1 | pcm_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-naija


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, pcm, transformers, autonlp_compatible


## Hugging Face's logo


language: rw
datasets:


# xlm-roberta-base-finetuned-kinyarwanda


## Model description


**xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-kinyarwanda')
>>> unmasker("Twabonye ko igihe mu <mask> hazaba hari ikirango abantu bakunze")

```


#### Limitations and bias


## Training data


This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)


## Training procedure


Dataset| XLM-R F1 | rw_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 73.22 | 77.76


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-kinyarwanda


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, rw, transformers, autonlp_compatible


## Hugging Face's logo


language: lg
datasets:


# xlm-roberta-base-finetuned-luganda


## Model description


**xlm-roberta-base-finetuned-luganda** is a **Luganda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luganda language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luganda')
>>> unmasker("Ffe tulwanyisa abo abaagala okutabangula <mask>, Kimuli bwe yategeezezza.")

```


#### 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.


## Training data


This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| XLM-R F1 | lg_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 79.69 | 84.70


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-luganda


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, lg, transformers, autonlp_compatible


## Hugging Face's logo


language: lg
datasets:


# bert-base-multilingual-cased-finetuned-luganda


## Model description


**bert-base-multilingual-cased-finetuned-luganda** is a **Luganda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luganda language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luganda')
>>> unmasker("Ffe tulwanyisa abo abaagala okutabangula [MASK], Kimuli bwe yategeezezza.")

```


#### Limitations and bias


## Training data


## Training procedure


Dataset| mBERT F1 | lg_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 80.36 | 84.70


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-luganda


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, lg, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: luo
datasets:


# xlm-roberta-base-finetuned-luo


## Model description


**xlm-roberta-base-finetuned-luo** is a **Luo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luo language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luo corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luo')
>>> unmasker("Obila ma Changamwe <mask> pedho achije angwen mag njore")

```


#### 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.


## Training data


This model was fine-tuned on JW300


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| XLM-R F1 | luo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.86 | 75.27


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-luo


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, luo, transformers, autonlp_compatible


## Hugging Face's logo


language: ha
datasets:


# xlm-roberta-base-finetuned-hausa


## Model description


**xlm-roberta-base-finetuned-hausa** is a **Hausa RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Hausa corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa')
>>> unmasker("Shugaban <mask> Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")

[{'sequence': '<s> Shugaban kasa Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>',
'score': 0.8104371428489685,
'token': 29762,
'token_str': '▁kasa'},
{'sequence': '<s> Shugaban Najeriya Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.17371904850006104,
'token': 49173,
'token_str': '▁Najeriya'},
{'sequence': '<s> Shugaban kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.006917025428265333,
'token': 21221,
'token_str': '▁kasar'},
{'sequence': '<s> Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.005785710643976927,
'token': 72620,
'token_str': '▁Nigeria'},
{'sequence': '<s> Shugaban Kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.0010596115607768297,
'token': 170255,
'token_str': '▁Kasar'}]

```


#### Limitations and bias


## Training data


This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/)


## Training procedure


Dataset| XLM-R F1 | ha_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.10 | 91.47
[VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | |


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-hausa


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, ha, transformers, autonlp_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# m2m100_418M-eng-yor-mt


## Model description


**m2m100_418M-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned   facebook/m2m100_418M model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.


Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt).


#### Limitations and bias


This model is limited by its training dataset. This may not generalize well for all use cases in different domains.


## Training data


This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


Fine-tuning m2m100_418M achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82


### BibTeX entry and citation info


By David Adelani


model name: m2m100_418M-eng-yor-mt


model author: Davlan


task: text2text-generation


tags: pytorch, m2m_100, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language: pcm
datasets:


# bert-base-multilingual-cased-finetuned-naija


## Model description


**bert-base-multilingual-cased-finetuned-naija** is a **Nigerian-Pidgin BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Nigerian-Pidgin language texts.  It provides **better performance** than the multilingual BERT on named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Nigerian-Pidgin corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-naija')
>>> unmasker("Another attack on ambulance happen for Koforidua in March [MASK] year where robbers kill Ambulance driver")

```


#### 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.


## Training data


This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | pcm_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.23 | 89.95


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-naija


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, pcm, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# mT5_base_eng_yor_mt


## Model description


**mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.


Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


## Intended uses & limitations


#### How to use


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


```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```


#### Limitations and bias


## Training data


This model was fine-tuned on on  JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


## Eval results on Test set (BLEU score)


9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)


### BibTeX entry and citation info


By David Adelani


model name: mt5_base_eng_yor_mt


model author: Davlan


task: text2text-generation


tags: pytorch, mt5, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language:


# byt5-base-eng-yor-mt


## Model description


**byt5-base-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned  byt5-base  model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.


Specifically, this model is a *byt5-base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


#### Limitations and bias


This model is limited by its training dataset. This may not generalize well for all use cases in different domains.


## Training data


## Training procedure


This model was trained on NVIDIA V100 GPU


Fine-tuning byt5-base achieves **12.23 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82


### BibTeX entry and citation info


By David Adelani


model name: byt5-base-eng-yor-mt


model author: Davlan


task: text2text-generation


tags: pytorch, t5, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language:


- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual


# xlm-roberta-large-ner-hrl


## Model description


**xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```


#### 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.


## Training data


The training data for the 10 languages are from:


Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.


model name: xlm-roberta-large-ner-hrl


model author: Davlan


task: token-classification


tags: pytorch, xlm-roberta, token-classification, ar, de, en, es, fr, it, lv, nl, pt, zh, multilingual, transformers, autonlp_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# mbart50-large-eng-yor-mt


## Model description


**mbart50-large-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.


Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.


#### Limitations and bias


This model is limited by its training dataset. This may not generalize well for all use cases in different domains.


## Training data


This model was fine-tuned on on  JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


Fine-tuning mbarr50-large achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82


### BibTeX entry and citation info


By David Adelani


model name: mbart50-large-eng-yor-mt


model author: Davlan


task: text2text-generation


tags: pytorch, mbart, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language:


# mbart50-large-yor-eng-mt


## Model description


**mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.


#### Limitations and bias


## Training data


## Training procedure


Fine-tuning mbart50-large achieves **15.88 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57


model name: mbart50-large-yor-eng-mt


- amh
- hau
- ibo
- kin
- lug
- luo
- pcm
- swa
- wol
- yor
- multilingual


datasets:


- masakhaner


# xlm-roberta-large-masakhaner


**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned  XLM-RoBERTa large model.  It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```


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.


This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.


## Eval results on Test set (F-score)


language|F1-score
-|-
amh |75.76
hau |91.75
ibo |86.26
kin |76.38
lug |84.64
luo |80.65
pcm |89.55
swa |89.48
wol |70.70
yor |82.05


### BibTeX entry and citation info


```
@article{adelani21tacl,
    title = {Masakha{NER}: Named Entity Recognition for African Languages},
    author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    month = {},
    url = {https://arxiv.org/abs/2103.11811},
    year = {2021}
}
```


model name: xlm-roberta-large-masakhaner


model author: Davlan


task: token-classification


tags: pytorch, xlm-roberta, token-classification, amh, hau, ibo, kin, lug, luo, pcm, swa, wol, yor, multilingual, dataset:masakhaner, arxiv:2103.11811, transformers, autonlp_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# m2m100_418M-eng-yor-mt


## Model description


**m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned   facebook/m2m100_418M model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.


Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt).


#### Limitations and bias


This model is limited by its training dataset. This may not generalize well for all use cases in different domains.


## Training data


This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57


### BibTeX entry and citation info


By David Adelani


model name: m2m100_418M-yor-eng-mt


model author: Davlan


task: text2text-generation


tags: pytorch, m2m_100, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language: yo
datasets:


# xlm-roberta-base-finetuned-yoruba


## Model description


**xlm-roberta-base-finetuned-yoruba** is a **Yoruba RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Yorùbá language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-yoruba')
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ <mask> Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")

[{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.24844281375408173,
'token': 44109,
'token_str': '▁Queen'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ile Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.1665010154247284,
'token': 1350,
'token_str': '▁ile'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.07604238390922546,
'token': 1053,
'token_str': '▁ti'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ baba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.06353845447301865,
'token': 12878,
'token_str': '▁baba'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Oba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.03836742788553238,
'token': 82879,
'token_str': '▁Oba'}]

```


#### 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.


## Training data


This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| XLM-R F1 | yo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 77.58 | 83.66
[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) |  |


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-yoruba


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, yo, transformers, autonlp_compatible


## Hugging Face's logo


language:


- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual


# bert-base-multilingual-cased-ner-hrl


## Model description


**bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned  mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```


#### Limitations and bias


## Training data


The training data for the 10 languages are from:


Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.


model name: bert-base-multilingual-cased-ner-hrl


model author: Davlan


task: token-classification


tags: pytorch, bert, token-classification, ar, de, en, es, fr, it, lv, nl, pt, zh, multilingual, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: ha
datasets:


# bert-base-multilingual-cased-finetuned-hausa


## Model description


**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-hausa')
>>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")

[{'sequence':
'[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]',
'score': 0.9762618541717529,
'token': 22045,
'token_str': 'Nigeria'},
{'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181,
'token': 25444,
'token_str': 'Ka'},
{'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194,
'token': 117,
'token_str': ','},
{'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017,
'token': 28682,
'token_str': 'Ghana'},
{'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171,
'token': 11717,
'token_str': '##mu'}]

```


#### 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.


## Training data


This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | ha_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.65 | 91.31
[VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | 84.76 | 90.98


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-hausa


model author: Davlan


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ha, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: ig
datasets:


# xlm-roberta-base-finetuned-igbo


## Model description


**xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-igbo')
>>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.")

```


#### Limitations and bias


## Training data


This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/)


## Training procedure


Dataset| XLM-R F1 | ig_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 87.74


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-igbo


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, ig, transformers, autonlp_compatible


## Hugging Face's logo


language: yo
datasets:


- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# mT5_base_yoruba_adr


## Model description


**mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.


Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("")
model = AutoModelForTokenClassification.from_pretrained("")
nlp = pipeline("", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```


#### 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.


## Training data


This model was fine-tuned on on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564)
70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)


### BibTeX entry and citation info


By Jesujoba Alabi and David Adelani


model name: mT5_base_yoruba_adr


model author: Davlan


task: text2text-generation


tags: pytorch, mt5, text2text-generation, yo, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2003.10564, arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language:


- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual


# distilbert-base-multilingual-cased-ner-hrl


## Model description


**distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned  Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages


## Intended uses & limitations


#### How to use


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


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```


#### Limitations and bias


## Training data


The training data for the 10 languages are from:


Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)


The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location


## Training procedure


This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.


model name: distilbert-base-multilingual-cased-ner-hrl


model author: Davlan


task: token-classification


tags: pytorch, distilbert, token-classification, ar, de, en, es, fr, it, lv, nl, pt, zh, multilingual, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: ha
datasets:


# bert-base-multilingual-cased-finetuned-swahili


## Model description


**bert-base-multilingual-cased-finetuned-swahili** is a **Swahili BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Swahili language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa")

[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.31642526388168335,
'token': 10728,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa',
'score': 0.15753623843193054,
'token': 57557,
'token_str': 'Rwanda'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa',
'score': 0.07211585342884064,
'token': 57824,
'token_str': 'Burundi'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.029844321310520172,
'token': 10688,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa',
'score': 0.0265930388122797,
'token': 38052,
'token_str': 'Senegal'}]

```


#### 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.


## Training data


This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| mBERT F1 | sw_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.80 | 89.36


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-swahili


model author: Davlan


task: fill-mask


tags: pytorch, bert, fill-mask, ha, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: yo
datasets:


# bert-base-multilingual-cased-finetuned-yoruba


## Model description


**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.


Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")

[{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746,
'token': 12176,
'token_str': 'Mary'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092,
'token': 13704,
'token_str': 'Queen'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615,
'token': 14382,
'token_str': 'ti'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525,
'token': 11515,
'token_str': 'King'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962,
'token': 14005,
'token_str': 'Lady'}]

```


#### Limitations and bias


## Training data


This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.


## Training procedure


Dataset| mBERT F1 | yo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 82.58
[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11


### BibTeX entry and citation info


By David Adelani


model name: bert-base-multilingual-cased-finetuned-yoruba


model author: Davlan


task: fill-mask


tags: pytorch, jax, bert, fill-mask, yo, transformers, autonlp_compatible, infinity_compatible


## Hugging Face's logo


language: sw
datasets:


# xlm-roberta-base-finetuned-swahili


## Model description


**xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus.


## Intended uses & limitations


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa")

[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa',
'score': 0.5077782273292542,
'token': 190096,
'token_str': 'Ufaransa'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.3657738268375397,
'token': 7270,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa',
'score': 0.01592041552066803,
'token': 176392,
'token_str': 'Gabon'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.010881908237934113,
'token': 9942,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa',
'score': 0.009554869495332241,
'token': 185918,
'token_str': 'Marseille'}]

```


#### 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.


## Training data


This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/)


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (F-score, average over 5 runs)


Dataset| XLM-R F1 | sw_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.55 | 89.46


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-swahili


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, sw, transformers, autonlp_compatible


## Hugging Face's logo


language: am
datasets:


# xlm-roberta-base-finetuned-amharic


## Model description


**xlm-roberta-base-finetuned-amharic** is a **Amharic RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Amharic language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Amharic corpus.


## Intended uses & limitations


#### How to use


```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa')
>>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን <mask> መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።")

```


#### Limitations and bias


## Training data


This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/)


## Training procedure


Dataset| XLM-R F1 | am_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 70.96 | 77.97


### BibTeX entry and citation info


By David Adelani


model name: xlm-roberta-base-finetuned-amharic


model author: Davlan


task: fill-mask


tags: pytorch, xlm-roberta, fill-mask, am, transformers, autonlp_compatible


## Hugging Face's logo


language:


- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


# mT5_base_yor_eng_mt


## Model description


**mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned  mT5-base  model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.


Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)


## Intended uses & limitations


#### How to use


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


```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```


#### 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.


## Training data


This model was fine-tuned on on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset


## Training procedure


This model was trained on a single NVIDIA V100 GPU


## Eval results on Test set (BLEU score)


15.57 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)


### BibTeX entry and citation info


By David Adelani


model name: mt5_base_yor_eng_mt


model author: Davlan


task: text2text-generation


tags: pytorch, mt5, text2text-generation, yo, en, dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), arxiv:2103.08647, transformers, autonlp_compatible


## Hugging Face's logo


language:


- hau
- ibo
- pcm
- yor
- multilingual


# naija-twitter-sentiment-afriberta-large


## Model description


**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned  castorini/afriberta_large large model.\
It achieves the **state-of-the-art performance** for the twitter sentiment classification task trained on the [NaijaSenti corpus](https://github.com/hausanlp/NaijaSenti).
The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from [NaijaSenti](https://github.com/hausanlp/NaijaSenti) dataset.


## Intended uses & limitations


#### How to use


You can use this model with Transformers for Sentiment Classification.


```python
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax

MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

text = "I like you"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)

id2label = {0:"positive", 1:"neutral", 2:"negative"}

ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
    l = id2label[ranking[i]]
    s = scores[ranking[i]]
    print(f"{i+1}) {l} {np.round(float(s), 4)}")
```


#### Limitations and bias


This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.


## Training procedure


This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the [original NaijaSenti paper](https://arxiv.org/abs/2201.08277).


## Eval results on Test set (F-score), average over 5 runs.


language|F1-score
-|-
hau |81.2
ibo |80.8
pcm |74.5
yor |80.4


### BibTeX entry and citation info


```
@inproceedings{Muhammad2022NaijaSentiAN,
  title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
  author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil},
  year={2022}
}
```


model name: naija-twitter-sentiment-afriberta-large


model author: Davlan


task: text-classification


tags: pytorch, xlm-roberta, text-classification, hau, ibo, pcm, yor, multilingual, arxiv:2201.08277, transformers


tags:


- conversational


# My Awesome Model


model name: newbrokiev2


model author: brokentx


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


# Kokkoro DialoGPT Model


model name: DialoGPT-medium-Kokkoro


model author: lulueve3


model name: DialoGPT-medium-Kokkoro2


## license: other


model name: maskrcnn


model author: srishtiganguly


tags: license:other


# Chat Boi


model name: gpt-neo-125M-splishy


model author: thefryingpan


tags: pytorch, gpt_neo, text-generation, transformers, conversational


model name: test1


model author: batterydata


task: question-answering


tags: pytorch, bert, question-answering, transformers, autonlp_compatible, infinity_compatible


model name: test4


task: text-classification