- WER: 0.4490281634272114
- CER: 0.12198285179047481


# Evaluation results on OpenSLR "test" with LM ngram (self-split 10%) (Running ./eval.py):


- WER: 0.32130107100357
- CER: 0.09345053678218891


# Note


- Since this dataset is small (4 hours of voice recording), we decided not to train that for too long to avoid overfitting and under-generalization.
- This model performs worse than its 300M-variant. Probably, we don't explore the hyper-parameter enough?


## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 75
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5671        | 5.47  | 400  | 12.0218         | 1.0    |
| 3.5159        | 10.95 | 800  | 10.6337         | 1.0    |
| 2.4543        | 16.43 | 1200 | 1.8256          | 0.9839 |
| 1.9437        | 21.91 | 1600 | 1.1237          | 0.9173 |
| 1.696         | 27.39 | 2000 | 0.8246          | 0.7700 |
| 1.5342        | 32.87 | 2400 | 0.6433          | 0.6594 |
| 1.4509        | 38.35 | 2800 | 0.5500          | 0.5787 |
| 1.3478        | 43.83 | 3200 | 0.5070          | 0.4907 |
| 1.3096        | 49.31 | 3600 | 0.4692          | 0.4726 |
| 1.2532        | 54.79 | 4000 | 0.4448          | 0.4479 |
| 1.2291        | 60.27 | 4400 | 0.4374          | 0.4366 |
| 1.196         | 65.75 | 4800 | 0.4314          | 0.4310 |
| 1.1862        | 71.23 | 5200 | 0.4239          | 0.4221 |


### Framework versions


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


model name: wav2vec2-xls-r-1b-khmer


model author: vitouphy


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, km, transformers, license:apache-2.0, openslr, robust-speech-event, generated_from_trainer, model-index


language:


- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ja
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Japanese
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
    - name: Test WER
type: wer
value: 54.05
    - name: Test CER
type: cer
value: 27.54
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
    - name: Validation WER
type: wer
value: 48.77
    - name: Validation CER
type: cer
value: 24.87


This model is for transcribing audio into Hiragana, one format of Japanese language.


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:


- Modify `eval.py` to suit the use case.
- Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).


It achieves the following results on the evaluation set:


- Loss: 0.7751
- Cer: 0.2227


# Evaluation results (Running ./eval.py):


| Model    | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data   |
|:--------:|:------:|:-------------------:|:------------------------------------------:|
| w/o LM   | WER    | 0.5964              | 0.5532                                     |
|          | CER    | 0.2944              | 0.2629                                     |
| w/  LM   | WER    | 0.5405              | 0.4877                                     |
|          | CER    | **0.2754**              | **0.2487**                                     |


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4081        | 1.6   | 500   | 4.0983          | 1.0    |
| 3.303         | 3.19  | 1000  | 3.3563          | 1.0    |
| 3.1538        | 4.79  | 1500  | 3.2066          | 0.9239 |
| 2.1526        | 6.39  | 2000  | 1.1597          | 0.3355 |
| 1.8726        | 7.98  | 2500  | 0.9023          | 0.2505 |
| 1.7817        | 9.58  | 3000  | 0.8219          | 0.2334 |
| 1.7488        | 11.18 | 3500  | 0.7915          | 0.2222 |
| 1.7039        | 12.78 | 4000  | 0.7751          | 0.2227 |
| Stop & Train  |       |       |                 |        |
| 1.6571        | 15.97 | 5000  | 0.6788          | 0.1685 |
| 1.520400      | 19.16 | 6000  | 0.6095          | 0.1409 |
| 1.448200      | 22.35 | 7000  | 0.5843          | 0.1430 |
| 1.385400      | 25.54 | 8000  | 0.5699          | 0.1263 |
| 1.354200      | 28.73 | 9000  | 0.5686          | 0.1219 |
| 1.331500      | 31.92 | 10000 | 0.5502          | 0.1144 |
| 1.290800      | 35.11 | 11000 | 0.5371          | 0.1140 |
| Stop & Train  |       |       |                 |        |
| 1.235200      | 38.30 | 12000 | 0.5394          | 0.1106 |


### Framework versions


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


model author: vitouphy


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, ja, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model-index


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: daniel_asr
results: []


<!-- 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. -->


# daniel_asr


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 0.4565
- Wer: 0.3423


## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4909        | 4.0   | 500  | 1.3485          | 0.8887 |
| 0.5887        | 8.0   | 1000 | 0.4957          | 0.4641 |
| 0.2207        | 12.0  | 1500 | 0.4621          | 0.3971 |
| 0.125         | 16.0  | 2000 | 0.4339          | 0.3756 |
| 0.0829        | 20.0  | 2500 | 0.4618          | 0.3613 |
| 0.0601        | 24.0  | 3000 | 0.4564          | 0.3535 |
| 0.0456        | 28.0  | 3500 | 0.4565          | 0.3423 |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


model name: daniel_asr


model author: danielbubiola


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, transformers, license:apache-2.0, generated_from_trainer, model-index


tags:


- generated_from_trainer
model-index:
- name: bangla_asr
results: []


# bangla_asr


This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 157.8652
- Wer: 0.4507


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2601.5363     | 7.46  | 500  | 259.6630        | 0.6863 |
| 417.7386      | 14.93 | 1000 | 156.6117        | 0.5275 |
| 262.9455      | 22.39 | 1500 | 155.0886        | 0.5006 |
| 178.7715      | 29.85 | 2000 | 155.1077        | 0.4840 |
| 132.448       | 37.31 | 2500 | 163.8623        | 0.4770 |
| 116.3943      | 44.78 | 3000 | 161.5531        | 0.4609 |
| 87.1653       | 52.24 | 3500 | 165.6857        | 0.4597 |
| 80.5606       | 59.7  | 4000 | 157.8652        | 0.4507 |


### Framework versions


model name: bangla_asr


model author: danielbubiola


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, transformers, generated_from_trainer, model-index


language:


- ar
- en
license: apache-2.0
datasets:
- 4Dialects
- MADAR
- CSCS
thumbnail: https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@images/image/preview
tags:
- flair
- token-classification
- sequence-tagger-model
- Dialectal Arabic
- Code-Switching
- Code-Mixing
metrics:
- f1
widget:
- text: "طلعوا جماعة الممانعة بالسياسة ما بيعرفوا ولا بالصحة بيعرفوا ولا حتى بالدين"
- text: "أعلم أن هذا يبدو غير عادل ، لكن لا يمكن أن يكون هناك ظلم"
- text: "أنا عارف أن الموضوع ده شكله مش عادل ، بس لا يمكن أن يكون فيه ظلم"


# Arabic Flair + fastText Part-of-Speech tagging Model (Egyptian and Levant)


Pretrained Part-of-Speech tagging model built on a joint corpus written in Egyptian and Levantine (Jordanian, Lebanese, Palestinian, Syrian) dialects with code-switching of Egyptian Arabic and English. The model is trained using [Flair](https://aclanthology.org/C18-1139/) (forward+backward)and [fastText](https://fasttext.cc) embeddings.


# Pretraining Corpora:


This sequence labeling model was pretrained on three corpora jointly:


1. [4 Dialects](https://huggingface.co/datasets/viewer/?dataset=arabic_pos_dialect)
A Dialectal Arabic Datasets containing four dialects of Arabic, Egyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR). Each dataset consists of a set of 350 manually segmented and PoS tagged tweets.
1. [UD South Levantine Arabic MADAR](https://universaldependencies.org/treebanks/ajp_madar/index.html)
A Dataset with 100 manually-annotated sentences taken from the [MADAR](https://camel.abudhabi.nyu.edu/madar/) (Multi-Arabic Dialect Applications and Resources) project by [Shorouq Zahra](mailto:shorouqjzahra@gmail.com).
1. Parts of the Cairo Students Code-Switch (CSCS) corpus developed for ["Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus"](https://aclanthology.org/L18-1601.pdf) by Hamed et al.


# Usage


```python
from flair.data import Sentence
from flair.models import SequenceTagger

tagger = SequenceTagger.load("megantosh/flair-arabic-dialects-codeswitch-egy-lev")
sentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')
tagger.predict(sentence)
for entity in sentence.get_spans('pos'):
    print(entity)
```


Due to the right-to-left in left-to-right context, some formatting errors might occur. and your code might appear like [this](https://ibb.co/ky20Lnq), (link accessed on 2020-10-27)


<!--# Example

# Tagset-->


# Scores & Tagset


<details>


| |precision |   recall | f1-score |  support|
|--|-----------|------|-------------|--------------|
|INTJ |    0.8182   | 0.9000    |0.8571    |    10|
|OUN   |  0.9009   | 0.9402    |0.9201      | 435|
|NUM    | 0.9524   | 0.8333   | 0.8889       | 24|
|ADJ     |0.8762   | 0.7603  |  0.8142      | 121|
|ADP     |0.9903    |0.9623 |   0.9761       |106|
| CCONJ |    0.9600   | 0.9730 |   0.9664 |       74|
|PROPN |    0.9333   | 0.9333  |  0.9333  |      15|
| ADV  |   0.9135   | 0.8051  |  0.8559   |    118|
|VERB   |  0.8852    | 0.9231 |   0.9038   |    117|
|PRON    | 0.9620    | 0.9465 |   0.9542    |   187|
|SCONJ |    0.8571   | 0.9474  |  0.9000      |  19|
|PART  |   0.9350   | 0.9791   | 0.9565       | 191|
| DET   |  0.9348    | 0.9149  |  0.9247 |       47|
|PUNCT    | 1.0000    | 1.0000  |  1.0000  |      35|
| AUX  |   0.9286    | 0.9811  |  0.9541   |     53|
|MENTION   |  0.9231   |  1.0000  |  0.9600    |    12|
|     V    | 0.8571   | 0.8780    | 0.8675     |   82|
| FUT-PART+V+PREP+PRON     |1.0000   | 0.0000   | 0.0000       |  1|
|  PROG-PART+V+PRON+PREP+PRON |     0.0000  |  1.0000  |  0.0000       |  0|
|ADJ+NSUFF |    0.6111   | 0.8462   | 0.7097 |       26|
|NOUN+NSUFF  |   0.8182   | 0.8438   | 0.8308  |      64|
|PREP+PRON   |  0.9565   | 0.9565   | 0.9565   |     23|
|                   PUNC    | 0.9941   | 1.0000   | 0.9971    |   169|
|                    EOS     |1.0000   | 1.0000   | 1.0000    |   70|
|             NOUN+PRON   |  0.6986   | 0.8500   | 0.7669      |  60|
|                V+PRON    | 0.7258   | 0.8036   | 0.7627       | 56|
|            PART+PRON    | 1.0000   | 0.9474   | 0.9730    |    19|
|          PROG-PART+V    | 0.8333   | 0.9302   | 0.8791 |       43|
|            DET+NOUN    | 0.9625   | 1.0000   | 0.9809  |      77|
|     NOUN+NSUFF+PRON    | 0.9091   | 0.7143   | 0.8000   |     14|
|     PROG-PART+V+PRON    | 0.7083   | 0.9444   | 0.8095    |    18|
|      PREP+NOUN+NSUFF    | 0.6667   | 0.4000   | 0.5000         5|
|     NOUN+NSUFF+NSUFF    | 1.0000   | 0.0000   | 0.0000 |        3|
|                CONJ    | 0.9722   | 1.0000   | 0.9859  |      35|
|        V+PRON+PRON    | 0.6364   | 0.5833   | 0.6087   |     12|
|           FOREIGN    | 0.6667   | 0.6667   | 0.6667    |     3|
|        PREP+NOUN    | 0.6316   | 0.7500  |  0.6857 |       16|
|  DET+NOUN+NSUFF    | 0.9000   | 0.9310  |  0.9153  |      29|
|  DET+ADJ+NSUFF    | 1.0000   | 0.5714  |  0.7273   |      7|
|     CONJ+PRON    | 1.0000   | 0.8750  |  0.9333     |    8|
|    NOUN+CASE    | 0.0000   | 0.0000  |  0.0000    |     2|
|     DET+ADJ    | 1.0000   | 0.6667  |  0.8000      |   6|
|       PREP    | 1.0000   | 0.9718  |  0.9857  |      71|
|  CONJ+FUT-PART+V    | 0.0000   | 0.0000  |  0.0000   |      1|
|            CONJ+V    | 0.6667   | 0.7500  |  0.7059    |     8|
|         FUT-PART    | 1.0000   | 1.0000  |  1.0000     |    2|
|             ADJ+PRON    | 1.0000   | 0.0000  |  0.0000      |   8|
|   CONJ+PREP+NOUN+PRON    | 1.0000   | 0.0000  |  0.0000       |  1|
|        CONJ+NOUN+PRON    | 0.3750   | 1.0000  |  0.5455      |   3|
|              PART+ADJ    | 1.0000   | 0.0000  |  0.0000       |  1|
|             PART+NOUN    | 0.5000   | 1.0000  |  0.6667        | 1|
|       CONJ+PREP+NOUN    | 1.0000   | 0.0000  |  0.0000       |  1|
|           CONJ+NOUN    | 0.7000   | 0.7778  |  0.7368  |       9|
|                URL    | 1.0000   | 1.0000   | 1.0000 |        3|
|     CONJ+FUT-PART    | 1.0000   | 0.0000   | 0.0000  |       1|
|       FUT-PART+V    | 0.8571   | 0.6000   | 0.7059   |     10|
|      PREP+NOUN+NSUFF+NSUFF    | 1.0000   | 0.0000    | 0.0000   |      1|
|                      HASH    | 1.0000   | 0.9412   | 0.9697     |   17|
|            ADJ+PREP+PRON    | 1.0000   | 0.0000   | 0.0000  |       3|
|          PREP+NOUN+PRON    | 0.0000   | 0.0000   | 0.0000   |      1|
|                   EMOT    | 1.0000   | 0.8889   | 0.9412    |    18|
|             CONJ+PREP    | 1.0000   | 0.7500   | 0.8571     |    4|
|  PREP+DET+NOUN+NSUFF    | 1.0000   | 0.7500   | 0.8571      |   4|
| PRON+DET+NOUN+NSUFF    | 0.0000   | 1.0000   | 0.0000       |  0|
|        V+PREP+PRON    | 1.0000   | 0.0000   | 0.0000        | 5|
|  V+PRON+PREP+PRON    | 0.0000   | 1.0000   | 0.0000         | 0|
|  CONJ+NOUN+NSUFF    | 0.5000   | 0.5000   | 0.5000 |        2|
|      V+NEG-PART    | 1.0000   | 0.0000   | 0.0000  |       2|
|  PREP+DET+NOUN    | 0.9091   | 1.0000   | 0.9524   |     10|
|        PREP+V    | 1.0000   | 0.0000   | 0.0000    |     2|
|    CONJ+PART    | 1.0000   | 0.7778   | 0.8750     |    9|
| CONJ+V+PRON    | 1.0000   | 1.0000   | 1.0000 |        5|
|    PROG-PART+V+PREP+PRON    | 1.0000   | 0.5000   | 0.6667  |       2|
|    PREP+NOUN+NSUFF+PRON    | 1.0000   | 1.0000   | 1.0000   |      1|
|               ADJ+CASE    | 1.0000   | 0.0000    | 0.0000   |      1|
|        PART+NOUN+PRON    | 1.0000   | 1.0000   | 1.0000     |    1|
|               PART+V    | 1.0000   | 0.0000  |  0.0000      |   3|
|         PART+V+PRON    | 0.0000   | 1.0000  |  0.0000       |  0|
|    FUT-PART+V+PRON    | 0.0000   | 1.0000  |  0.0000        | 0|
|FUT-PART+V+PRON+PRON    | 1.0000   | 0.0000  |  0.0000  |       1|
|     CONJ+PREP+PRON    | 1.0000   | 0.0000  |  0.0000   |      1|
|CONJ+V+PRON+PREP+PRON    | 1.0000   | 0.0000  |  0.0000    |     1|
|    CONJ+V+PREP+PRON    | 0.0000   | 1.0000  |  0.0000     |    0|
|CONJ+DET+NOUN+NSUFF    | 1.0000   | 0.0000  |  0.0000      |   1|
|     CONJ+DET+NOUN    | 0.6667   | 1.0000  |  0.8000    |     2|
| CONJ+PREP+DET+NOUN   |  1.0000  |  1.0000 |   1.0000  |       1|
|       PREP+PART    | 1.0000   | 0.0000  |  0.0000  |       2|
|      PART+V+PRON+NEG-PART    | 0.3333   | 0.3333  |  0.3333         | 3|
|          PART+V+NEG-PART    | 0.3333   | 0.5000  |  0.4000        | 2|
|      PART+PREP+NEG-PART    | 1.0000   | 1.0000  |  1.0000       |  3|
| PART+PROG-PART+V+NEG-PART    | 1.0000   | 0.3333   | 0.5000      |   3|
| PREP+DET+NOUN+NSUFF+PREP+PRON   |  1.0000  |  0.0000  |  0.0000    |     1|
|         PREP+PRON+DET+NOUN    | 0.0000   | 1.0000    | 0.0000   |      0|
|                PART+NSUFF    | 1.0000   | 0.0000    | 0.0000  |       1|
|    CONJ+PROG-PART+V+PRON    | 1.0000   | 1.0000   | 1.0000    |     1|
|          PART+PREP+PRON    | 1.0000   | 0.0000   | 0.0000   |      1|
|         CONJ+PART+PREP    | 1.0000   | 0.0000    | 0.0000        | 1|
|             NUM+NSUFF    | 0.6667   | 0.6667   | 0.6667        | 3|
| CONJ+PART+V+PRON+NEG-PART   |  1.0000  |  1.0000  |  1.0000      |   1|
|     PART+NOUN+NEG-PART    | 1.0000   | 1.0000   | 1.0000      |   1|
|        CONJ+ADJ+NSUFF     | 1.0000  |  0.0000  |  0.0000    |     1|
|             PREP+ADJ     | 1.0000  |  0.0000  |  0.0000   |      1|
|      ADJ+NSUFF+PRON     | 1.0000  |  0.0000  |  0.0000  |       2|
|   CONJ+PROG-PART+V    | 1.0000   | 0.0000   | 0.0000   |      1|
| CONJ+PART+PROG-PART+V+PREP+PRON+NEG-PART   |  1.0000  |  0.0000  |  0.0000 |        1|
|          CONJ+PART+PREP+PRON+NEG-PART    | 0.0000   | 1.0000   | 0.0000 |        0|
|                       PREP+PART+PRON    | 1.0000   | 0.0000   | 0.0000    |     1|
|                      CONJ+ADV+NSUFF    | 1.0000   | 0.0000    |0.0000   |      1|
|                           CONJ+ADV    | 0.0000   | 1.0000   | 0.0000  |       0|
|           PART+NOUN+PRON+NEG-PART    | 0.0000   | 1.0000  |  0.0000 |        0|
|                         CONJ+ADJ    | 1.0000   | 1.0000 |   1.0000 |         1|


</details>


- F-score (micro): 0.8974
- F-score (macro): 0.5188
- Accuracy (incl. no class): 0.901


Expand details below to show class scores for each tag. Note that tag compounds (a tag made for multiple agglutinated parts of speech) are considered as separate ones.


# Citation


*if you use this model in your work, please consider citing this work:*


```latex
@unpublished{MMHU21
author = "M. Megahed",
title = "Sequence Labeling Architectures in Diglossia",
note = "In review",
}
```


model name: flair-arabic-dialects-codeswitch-egy-lev


model author: megantosh


task: token-classification


tags: pytorch, ar, en, dataset:4Dialects, dataset:MADAR, dataset:CSCS, flair, license:apache-2.0, token-classification, sequence-tagger-model, Dialectal Arabic, Code-Switching, Code-Mixing


language: ar
license: apache-2.0
datasets:


- AQMAR
- ANERcorp
thumbnail: https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@images/image/preview
tags:
- flair
- Text Classification
- token-classification
- sequence-tagger-model
metrics:
- f1
widget:
- text: "اختارها خيري بشارة كممثلة، دون سابقة معرفة أو تجربة تمثيلية، لتقف بجانب فاتن حمامة في فيلم «يوم مر ويوم حلو» (1988) وهي ما زالت شابة لم تتخطَ عامها الثاني"


# Arabic NER Model for AQMAR dataset


Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings.


## Original Dataset:


- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)


## Results:


- F1-score (micro) 0.9323
- F1-score (macro) 0.9272


|      | True Posititves  | False Positives | False Negatives | Precision | Recall | class-F1 |
|------|-----|----|----|---------|--------|----------|
| LOC  | 164 | 7  | 13 | 0.9591  | 0.9266 | 0.9425   |
| MISC | 398 | 22 | 37 |  0.9476 | 0.9149 | 0.9310   |
| ORG  | 65  | 6  | 9  | 0.9155  | 0.8784 | 0.8966   |
| PER  | 199 | 13 | 13 | 0.9387  | 0.9387 | 0.9387   |


# Usage


```python
from flair.data import Sentence
from flair.models import SequenceTagger
import pyarabic.araby as araby
from icecream import ic

arTagger = SequenceTagger.load('megantosh/flair-arabic-MSA-aqmar')

sentence = Sentence('George Washington went to Washington .')
arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')

# predict NER tags
tagger.predict(sentence)
arTagger.predict(arSentence)

# print sentence with predicted tags
ic(sentence.to_tagged_string)
ic(arSentence.to_tagged_string)

```


# Example


see an example from a [similar NER model in Flair](https://huggingface.co/megantosh/flair-arabic-multi-ner)


# Model Configuration


```python
  (embeddings): StackedEmbeddings(
    (list_embedding_0): WordEmbeddings('ar')
    (list_embedding_1): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
    (list_embedding_2): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (embedding2nn): Linear(in_features=4396, out_features=4396, bias=True)
  (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True)
  (linear): Linear(in_features=512, out_features=14, bias=True)
  (beta): 1.0
  (weights): None
  (weight_tensor) None
)"
2021-03-31 22:19:50,654 ----------------------------------------------------------------------------------------------------
2021-03-31 22:19:50,654 Corpus: "Corpus: 3025 train + 336 dev + 373 test sentences"
2021-03-31 22:19:50,654 ----------------------------------------------------------------------------------------------------
2021-03-31 22:19:50,654 Parameters:
2021-03-31 22:19:50,654  - learning_rate: "0.3"
2021-03-31 22:19:50,654  - mini_batch_size: "48"
2021-03-31 22:19:50,654  - patience: "3"
2021-03-31 22:19:50,654  - anneal_factor: "0.5"
2021-03-31 22:19:50,654  - max_epochs: "150"
2021-03-31 22:19:50,654  - shuffle: "True"
2021-03-31 22:19:50,654  - train_with_dev: "False"
2021-03-31 22:19:50,654  - batch_growth_annealing: "False"
2021-03-31 22:19:50,655 ------------------------------------
```


Due to the right-to-left in left-to-right context, some formatting errors might occur. and your code might appear like [this](https://ibb.co/ky20Lnq), (link accessed on 2020-10-27)


# Citation


```latex
@unpublished{MMHU21
author = "M. Megahed",
title = "Sequence Labeling Architectures in Diglossia",
note = "In Review",
}
```


model name: flair-arabic-MSA-aqmar


model author: megantosh


task: token-classification


tags: pytorch, ar, dataset:AQMAR, dataset:ANERcorp, flair, license:apache-2.0, Text Classification, token-classification, sequence-tagger-model


language:


- ar
- en
license: apache-2.0
datasets:
- AQMAR
- ANERcorp
thumbnail: https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@images/image/preview
tags:
- flair
- Text Classification
- token-classification
- sequence-tagger-model
metrics:
- f1
widget:
- text: أعرف كل شيء عن جيجي
- text: ترتقي شريحة M1 Pro وشريحة M1 Max ببنية شريحة M1 المذهلة إلى مستويات جديدة، إذ تأتيان للمرة الأولى ببنية نظام متكامل في شريحة (SoC) إلى جهاز نوت بوك للمحترفين.
- text: "اختارها خيري بشارة كممثلة، دون سابقة معرفة أو تجربة تمثيلية، لتقف بجانب فاتن حمامة في فيلم «يوم مر ويوم حلو» (1988) وهي ما زالت شابة لم تتخطَ عامها الثاني"


# Arabic NER Model using Flair Embeddings


Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, starting from 0.225 and a batch size of 32 with GloVe and Flair forward and backward embeddings.


## Original Datasets:


- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
- [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp)


## Results:


- F1-score (micro) 0.8666
- F1-score (macro) 0.8488


|      | Named Entity Type    | True Posititves  | False Positives | False Negatives | Precision | Recall | class-F1 |
|------|-|----|----|----|-----------|--------|----------|
| LOC  | Location| 539 | 51 | 68 | 0.9136    | 0.8880 | 0.9006   |
| MISC | Miscellaneous|408 | 57 | 89 | 0.8774    | 0.8209 | 0.8482   |
| ORG  | Organisation|167 | 43 | 64 | 0.7952    | 0.7229 | 0.7574   |
| PER  | Person (no title)|501 | 65 | 60 | 0.8852    | 0.8930 | 0.8891   |


# Usage


```python
from flair.data import Sentence
from flair.models import SequenceTagger
import pyarabic.araby as araby
from icecream import ic

tagger = SequenceTagger.load("julien-c/flair-ner")
arTagger = SequenceTagger.load('megantosh/flair-arabic-multi-ner')

sentence = Sentence('George Washington went to Washington .')
arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')

# predict NER tags
tagger.predict(sentence)
arTagger.predict(arSentence)

# print sentence with predicted tags
ic(sentence.to_tagged_string)
ic(arSentence.to_tagged_string)

```


# Example


```bash
2021-07-07 14:30:59,649 loading file /Users/mega/.flair/models/flair-ner/f22eb997f66ae2eacad974121069abaefca5fe85fce71b49e527420ff45b9283.941c7c30b38aef8d8a4eb5c1b6dd7fe8583ff723fef457382589ad6a4e859cfc
2021-07-07 14:31:04,654 loading file /Users/mega/.flair/models/flair-arabic-multi-ner/c7af7ddef4fdcc681fcbe1f37719348afd2862b12aa1cfd4f3b93bd2d77282c7.242d030cb106124f7f9f6a88fb9af8e390f581d42eeca013367a86d585ee6dd6
ic| sentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "George Washington went to Washington ."   [− Tokens: 6  − Token-Labels: "George <B-PER> Washington <E-PER> went to Washington <S-LOC> ."]>
ic| arSentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة ."   [− Tokens: 11  − Token-Labels: "عمرو <B-PER> عادلي <I-PER> أستاذ للاقتصاد السياسي المساعد في الجامعة <B-ORG> الأمريكية <I-ORG> بالقاهرة <B-LOC> ."]>
ic| entity: <PER-span (1,2): "George Washington">
ic| entity: <LOC-span (5): "Washington">
ic| entity: <PER-span (1,2): "عمرو عادلي">
ic| entity: <ORG-span (8,9): "الجامعة الأمريكية">
ic| entity: <LOC-span (10): "بالقاهرة">
ic| sentence.to_dict(tag_type='ner'):
{"text":"عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .",
"labels":[],
{"entities":[{{{
               "text":"عمرو عادلي",
               "start_pos":0,
               "end_pos":10,
               "labels":[PER (0.9826)]},
            {"text":"الجامعة الأمريكية",
               "start_pos":45,
               "end_pos":62,
               "labels":[ORG (0.7679)]},
            {"text":"بالقاهرة",
               "start_pos":64,
               "end_pos":72,
               "labels":[LOC (0.8079)]}]}
"text":"George Washington went to Washington .",
"labels":[],
"entities":[{
           {"text":"George Washington",
            "start_pos":0,
            "end_pos":17,
            "labels":[PER (0.9968)]},
           {"text":"Washington""start_pos":26,
            "end_pos":36,
            "labels":[LOC (0.9994)]}}]}
```


# Model Configuration


```python
SequenceTagger(
  (embeddings): StackedEmbeddings(
    (list_embedding_0): WordEmbeddings('glove')
    (list_embedding_1): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
    (list_embedding_2): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
  (linear): Linear(in_features=512, out_features=15, bias=True)
  (beta): 1.0
  (weights): None
  (weight_tensor) None
```


# Citation


*if you use this model in your work, please consider citing this work:*


```latex
@unpublished{MMHU21
author = "M. Megahed",
title = "Sequence Labeling Architectures in Diglossia",
note = "In preparation",
}
```


model name: flair-arabic-multi-ner


model author: megantosh


task: token-classification


tags: pytorch, ar, en, dataset:AQMAR, dataset:ANERcorp, flair, license:apache-2.0, Text Classification, token-classification, sequence-tagger-model


tags:


- conversational


# harry potter DialogGPT Model


model name: DialogGPT_small_harrypotter


model author: logube


task: conversational


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


- image-classification
- pytorch
- huggingpics
metrics:
- accuracy


model-index:


- name: ice_cream
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.5166666507720947


# ice_cream


Autogenerated by HuggingPics🤗🖼


Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).


Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).


## Example Images


#### chocolate ice cream


![chocolate ice cream](images/chocolate_ice_cream.jpg)


#### vanilla ice cream


![vanilla ice cream](images/vanilla_ice_cream.jpg)


model name: ice_cream


model author: juanfiguera


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, transformers, huggingpics, model-index


基于有赞商品评论语料训练的bert模型。


model name: bert-product-comment-chinese


model author: youzanai


task: fill-mask


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


基于有赞商品标题语料训练的bert模型。


model name: bert-product-title-chinese


<!--
 * @Description:
 * @Version:
 * @Author: Hardy
 * @Date: 2022-02-09 15:13:53
 * @LastEditors: Hardy
 * @LastEditTime: 2022-02-09 16:59:01
-->


clip-product-title-chinese


## 基于有赞商品图片和标题语料训练的clip模型。


## Usage


使用模型前，请 git clone https://github.com/youzanai/trexpark.git


```python
import torch
from src.clip.clip import ClipProcesserChinese, ClipChineseModel
import requests
from PIL import Image

clip_processor = ClipProcesserChinese.from_pretrained('youzanai/clip-product-title-chinese')
model = ClipChineseModel.from_pretrained('youzanai/clip-product-title-chinese')

url = 'http://img.yzcdn.cn/upload_files/2015/04/21/0140dac4657f874f2acff9294b28088c.jpg'
img = Image.open(requests.get(url, stream=True).raw).convert('RGB')
imgs = [img]
texts = ['运动鞋', '红色连衣裙', '黑色连衣裙', '大衣', '文具']

f = clip_processor(texts, imgs, return_tensors='pt', truncation=True, padding=True)
del f['token_type_ids']
with torch.no_grad():
    out = model(**f)
logits_per_image, logits_per_text = out['logits_per_image'], out['logits_per_text']

print(logits_per_image.softmax(dim=-1).cpu().detach().numpy())

# 结果： [[1.1700666e-07 9.9948394e-01 5.1582896e-04 4.7687358e-11 6.9604440e-08]]
```


model name: clip-product-title-chinese


tags: pytorch, clip_chinese_model, transformers


language: fr
license: mit
datasets:


- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- flaubert-small
- cased


# FlauBERT: Unsupervised Language Model Pre-training for French


**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer.


Along with FlauBERT comes [**FLUE**](https://github.com/getalp/Flaubert/tree/master/flue): an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.For more details please refer to the [official website](https://github.com/getalp/Flaubert).


## FlauBERT models


| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------:       |   :---: | :---: | :---: | :---: |
| `flaubert-small-cased` | 6    | 8    | 512   | 54 M |
| `flaubert-base-uncased`  | 12  | 12  | 768  | 137 M |
| `flaubert-base-cased`   | 12   | 12      | 768   | 138 M |
| `flaubert-large-cased`  | 24   | 16     | 1024 | 373 M |


**Note:** `flaubert-small-cased` is partially trained so performance is not guaranteed. Consider using it for debugging purpose only.


## Using FlauBERT with Hugging Face's Transformers


```python
import torch
from transformers import FlaubertModel, FlaubertTokenizer

# Choose among ['flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased',
#               'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased']
modelname = 'flaubert/flaubert_base_cased'

# Load pretrained model and tokenizer
flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True)
flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False)
# do_lowercase=False if using cased models, True if using uncased ones

sentence = "Le chat mange une pomme."
token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)])

last_layer = flaubert(token_ids)[0]
print(last_layer.shape)
# torch.Size([1, 8, 768])  -> (batch size x number of tokens x embedding dimension)

# The BERT [CLS] token correspond to the first hidden state of the last layer
cls_embedding = last_layer[:, 0, :]
```


**Notes:** if your `transformers` version is <=2.10.0, `modelname` should take one
of the following values:


```
['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased']
```


## References


If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers:


[LREC paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.302.pdf)


```
@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```


[TALN paper](https://hal.archives-ouvertes.fr/hal-02784776/)


```
@inproceedings{le2020flaubert,
  title         = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais},
  author        = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier},
  booktitle     = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles},
  pages         = {268--278},
  year          = {2020},
  organization  = {ATALA}
}
```


model name: flaubert_small_cased


model author: flaubert


task: fill-mask


tags: pytorch, flaubert, fill-mask, fr, dataset:flaubert, transformers, license:mit, bert, language-model, flue, french, flaubert-small, cased, autonlp_compatible


language: fr
license: mit
datasets:


- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- bert-base
- flaubert-base
- cased


## FlauBERT models


model name: flaubert_base_cased


tags: pytorch, flaubert, fill-mask, fr, dataset:flaubert, transformers, license:mit, bert, language-model, flue, french, bert-base, flaubert-base, cased, autonlp_compatible


- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- bert-large
- flaubert-large
- cased


# FlauBERT: Unsupervised Language Model Pre-training for French


**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer.


Along with FlauBERT comes [**FLUE**](https://github.com/getalp/Flaubert/tree/master/flue): an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.For more details please refer to the [official website](https://github.com/getalp/Flaubert).


| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------:       |   :---: | :---: | :---: | :---: |
| `flaubert-small-cased` | 6    | 8    | 512   | 54 M |
| `flaubert-base-uncased`  | 12  | 12  | 768  | 137 M |
| `flaubert-base-cased`   | 12   | 12      | 768   | 138 M |
| `flaubert-large-cased`  | 24   | 16     | 1024 | 373 M |


**Note:** `flaubert-small-cased` is partially trained so performance is not guaranteed. Consider using it for debugging purpose only.


## Using FlauBERT with Hugging Face's Transformers


```python
import torch
from transformers import FlaubertModel, FlaubertTokenizer

# Choose among ['flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased',
#               'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased']
modelname = 'flaubert/flaubert_base_cased'

# Load pretrained model and tokenizer
flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True)
flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False)
# do_lowercase=False if using cased models, True if using uncased ones

sentence = "Le chat mange une pomme."
token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)])

last_layer = flaubert(token_ids)[0]
print(last_layer.shape)
# torch.Size([1, 8, 768])  -> (batch size x number of tokens x embedding dimension)

# The BERT [CLS] token correspond to the first hidden state of the last layer
cls_embedding = last_layer[:, 0, :]
```


**Notes:** if your `transformers` version is <=2.10.0, `modelname` should take one
of the following values:


```
['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased']
```


## References


If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers:


[LREC paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.302.pdf)


```
@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```


[TALN paper](https://hal.archives-ouvertes.fr/hal-02784776/)


```
@inproceedings{le2020flaubert,
  title         = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais},
  author        = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier},
  booktitle     = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles},
  pages         = {268--278},
  year          = {2020},
  organization  = {ATALA}
}
```


model name: flaubert_large_cased


model author: flaubert


task: fill-mask


tags: pytorch, flaubert, fill-mask, fr, dataset:flaubert, transformers, license:mit, bert, language-model, flue, french, bert-large, flaubert-large, cased, autonlp_compatible


language: fr
license: mit
datasets:


- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- flaubert-base
- uncased


## FlauBERT models


model name: flaubert_base_uncased


tags: pytorch, flaubert, fill-mask, fr, dataset:flaubert, transformers, license:mit, bert, language-model, flue, french, flaubert-base, uncased, autonlp_compatible


hello


model name: gpt2_french


model author: inspectorsolaris


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


license: apache-2.0
tags:


- image-classification
- generated_from_trainer
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: vit-base-cats-vs-dogs
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: cats_vs_dogs
type: cats_vs_dogs
args: default
metrics:
    - name: Accuracy
type: accuracy
value: 0.9883257403189066


<!-- 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. -->


# vit-base-cats-vs-dogs


This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0369
- Accuracy: 0.9883


## how to use


```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('akahana/vit-base-cats-vs-dogs')
inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```


## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0949        | 1.0   | 2488 | 0.0369          | 0.9883   |


### Framework versions


- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: vit-base-cats-vs-dogs


model author: akahana


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, dataset:cats_vs_dogs, transformers, license:apache-2.0, generated_from_trainer, model-index


language: id
tags:


- roberta-base-indonesia
license: mit
datasets:
- wikipedia
widget:
- text: "Gajah <mask> sedang makan di kebun binatang."


# Indonesian RoBERTa Base


## How to Use


### As Masked Language Model


```python
from transformers import pipeline

pretrained_name = "akahana/roberta-base-indonesia"

fill_mask = pipeline(
    "fill-mask",
    model=pretrained_name,
    tokenizer=pretrained_name
)

fill_mask("Gajah <mask> sedang makan di kebun binatang.")
```


### Feature Extraction in PyTorch


```python
from transformers import RobertaModel, RobertaTokenizerFast

pretrained_name = "akahana/roberta-base-indonesia"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)

prompt = "Gajah <mask> sedang makan di kebun binatang."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```


model name: roberta-base-indonesia


task: feature-extraction


tags: pytorch, tf, roberta, feature-extraction, id, dataset:wikipedia, transformers, roberta-base-indonesia, license:mit, infinity_compatible


language: "id"
widget:


- text: "dia orang yang baik ya bunds."


## how to use


```python
from transformers import pipeline, set_seed

path = "akahana/indonesia-emotion-roberta"
emotion = pipeline('text-classification',
                     model=path,device=0)
set_seed(42)

kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds

[{'label': 'BAHAGIA', 'score': 0.8790940046310425}]

```


model name: indonesia-emotion-roberta


model author: akahana


task: text-classification


tags: pytorch, tensorboard, roberta, text-classification, id, transformers, infinity_compatible


- text: "dahulu kala ada sebuah"


```python
from transformers import pipeline, set_seed

path = "akahana/gpt2-indonesia"
generator = pipeline('text-generation',
                     model=path)
set_seed(42)

kalimat = "dahulu kala ada sebuah"
preds = generator(kalimat,
                  max_length=64,
                  num_return_sequences=3)
for data in preds:
  print(data)

{'generated_text': 'dahulu kala ada sebuah perkampungan yang bernama pomere. namun kini kawasan ini sudah tidak dikembangkan lagi sebagai kawasan industri seperti perusahaan pupuk. sumber-sumber lain sudah sulit ditemukan karena belum adanya kilang pupuk milik indonesia yang sering di kembangkan sehingga belum ada satupun yang masih tersisa yang tersisa. kawasan ini juga memproduksi gula aren milik pt graha bina sarana'}
{'generated_text': 'dahulu kala ada sebuah desa kecil bernama desa. desa yang terkenal seperti halnya kota terdekat lainnya adalah desa tetangga yang bernama sama."\n"sebuah masjid merupakan suatu tempat suci yang digunakan umat islam untuk beribadah. beberapa masjid yang didaftarkan berikut memiliki suatu kehormatan tersendiri bagi masing-masing denominasi islam di dunia. sebuah masjid selain memiliki fungsi sebagai tempat'}
{'generated_text': 'dahulu kala ada sebuah peradaban yang dibangun di sebelah barat sungai mississippi di sekitar desa kecil desa yang bernama sama. penduduk asli di desa ini berasal dari etnis teweh yang berpindah agama menjadi kristen, namun kemudian pindah agama menjadi kristen. desa arawak mempunyai beberapa desa lain seperti adibei, deti, riuhut dan sa'}

```


model name: gpt2-indonesia


task: text-generation


tags: pytorch, tf, gpt2, text-generation, id, transformers


language: id
tags:


- tiny-roberta-indonesia
license: mit
datasets:
- wikipedia
widget:
- text: "ikiryo adalah <mask> hantu dalam mitologi jepang."


# Indonesian tiny-RoBERTa


## How to Use


### As Masked Language Model


```python
from transformers import pipeline

pretrained_name = "akahana/tiny-roberta-indonesia"

fill_mask = pipeline(
    "fill-mask",
    model=pretrained_name,
    tokenizer=pretrained_name
)

fill_mask("ikiryo adalah <mask> hantu dalam mitologi jepang.")
```


### Feature Extraction in PyTorch


```python
from transformers import RobertaModel, RobertaTokenizerFast

pretrained_name = "akahana/tiny-roberta-indonesia"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)

prompt = "ikiryo adalah <mask> hantu dalam mitologi jepang."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```


model name: tiny-roberta-indonesia


model author: akahana


task: feature-extraction


tags: pytorch, tf, roberta, feature-extraction, id, dataset:wikipedia, transformers, tiny-roberta-indonesia, license:mit, infinity_compatible


language: "id"
widget:


- text: "dia orang yang baik ya bunds."


## how to use


```python
from transformers import pipeline, set_seed

path = "akahana/indonesia-sentiment-roberta"
emotion = pipeline('text-classification',
                     model=path,device=0)
set_seed(42)

kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds
```


model name: indonesia-sentiment-roberta


task: text-classification


tags:


- conversational


# Ultron Small


model name: Ultron-Small


model author: Xeouz


task: conversational


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


language:


- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-fr
results: []


<!-- 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-fr


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
It achieves the following results on the evaluation set:


- Loss: inf
- Wer: 0.4103


## 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: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 20.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.591         | 1.28  | 500  | inf             | 0.9935 |
| 2.9126        | 2.56  | 1000 | inf             | 0.9935 |
| 1.9567        | 3.84  | 1500 | inf             | 0.8861 |
| 1.4769        | 5.13  | 2000 | inf             | 0.6826 |
| 1.2254        | 6.41  | 2500 | inf             | 0.5388 |
| 1.1349        | 7.69  | 3000 | inf             | 0.4978 |
| 1.0985        | 8.97  | 3500 | inf             | 0.4747 |
| 1.0618        | 10.26 | 4000 | inf             | 0.4578 |
| 1.0258        | 11.54 | 4500 | inf             | 0.4421 |
| 1.0007        | 12.82 | 5000 | inf             | 0.4326 |
| 1.0017        | 14.1  | 5500 | inf             | 0.4263 |
| 0.966         | 15.38 | 6000 | inf             | 0.4216 |
| 0.9604        | 16.67 | 6500 | inf             | 0.4157 |
| 0.9479        | 17.95 | 7000 | inf             | 0.4126 |
| 0.9319        | 19.23 | 7500 | inf             | 0.4106 |


### Framework versions


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


model name: wav2vec2-xls-fr


model author: Ilyes


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, fr, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, model-index


language: fr
datasets:


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French_punctuation by Ilyes Rebai
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
args: fr
metrics:
    - name: Test WER and CER on text and puctuation prediction
types: [wer, cer]
values:  [19.47%, 6.66%]
    - name: Test WER and CER on text without punctuation
types: [wer, cer]
values:  [17.88%, 6.37%]


## Evaluation on Common Voice FR Test


```python
import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
)

model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation"

model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
processor = Wav2Vec2Processor.from_pretrained(model_name)

ds = load_dataset("common_voice", "fr", split="test")

chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]'
def normalize_text(text):
    text = text.lower().strip()
    text = re.sub('œ', 'oe', text)
    text = re.sub('æ', 'ae', text)
    text = re.sub("’|´|′|ʼ|‘|ʻ|`", "'", text)
    text = re.sub("'+ ", " ", text)
    text = re.sub(" '+", " ", text)
    text = re.sub("'$", " ", text)
    text = re.sub("' ", " ", text)
    text = re.sub("−|‐", "-", text)
    text = re.sub(" -", "", text)
    text = re.sub("- ", "", text)
    text = re.sub(chars_to_ignore_regex, '', text)
    return text

def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    batch["sentence"] = normalize_text(batch["sentence"])
    return batch

ds = ds.map(map_to_array)

resampler = torchaudio.transforms.Resample(48_000, 16_000)
def map_to_pred(batch):
    features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["predicted"] = processor.batch_decode(pred_ids)
    batch["target"] = batch["sentence"]
    # remove duplicates
    batch["target"] = re.sub('\.+', '.', batch["target"])
    batch["target"] = re.sub('\?+', '?', batch["target"])
    batch["target"] = re.sub('!+', '!', batch["target"])
    batch["target"] = re.sub(',+', ',', batch["target"])
    return batch

result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```


## Some results


| Reference  | Prediction |
| ------------- | ------------- |
| il vécut à new york et y enseigna une grande partie de sa vie. | il a vécu à new york et y enseigna une grande partie de sa vie. |
| au classement par nations, l'allemagne est la tenante du titre. | au classement der nation l'allemagne est la tenante du titre. |
| voici un petit calcul pour fixer les idées. | voici un petit calcul pour fixer les idées. |
| oh! tu dois être beau avec | oh! tu dois être beau avec. |
| babochet vous le voulez? | baboche, vous le voulez? |
| la commission est, par conséquent, défavorable à cet amendement. | la commission est, par conséquent, défavorable à cet amendement. |


All the references and predictions of the test corpus are already available in this repository.


## Results


text + punctuation


WER=21.47% CER=7.21%


text (without punctuation)


WER=19.71% CER=6.91%


model name: wav2vec2-large-xlsr-53-french_punctuation


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, fr, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning, license:apache-2.0, model-index


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French by Ilyes Rebai
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fr
type: common_voice
args: fr
metrics:
    - name: Test WER
type: wer
value: 12.82


The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
)
import re

model_name = "Ilyes/wav2vec2-large-xlsr-53-french"

model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
processor = Wav2Vec2Processor.from_pretrained(model_name)

ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")

chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]'
def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
    return batch

ds = ds.map(map_to_array)

resampler = torchaudio.transforms.Resample(48_000, 16_000)
def map_to_pred(batch):
    features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["predicted"] = processor.batch_decode(pred_ids)
    batch["target"] = batch["sentence"]
    return batch

result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```


model name: wav2vec2-large-xlsr-53-french


model author: Ilyes


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, fr, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


language: fr
datasets:


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- BPE
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French-BPE by Ilyes Rebai
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fr
type: common_voice
args: fr
metrics:
    - name: Test WER
type: wer
value: 17.32
    - name: Test CER
type: cer
value: 7.03


The script used for training can be found here: https://github.com/irebai/wav2vec2


## Results


model name: wav2vec2-large-xlsr-53-french_BPE


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, fr, dataset:common_voice, transformers, audio, speech, BPE, xlsr-fine-tuning-week, license:apache-2.0, model-index


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-wikitext2
results:
  - task:
name: Causal Language Modeling
type: text-generation


<!-- 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. -->


# distilgpt2-finetuned-wikitext2


This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 3.6424


## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7598        | 1.0   | 2334 | 3.6654          |
| 3.6321        | 2.0   | 4668 | 3.6453          |
| 3.6076        | 3.0   | 7002 | 3.6424          |


### Framework versions


- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


model name: distilgpt2-finetuned-wikitext2


model author: supah-hakah


task: text-generation


tags: pytorch, tensorboard, gpt2, text-generation, transformers, license:apache-2.0, generated_from_trainer


tags:


- conversational


# Harry Potter DialoGPT Model


model name: HarryPotter


model author: shihab


task: conversational


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


# Fibruh Bot Model


model name: DialoGPT-small-harrypotter


model author: fibruh


Dummy model


model name: dummy


model author: KalyanM


demo file


model name: demo


- automatic-speech-recognition
- robust-speech-event


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


The following hyper-parameters were used during training:


- learning_rate: 3e-4
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 30
- mixed_precision_training: Native AMP


model name: wav2vec2-XLS-R-300m-konkani


model author: StephennFernandes


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, transformers


language:


- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- generated_from_trainer
model-index:
- name: XLS-R-marathi
results: []


# XLS-R-marathi


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1200
- num_epochs: 30.0
- mixed_precision_training: Native AMP


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3


model name: XLS-R-marathi


model author: StephennFernandes


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, mr, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, robust-speech-event, generated_from_trainer, model-index


This is a dummy readme


model name: XLS-R-assamese-LM


tags:


- conversational


# Loki GPT Dialog Bot


model name: LokiDiscordBot-medium


model author: bhaden94


task: conversational


- spacy
- token-classification
language:
- hu
license: cc-by-sa-4.0
model-index:
- name: hu_core_news_lg
results:
  - task:
name: NER
type: token-classification
metrics:
    - name: NER Precision
type: precision
value: 0.8543930456
    - name: NER Recall
type: recall
value: 0.8369829684
    - name: NER F Score
type: f_score
value: 0.8455984022
  - task:
name: TAG
type: token-classification
metrics:
    - name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9648308532
  - task:
name: POS
type: token-classification
metrics:
    - name: POS (UPOS) Accuracy
type: accuracy
value: 0.9652136466
  - task:
name: MORPH
type: token-classification
metrics:
    - name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9279356876
  - task:
name: LEMMA
type: token-classification
metrics:
    - name: Lemma Accuracy
type: accuracy
value: 0.9543584346
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8110496002
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7398792217
  - task:
name: SENTS
type: token-classification
metrics:
    - name: Sentences F-Score
type: f_score
value: 0.9754464286


Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner


| Feature | Description |
| --- | --- |
| **Name** | `hu_core_news_lg` |
| **Version** | `0.4.2` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
| **Vectors** | 1140008 keys, 1140008 unique vectors (300 dimensions) |
| **Sources** | [UD Hungarian Szeged](https://universaldependencies.org/treebanks/hu_szeged/index.html) (Richárd Farkas, Katalin Simkó, Zsolt Szántó, Viktor Varga, Veronika Vincze (MTA-SZTE Research Group on Artificial Intelligence))
[NYTK-NerKor corpus](https://github.com/nytud/NYTK-NerKor) (Eszter Simon, Noémi Vadász (Department of Language Technology and Applied Linguistics))
[hunNERwiki](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Eszter Simon, Dávid Márk Nemeskey (HLT Group, Budapest University of Technology and Economics))
[Szeged NER Corpus](https://rgai.inf.u-szeged.hu/node/130) (György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik (MTA-SZTE Research Group on Artificial Intelligence))
[Webcorpuswiki word2vec model](https://github.com/oroszgy/hunlp-resources/releases/tag/webcorpuswiki_word2vec_v0.1) (György Orosz) |
| **License** | `cc-by-sa-4.0` |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |


### Label Scheme


<details>


<summary>View label scheme (1215 labels for 5 components)</summary>