- name: roberta-base-ca-cased-sts
results:
  - task:
type: sentence-similarity
dataset:
type:   projecte-aina/sts-ca
name: sts-ca
metrics:
    - type: pearson
value: 0.8120486139447483


# Catalan BERTa (RoBERTa-base) finetuned for Semantic Textual Similarity.


The **roberta-base-ca-cased-sts** is a Semantic Textual Similarity (STS) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).


## Datasets


We used the STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and evaluation.


## Evaluation and results


We evaluated the *roberta-base-ca-cased-sts* on the STS-ca test set against standard multilingual and monolingual baselines:


| Model       | STS-ca (Pearson)   |
|:------------|:----|
| roberta-base-ca-cased-sts | **81.20** |
| mBERT       | 76.34 |
| XLM-RoBERTa | 75.40 |
| WikiBERT-ca | 77.18 |


For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/berta).


## How to use


To get the correct1 model's prediction scores with values between 0.0 and 5.0, use the following code:


```python
from transformers import pipeline, AutoTokenizer
from scipy.special import logit

model = 'projecte-aina/roberta-base-ca-cased-sts'
tokenizer = AutoTokenizer.from_pretrained(model)
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)

def prepare(sentence_pairs):
    sentence_pairs_prep = []
    for s1, s2 in sentence_pairs:
        sentence_pairs_prep.append(f"{tokenizer.cls_token} {s1}{tokenizer.sep_token}{tokenizer.sep_token} {s2}{tokenizer.sep_token}")
    return sentence_pairs_prep

sentence_pairs = [("El llibre va caure per la finestra.", "El llibre va sortir volant."),
                  ("M'agrades.", "T'estimo."),
                  ("M'agrada el sol i la calor", "A la Garrotxa plou molt.")]

predictions = pipe(prepare(sentence_pairs), add_special_tokens=False)

# convert back to scores to the original 1 and 5 interval
for prediction in predictions:
    prediction['score'] = logit(prediction['score'])
print(predictions)
```


Expected output:


```
[{'label': 'SIMILARITY', 'score': 2.4280577200108384},
{'label': 'SIMILARITY', 'score': 2.132843521240822},
{'label': 'SIMILARITY', 'score': 1.615101695426227}]
```


1 ***avoid using the widget** scores since they are normalized and do not reflect the original annotation values.*


## Citing


If you use any of these resources (datasets or models) in your work, please cite our latest paper:


```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
    author = "Armengol-Estap{\'e}, Jordi  and
      Carrino, Casimiro Pio  and
      Rodriguez-Penagos, Carlos  and
      de Gibert Bonet, Ona  and
      Armentano-Oller, Carme  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.437",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",
}
```


model name: roberta-base-ca-cased-sts


model author: projecte-aina


task: text-classification


tags: pytorch, roberta, text-classification, ca, dataset:projecte-aina/sts-ca, arxiv:1907.11692, transformers, license:???, catalan, semantic textual similarity, sts-ca, CaText, Catalan Textual Corpus, model-index, infinity_compatible


language:


- ca


license: ???


tags:


- "catalan"
- "named entity recognition"
- "ner"
- "CaText"
- "Catalan Textual Corpus"


datasets:


- "projecte-aina/ancora-ca-ner"


metrics:


- f1


model-index:


- name: roberta-base-ca-cased-te
results:
  - task:
type: token-classification
dataset:
type:   projecte-aina/ancora-ca-ner
name: ancora-ca-ner
metrics:
    - type: f1
value: 0.8963029756537422


widget:


- text: "Em dic Lluïsa i visc a Santa Maria del Camí."
- text: "L'Aina, la Berta i la Norma són molt amigues."
- text: "El Martí llegeix el Cavall Fort."


# Catalan BERTa (RoBERTa-base) finetuned for Named Entity Recognition.


The **roberta-base-ca-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).


## Datasets


We used the NER dataset in Catalan called [Ancora-ca-ner](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) for training and evaluation.


## Evaluation and results


We evaluated the *roberta-base-ca-cased-tc* on the Ancora-ca-ner test set against standard multilingual and monolingual baselines:


| Model        | Ancora-ca-ner (F1)|
| ------------|:-------------|
| roberta-base-ca-cased-ner | **89.63** |
| mBERT       | 86.38 |
| XLM-RoBERTa | 87.66 |
| WikiBERT-ca | 77.66 |


## Citing


model name: roberta-base-ca-cased-ner


model author: projecte-aina


task: token-classification


tags: pytorch, roberta, token-classification, ca, dataset:projecte-aina/ancora-ca-ner, arxiv:1907.11692, transformers, license:???, catalan, named entity recognition, ner, CaText, Catalan Textual Corpus, model-index, autonlp_compatible, infinity_compatible


language:


- ca
license: apache-2.0
tags:
- "catalan"
- "qa"
datasets:
- "xquad-ca"
- "viquiquad"\
metrics:
- "f1"
- "exact match"
widget:
- text: "Quan va començar el Super3?"
context: "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis."
- text: "Quants eren els germans Marx?"
context: "Els germans Marx van ser un grup de còmics dels Estats Units que originàriament estava compost per cinc germans (entre parèntesis els noms artístics): Leonard (Chico), Adolph (Harpo), Julius (Groucho), Milton (Gummo) i Herbert (Zeppo)."
- text: "On van ser els Jocs Olímpics de 1992?"
context: "Els Jocs Olímpics d'estiu de 1992, oficialment Jocs Olímpics de la XXV Olimpíada, es van celebrar a la ciutat de Barcelona entre els dies 25 de juliol i 9 d'agost de 1992. Hi participaren 9.356 atletes (6.652 homes i 2.704 dones) de 169 comitès nacionals, que competiren en 32 esports i 286 especialitats."
- text: "Qui va dissenyar la Sagrada Família?"
context: "El Temple Expiatori de la Sagrada Família, conegut habitualment com la Sagrada Família, és una basílica catòlica situada a la ciutat de Barcelona. És un dels exemples més coneguts del modernisme català i un edifici únic al món, que ha esdevingut tot un símbol de la ciutat. Obra inacabada de l'arquitecte català Antoni Gaudí, és al barri de la Sagrada Família, al districte de l'Eixample de la ciutat."
- text: "Quin és el tercer volcà més gran de la Terra?"
context: "El Teide (o Pic del Teide) és un estratovolcà i muntanya de Tenerife, Illes Canàries (28.27 N, 16.6 O). Amb una altitud de 3718 m sobre el nivell del mar i amb aproximadament uns 7000 m sobre el llit marí adjacent, és la muntanya més alta d'Espanya, la muntanya més alta de totes les illes atlàntiques i el tercer volcà més gran de la Terra."


# Catalan BERTa (RoBERTa-base) finetuned for Question Answering.


The **roberta-base-ca-cased-qa** is a Question Answering (QA) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).


We used the QA dataset in Catalan called [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) for training and evaluation, and the [XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) test set for evaluation.


We evaluated the *roberta-base-ca-cased-qa* on the ViquiQuAD and XQuAD-ca test sets against standard multilingual and monolingual baselines:


| Model        | ViquiQuAD (F1/EM)  | XQuAD-ca (F1/EM) |
| ------------|:-------------:| -----:|
| roberta-base-ca-cased-qa | **86.99/73.25** | **67.81/49.43**  |
| mBERT       | 86.97/72.22 | 67.15/46.51 |
| XLM-RoBERTa | 85.50/70.47 | 67.10/46.42 |
| WikiBERT-ca | 85.45/70.75 | 65.21/36.60  |


For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/berta).


If you use any of these resources (datasets or models) in your work, please cite our latest paper:


```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
    author = "Armengol-Estap{\'e}, Jordi  and
      Carrino, Casimiro Pio  and
      Rodriguez-Penagos, Carlos  and
      de Gibert Bonet, Ona  and
      Armentano-Oller, Carme  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.437",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",
}
```


model name: roberta-base-ca-cased-qa


task: question-answering


tags: pytorch, roberta, question-answering, ca, dataset:xquad-ca, dataset:viquiquad, arxiv:1907.11692, transformers, license:apache-2.0, catalan, qa, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
model-index:
- name: roberta-twitter-spam-classifier
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. -->


# roberta-twitter-spam-classifier


This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3856
- Micro-avg-precision: 0.8723
- Micro-avg-recall: 0.8490
- Micro-avg-f1-score: 0.8594


## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Micro-avg-precision | Micro-avg-recall | Micro-avg-f1-score |
|:-------------:|:-----:|:-----:|:---------------:|:-------------------:|:----------------:|:------------------:|
| 0.4923        | 1.0   | 2762  | 0.5676          | 0.8231              | 0.6494           | 0.6676             |
| 0.535         | 2.0   | 5524  | 0.4460          | 0.8065              | 0.8215           | 0.8132             |
| 0.5492        | 3.0   | 8286  | 0.6005          | 0.6635              | 0.5843           | 0.3906             |
| 0.5947        | 4.0   | 11048 | 0.5710          | 0.7875              | 0.7799           | 0.7835             |
| 0.4976        | 5.0   | 13810 | 0.5194          | 0.8375              | 0.7544           | 0.7800             |
| 0.5263        | 6.0   | 16572 | 0.5491          | 0.8739              | 0.7159           | 0.7475             |
| 0.4701        | 7.0   | 19334 | 0.4609          | 0.8681              | 0.7786           | 0.8069             |
| 0.4566        | 8.0   | 22096 | 0.4100          | 0.8637              | 0.8281           | 0.8430             |
| 0.4339        | 9.0   | 24858 | 0.4395          | 0.8642              | 0.8454           | 0.8540             |
| 0.3906        | 10.0  | 27620 | 0.3856          | 0.8723              | 0.8490           | 0.8594             |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: roberta-twitter-spam-classifier


model author: ryancallihan


task: text-classification


tags: pytorch, tensorboard, roberta, text-classification, transformers, generated_from_trainer, model-index, infinity_compatible


tags:


- conversational


# Harry Potter DialoGPT Model


model name: DialoGPT-small-harrypotter


model author: ps2102


task: conversational


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


language: ko
datasets:


- kowiki
- news


## deeqELECTRA-base


- model: electra-base-discriminator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M


model name: delectra


model author: deeq


tags: pytorch, electra, pretraining, ko, dataset:kowiki, dataset:news, transformers, infinity_compatible


## deeqBERT-base


- model: bert-base
- vocab: bert-wordpiece, 35k
- version: latest


model name: dbert


task: fill-mask


tags: pytorch, bert, fill-mask, ko, dataset:kowiki, dataset:news, transformers, autonlp_compatible, infinity_compatible


```
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model = BertForSequenceClassification.from_pretrained("deeq/dbert-sentiment")
tokenizer = BertTokenizer.from_pretrained("deeq/dbert")
nlp = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(nlp("좋아요"))
print(nlp("글쎄요"))
```


model name: dbert-sentiment


task: text-classification


tags: pytorch, bert, text-classification, transformers, infinity_compatible


- model: electra-base-generator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M


model name: delectra-generator


tags: pytorch, electra, fill-mask, ko, dataset:kowiki, dataset:news, transformers, autonlp_compatible, infinity_compatible


## deeqBERT5


- model: bert-base
- vocab: deeqnlp 1.5, 50k
- version: latest/3.5


model name: dbert5


model author: deeq


tags: pytorch, transformers


# gpt2_review_star2


* This model has been trained as a review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)


review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)


## Model links for each 1 to 5 star


* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)


## Overview


Language model: [gpt2](https://huggingface.co/gpt2)


Language: English


Training data: review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)


## Usage


## In Transformers


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star2")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star2")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt2_review_star2",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt2_review_star2


model author: ehdwns1516


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


# gpt2_review_star1


* This model has been trained as a review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


Training data: review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star1")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star1")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt2_review_star1",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt2_review_star1


# klue-roberta-base-kornli


* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)


klue-roberta-base-kornli API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/klue-roberta-base_kornli)


## Overview


Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)


Language: Korean


Training data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)


Eval data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/klue-roberta-base_finetunning_ex)


## Usage


## In Transformers


```
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-kornli")

classifier = pipeline(
    "text-classification",
    model="ehdwns1516/klue-roberta-base-kornli",
    return_all_scores=True,
)

premise = "your premise"
hypothesis = "your hypothesis"

result = dict()
result[0] = classifier(premise + tokenizer.sep_token + hypothesis)[0]
```


model name: klue-roberta-base-kornli


model author: ehdwns1516


task: text-classification


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


# ehdwns1516/bart_finetuned_xsum


* This model has been trained as a [xsum dataset](https://huggingface.co/datasets/xsum).
* Input text what you want to summarize.


review generator DEMO: [Ainize DEMO](https://main-text-summarizer-ehdwns1516.endpoint.ainize.ai/)


review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/text_summarizer)


## Overview


Language model: [facebook/bart-large](https://huggingface.co/facebook/bart-large)


Language: English


Training data: [xsum dataset](https://huggingface.co/datasets/xsum)


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/bart_finetuned_xsum-notebook)


```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bart_finetuned_xsum")

model = AutoModelForSeq2SeqLM.from_pretrained("ehdwns1516/bart_finetuned_xsum")

summarizer = pipeline(
    "summarization",
    model="ehdwns1516/bart_finetuned_xsum",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = summarizer(context)[0]
```


model name: bart_finetuned_xsum


task: text2text-generation


tags: pytorch, bart, text2text-generation, transformers, autonlp_compatible


# gpt2_review_star3


* This model has been trained as a review_body dataset with a star of 3 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)


review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)


## Model links for each 1 to 5 star


* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)


Language model: [gpt2](https://huggingface.co/gpt2)


Training data: review_body dataset with a star of 3 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)


## Usage


## In Transformers


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star3")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star3")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt2_review_star3",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt2_review_star3


model author: ehdwns1516


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


# ehdwns1516/gpt3-kor-based_gpt2_review_SR4


* This model has been trained Korean dataset as a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


## Overview


Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2)


Language: Korean


Training data: review_body dataset with a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR4")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR4")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt3-kor-based_gpt2_review_SR4",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt3-kor-based_gpt2_review_SR4


# ehdwns1516/gpt3-kor-based_gpt2_review_SR2


* This model has been trained Korean dataset as a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


## Model links for each 1 to 5 star


Training data: review_body dataset with a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).


## Usage


## In Transformers


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR2")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR2")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt3-kor-based_gpt2_review_SR2",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt3-kor-based_gpt2_review_SR2


model author: ehdwns1516


task: text-generation


# ehdwns1516/gpt3-kor-based_gpt2_review_SR5


* This model has been trained Korean dataset as a star of 5 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)


review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)


## Overview


Language: Korean


Training data: review_body dataset with a star of 5 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR5")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR5")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt3-kor-based_gpt2_review_SR5",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt3-kor-based_gpt2_review_SR5


tags: pytorch, gpt2, text-generation, transformers


# ehdwns1516/gpt3-kor-based_gpt2_review_SR1


* This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


## Model links for each 1 to 5 star


Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2)


Training data: review_body dataset with a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).


## Usage


## In Transformers


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt3-kor-based_gpt2_review_SR1",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt3-kor-based_gpt2_review_SR1


model author: ehdwns1516


task: text-generation


# gpt2_review_star4


* This model has been trained as a review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)


## Overview


Language model: [gpt2](https://huggingface.co/gpt2)


Language: English


Training data: review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star3")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star3")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt2_review_star4",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt2_review_star4


# ehdwns1516/gpt3-kor-based_gpt2_review_SR3


* This model has been trained Korean dataset as a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)


review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)


## Model links for each 1 to 5 star


Language: Korean


Training data: review_body dataset with a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)


## Usage


## In Transformers


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt3-kor-based_gpt2_review_SR3",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt3-kor-based_gpt2_review_SR3


model author: ehdwns1516


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


# gpt2_review_star5


* This model has been trained as a review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.


## Overview


Language: English


Training data: review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).


```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star5")

model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star5")

generator = pipeline(
    "text-generation",
    model="ehdwns1516/gpt2_review_star5",
    tokenizer=tokenizer
)

context = "your context"

result = dict()
result[0] = generator(context)[0]
```


model name: gpt2_review_star5


# klue-roberta-base-sae


* This model trained with Korean dataset.
* Input sentence what you want to grasp intent.
* You can use English, but don't expect accuracy.


klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)


klue-roberta-base-kornli API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae)


Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)


Language: Korean


Training data: [kor_sae](https://huggingface.co/datasets/kor_sae)


Eval data: [kor_sae](https://huggingface.co/datasets/kor_sae)


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae_notebook)


## Usage


## In Transformers


```
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-sae")

classifier = pipeline(
    "text-classification",
    model="ehdwns1516/klue-roberta-base-kornli",
    return_all_scores=True,
)

context = "sentence what you want to grasp intent"

result = dict()
result[0] = classifier(context)[0]
```


model name: klue-roberta-base_sae


model author: ehdwns1516


task: text-classification


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


# ehdwns1516/bert-base-uncased_SWAG


* This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
* Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
* Sentence Inference Multiple Choice API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/sentence_inference_multiple_choice)


## Overview


Language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)


Language: English


Training data: [SWAG dataset](https://huggingface.co/datasets/swag)


Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/Multiple_choice_SWAG_finetunning)


```
from transformers import AutoTokenizer, AutoModelForMultipleChoice

tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")

model = AutoModelForMultipleChoice.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")

def run_model(candicates_count, context: str, candicates: list[str]):
    assert len(candicates) == candicates_count, "you need " + candicates_count + " candidates"
    choices_inputs = []
    for c in candicates:
        text_a = ""  # empty context
        text_b = context + " " + c
        inputs = tokenizer(
            text_a,
            text_b,
            add_special_tokens=True,
            max_length=128,
            padding="max_length",
            truncation=True,
            return_overflowing_tokens=True,
        )
        choices_inputs.append(inputs)

    input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
    output = model(input_ids=input_ids)

    return {"result": candicates[torch.argmax(output.logits).item()]}

items = list()
count = 4 # candicates count
context = "your context"
for i in range(int(count)):
    items.append("sentence")

result = run_model(count, context, items)
```


model name: bert-base-uncased_SWAG


tags: pytorch, bert, multiple-choice, transformers, infinity_compatible


tags:


- generated_from_trainer
datasets:
- consumer_complaints
model-index:
- name: distilbert-complaints-product
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. -->


# distilbert-complaints-product


This model was trained from the [CFBP](https://www.consumerfinance.gov/data-research/consumer-complaints/) dataset, also made available on the HuggingFace Datasets library. This model predicts the type of financial complaint based on the text provided


## Model description


A DistilBert Text Classification Model, with 18 possible classes to determine the nature of a financial customer complaint.


## Intended uses & limitations


This model is used as part of.a demonstration for E2E Machine Learning Projects focused on Contact Centre Automation:


- **Infrastructure:** Terraform
- **ML Ops:** HuggingFace (Datasets, Hub, Transformers)
- **Ml Explainability:** SHAP
- **Cloud:** AWS
  - Model Hosting: Lambda
  - DB Backend: DynamoDB
  - Orchestration: Step-Functions
  - UI Hosting: EC2
  - Routing: API Gateway
- **UI:** Budibase


## Training and evaluation data


consumer_complaints dataset


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3


### Framework versions


- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


model name: distilbert-complaints-product


model author: Kayvane


task: text-classification


tags: pytorch, distilbert, text-classification, dataset:consumer_complaints, transformers, generated_from_trainer, model-index, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: marian-finetuned-en-map
results: []


# marian-finetuned-en-map


This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-map](https://huggingface.co/Helsinki-NLP/opus-mt-en-map) on an unknown dataset.
It achieves the following results on the evaluation set:


- eval_loss: 1.0542
- eval_bleu: 30.0673
- eval_runtime: 870.8596
- eval_samples_per_second: 14.467
- eval_steps_per_second: 0.226
- epoch: 2.29
- step: 17104


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP


### Framework versions


- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: marian-finetuned-en-map


model author: raphaelmerx


task: text2text-generation


tags: pytorch, tensorboard, marian, text2text-generation, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->


# distilbert-base-uncased-finetuned-imdb


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


- Loss: 2.4722


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7117        | 1.0   | 157  | 2.4977          |
| 2.5783        | 2.0   | 314  | 2.4241          |
| 2.5375        | 3.0   | 471  | 2.4358          |


### Framework versions


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


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


model author: raphaelmerx


task: fill-mask


tags: pytorch, tensorboard, distilbert, fill-mask, dataset:imdb, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


language:


- en
- zu
license: mit
tags:
- generated_from_trainer
model-index:
- name: m2m100_small_en_zu
results: []


# m2m100_small_en_zu


This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on an unknown dataset.


## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 65
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: m2m100_small_en_zu


model author: raphaelmerx


task: text2text-generation


tags: pytorch, tensorboard, m2m_100, text2text-generation, en, zu, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible


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A television show (often simply TV show) is any content produced for broadcast via over-the-air, satellite, cable, or internet and typically viewed on a television set, excluding breaking news, advertisements, or trailers that are typically placed TNTween shows. Television shows are most often scheduled well ahead of time and appear on electronic guides or other TV listings. A television show might also be called a television program (British English: programme), especially if it lacks a narrative structure. A television series is usually released in episodes that follow a narrative, and are usually divided into seasons (US and Canada) or series (UK) — yearly or semiannual sets of new episodes. A show with a limited number of episodes may be called a miniseries, serial, or limited series. A one-time show may be called a “special”. A television film (“made-for-TV movie” or “television movie”) is a film that is initially broadcast on television rather than released in theaters or direct-to-video. Television shows can be viewed as they are broadcast in real time (live), be recorded on home video or a digital video recorder for later viewing, or be viewed on demand via a set-top box or streamed over the internet.


📺 FORMATS AND GENRES


Television shows are more varied than most other forms of media due to the wide variety of formats and genres that can be presented. A show may be fictional (as in comedies and dramas), or non-fictional (as in documentary, news, and reality television). It may be topical (as in the case of a local newscast and some made-for-television films), or historical (as in the case of many documentaries and fictional series). They could be primarily instructional or educational, or entertaining as is the case in situation comedy and game shows. A drama program usually features a set of actors playing characters in a historical or contemporary setting. The program follows their lives and adventures. Before the 1980s, shows (except for soap opera-type serials) typically remained static without story arcs, and the main characters and premise changed little. If some change happened to the characters’ lives during the episode, it was usually undone by the end. Because of this, the episodes could be broadcast in any order. Since the 1980s, many series feature progressive change in the plot, the characters, or both. For instance, Hill Street Blues and St. Elsewhere were two of the first American prime time drama television series to have this kind of dramatic structure,while the later series Babylon 5 further exemplifies such structure in that it had a predetermined story running over its intended five-season run. In 2012, it was reported that television was growing into a larger component of major media companies’ revenues than film. Some also noted the increase in quality of some television programs. In 2012, Academy-Award-winning film director Steven Soderbergh, commenting on ambiguity and complexity of character and narrative, stated: “I think those qualities are now being seen on television and that people who want to see stories that have those kinds of qualities are watching television.


📺 CREDITS


Find all the movies that you can stream online, including those that were screened this week. If you are wondering what you can watch on this website, then you should know that it covers genres that include crime, Science, Fi-Fi, action, romance, thriller, Comedy, drama, Anime Movie, etc. Thank you very much. We tell everyone who is happy to receive us as news or information about this year’s film schedule and how you watch your favorite films. Hopefully we can become the best partner for you in finding recommendations for your favorite movies. That’s all from us, greetings! Thanks for watching Videos Today. I hope you enjoy the videos that I share. Give a thumbs up, like, or share if you enjoy what we’ve shared so that we more excited. Sprinkle cheerful smile so that the world back in a variety of colors, Stay safe and Stay home.


model name: The-Many-Saints-of-Newark-Stream-2021-Ganzer-film-auf-Deutsch-Kostenlos-in-HD


model author: TheMany


tags:


language:


- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ug
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M Uyghur CV7
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ug
metrics:
    - name: Test WER
type: wer
value: 25.845
    - name: Test CER
type: cer
value: 4.795


# XLS-R-300M Uyghur CV7


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_7_0 - UG dataset.
It achieves the following results on the evaluation set:


- Loss: 0.1772
- Wer: 0.2589


## Model description


For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)


The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed.


## Intended uses & limitations


This model is expected to be of some utility for low-fidelity use cases such as:


- Draft video captions
- Indexing of recorded broadcasts


The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.


## Training and evaluation data


The combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation.


## Training procedure


The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs).


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0001
- 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: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3043        | 2.73  | 500   | 3.2415          | 1.0    |
| 3.0482        | 5.46  | 1000  | 2.9591          | 1.0    |
| 1.4767        | 8.2   | 1500  | 0.4779          | 0.5777 |
| 1.3152        | 10.93 | 2000  | 0.3697          | 0.4938 |
| 1.2246        | 13.66 | 2500  | 0.3084          | 0.4459 |
| 1.1781        | 16.39 | 3000  | 0.2842          | 0.4154 |
| 1.1351        | 19.13 | 3500  | 0.2615          | 0.3929 |
| 1.1052        | 21.86 | 4000  | 0.2462          | 0.3747 |
| 1.0711        | 24.59 | 4500  | 0.2366          | 0.3652 |
| 1.035         | 27.32 | 5000  | 0.2268          | 0.3557 |
| 1.0277        | 30.05 | 5500  | 0.2243          | 0.3450 |
| 1.002         | 32.79 | 6000  | 0.2204          | 0.3389 |
| 0.9837        | 35.52 | 6500  | 0.2156          | 0.3349 |
| 0.9773        | 38.25 | 7000  | 0.2127          | 0.3289 |
| 0.9807        | 40.98 | 7500  | 0.2142          | 0.3274 |
| 0.9582        | 43.72 | 8000  | 0.2004          | 0.3142 |
| 0.9548        | 46.45 | 8500  | 0.2022          | 0.3050 |
| 0.9251        | 49.18 | 9000  | 0.2019          | 0.3035 |
| 0.9103        | 51.91 | 9500  | 0.1964          | 0.3021 |
| 0.915         | 54.64 | 10000 | 0.1970          | 0.3032 |
| 0.8962        | 57.38 | 10500 | 0.2007          | 0.3046 |
| 0.8729        | 60.11 | 11000 | 0.1967          | 0.2942 |
| 0.8744        | 62.84 | 11500 | 0.1952          | 0.2885 |
| 0.874         | 65.57 | 12000 | 0.1894          | 0.2895 |
| 0.8457        | 68.31 | 12500 | 0.1895          | 0.2828 |
| 0.8519        | 71.04 | 13000 | 0.1912          | 0.2875 |
| 0.8301        | 73.77 | 13500 | 0.1878          | 0.2760 |
| 0.8226        | 76.5  | 14000 | 0.1808          | 0.2701 |
| 0.8071        | 79.23 | 14500 | 0.1849          | 0.2741 |
| 0.7999        | 81.97 | 15000 | 0.1808          | 0.2717 |
| 0.7947        | 84.7  | 15500 | 0.1821          | 0.2716 |
| 0.7783        | 87.43 | 16000 | 0.1824          | 0.2661 |
| 0.7729        | 90.16 | 16500 | 0.1773          | 0.2639 |
| 0.7759        | 92.9  | 17000 | 0.1767          | 0.2629 |
| 0.7713        | 95.63 | 17500 | 0.1780          | 0.2621 |
| 0.7628        | 98.36 | 18000 | 0.1773          | 0.2594 |


### Framework versions


- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


model name: xls-r-uyghur-cv7


model author: lucio


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, ug, dataset:mozilla-foundation/common_voice_7_0, transformers, license:apache-2.0, mozilla-foundation/common_voice_7_0, generated_from_trainer, robust-speech-event, model-index


language:


- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ug
- robust-speech-event
datasets:
- - mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Uyghur CV8
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ug
metrics:
    - name: Test WER
type: wer
value: 30.50
    - name: Test CER
type: cer
value: 5.80


<!-- 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. -->


# XLS-R-300M Uyghur CV8


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 - UG dataset.
It achieves the following results on the evaluation set:


- Loss: 0.2026
- Wer: 0.3248


## Model description


## Intended uses & limitations


## Training and evaluation data


## Training procedure


The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 9400 steps (100 epochs).


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3036        | 5.32  | 500  | 3.2628          | 1.0    |
| 2.9734        | 10.63 | 1000 | 2.5677          | 0.9980 |
| 1.3466        | 15.95 | 1500 | 0.4455          | 0.6306 |
| 1.2424        | 21.28 | 2000 | 0.3603          | 0.5301 |
| 1.1655        | 26.59 | 2500 | 0.3165          | 0.4740 |
| 1.1026        | 31.91 | 3000 | 0.2930          | 0.4400 |
| 1.0655        | 37.23 | 3500 | 0.2675          | 0.4159 |
| 1.0239        | 42.55 | 4000 | 0.2580          | 0.3913 |
| 0.9938        | 47.87 | 4500 | 0.2373          | 0.3698 |
| 0.9655        | 53.19 | 5000 | 0.2379          | 0.3675 |
| 0.9374        | 58.51 | 5500 | 0.2486          | 0.3795 |
| 0.9065        | 63.83 | 6000 | 0.2243          | 0.3405 |
| 0.888         | 69.15 | 6500 | 0.2157          | 0.3277 |
| 0.8646        | 74.47 | 7000 | 0.2103          | 0.3288 |
| 0.8602        | 79.78 | 7500 | 0.2088          | 0.3238 |
| 0.8442        | 85.11 | 8000 | 0.2045          | 0.3266 |
| 0.8335        | 90.42 | 8500 | 0.2038          | 0.3241 |
| 0.8288        | 95.74 | 9000 | 0.2024          | 0.3280 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: xls-r-uyghur-cv8


model author: lucio


task: automatic-speech-recognition


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


language: rw
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large Kinyarwanda with apostrophes
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice rw
type: common_voice
args: rw
metrics:
    - name: Test WER
type: wer
value: 39.92


# Wav2Vec2-Large-XLSR-53-rw


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the [lucio/wav2vec2-large-xlsr-kinyarwanda](https://huggingface.co/lucio/wav2vec2-large-xlsr-kinyarwanda) model, which does not predict any punctuation, this model attempts to predict the apostrophes that mark contractions of pronouns with vowel-initial words, but may overgeneralize.
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# WARNING! This will download and extract to use about 80GB on disk.
test_dataset = load_dataset("common_voice", "rw", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


Result:


```
Prediction: ['yaherukago gukora igitaramo yiki mujyiwa na mor mu bubiligi', "ibi rero ntibizashoboka kandi n'umudabizi"]
Reference: ['Yaherukaga gukora igitaramo nk’iki mu Mujyi wa Namur mu Bubiligi.', 'Ibi rero, ntibizashoboka, kandi nawe arabizi.']
```


## Evaluation


The model can be evaluated as follows on the Kinyarwanda test data of Common Voice. Note that to even load the test data, the whole 40GB Kinyarwanda dataset will be downloaded and extracted into another 40GB directory, so you will need that space available on disk (e.g. not possible in the free tier of Google Colab). This script uses the `chunked_wer` function from [pcuenq](https://huggingface.co/pcuenq/wav2vec2-large-xlsr-53-es).


```python
import jiwer
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import unidecode

test_dataset = load_dataset("common_voice", "rw", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied")
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied")
model.to("cuda")

chars_to_ignore_regex = r'[!"#$%&()*+,./:;<=>?@\[\]\\_{}|~£¤¨©ª«¬®¯°·¸»¼½¾ðʺ˜˝ˮ‐–—―‚“”„‟•…″‽₋€™−√�]'

def remove_special_characters(batch):
    batch["text"] = re.sub(r'[ʻʽʼ‘’´`]', r"'", batch["sentence"])  # normalize apostrophes
    batch["text"] = re.sub(chars_to_ignore_regex, "", batch["text"]).lower().strip()  # remove all other punctuation
    batch["text"] = re.sub(r"([b-df-hj-np-tv-z])' ([aeiou])", r"\1'\2", batch["text"])  # remove spaces where apostrophe marks a deleted vowel
    batch["text"] = re.sub(r"(-| '|' |  +)", " ", batch["text"])  # treat dash and other apostrophes as word boundary
    batch["text"] = unidecode.unidecode(batch["text"])  # strip accents from loanwords
    return batch

## Audio pre-processing
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    batch["sampling_rate"] = 16_000
    return batch

def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = speech_file_to_array_fn(batch)
    return batch

test_dataset = test_dataset.map(cv_prepare)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

def chunked_wer(targets, predictions, chunk_size=None):
    if chunk_size is None: return jiwer.wer(targets, predictions)
    start = 0
    end = chunk_size
    H, S, D, I = 0, 0, 0, 0
    while start < len(targets):
        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
        H = H + chunk_metrics["hits"]
        S = S + chunk_metrics["substitutions"]
        D = D + chunk_metrics["deletions"]
        I = I + chunk_metrics["insertions"]
        start += chunk_size
        end += chunk_size
    return float(S + D + I) / float(H + S + D)

print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))
```


**Test Result**: 39.92 %


## Training


Examples from the Common Voice training dataset were used for training, after filtering out utterances that had any `down_vote` or were longer than 9.5 seconds. The data used totals about 125k examples, 25% of the available data, trained on 1 V100 GPU provided by OVHcloud, for a total of about 60 hours: 20 epochs on one block of 32k examples and then 10 epochs each on 3 more blocks of 32k examples. For validation, 2048 examples of the validation dataset were used.


The [script used for training](https://github.com/serapio/transformers/blob/feature/xlsr-finetune/examples/research_projects/wav2vec2/run_common_voice.py) is adapted from the [example script provided in the transformers repo](https://github.com/huggingface/transformers/blob/master/examples/research_projects/wav2vec2/run_common_voice.py).


model name: wav2vec2-large-xlsr-kinyarwanda-apostrophied


model author: lucio


task: automatic-speech-recognition


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


language: rw
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large Kinyarwanda no punctuation
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice rw
type: common_voice
args: rw
metrics:
    - name: Test WER
type: wer
value: 40.59


# Wav2Vec2-Large-XLSR-53-rw


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 20% of the training data (limited to utterances without downvotes and shorter than 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the [lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied](https://huggingface.co/lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied) model, which predicts the apostrophes that mark contractions of pronouns with vowel-initial words, this model does not predict any punctuation.
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


Result:


```
Prediction: ['yaherukaga gukora igitaramo y iki mu jyiwa na mul mumbiliki', 'ini rero ntibizashoboka ka nibo nkunrabibzi']
Reference: ['Yaherukaga gukora igitaramo nk’iki mu Mujyi wa Namur mu Bubiligi.', 'Ibi rero, ntibizashoboka, kandi nawe arabizi.']
```


## Evaluation


```python
import jiwer
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import unidecode

test_dataset = load_dataset("common_voice", "rw", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")
model.to("cuda")

chars_to_ignore_regex = r'[!"#$%&()*+,./:;<=>?@\[\]\\_{}|~£¤¨©ª«¬®¯°·¸»¼½¾ðʺ˜˝ˮ‐–—―‚“”„‟•…″‽₋€™−√�]'

def remove_special_characters(batch):
    batch["text"] = re.sub(r'[ʻʽʼ‘’´`]', r"'", batch["sentence"])  # normalize apostrophes
    batch["text"] = re.sub(chars_to_ignore_regex, "", batch["text"]).lower().strip()  # remove all other punctuation
    batch["text"] = re.sub(r"(-| ?' ?|  +)", " ", batch["text"])  # treat dash and apostrophe as word boundary
    batch["text"] = unidecode.unidecode(batch["text"])  # strip accents
    return batch

## Audio pre-processing
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    batch["sampling_rate"] = 16_000
    return batch

def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = speech_file_to_array_fn(batch)
    return batch

test_dataset = test_dataset.map(cv_prepare)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

def chunked_wer(targets, predictions, chunk_size=None):
    if chunk_size is None: return jiwer.wer(targets, predictions)
    start = 0
    end = chunk_size
    H, S, D, I = 0, 0, 0, 0
    while start < len(targets):
        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
        H = H + chunk_metrics["hits"]
        S = S + chunk_metrics["substitutions"]
        D = D + chunk_metrics["deletions"]
        I = I + chunk_metrics["insertions"]
        start += chunk_size
        end += chunk_size
    return float(S + D + I) / float(H + S + D)

print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))
```


**Test Result**: 40.59 %


## Training


Blocks of examples from the Common Voice training dataset were used for training, after filtering out utterances that had any `down_vote` or were longer than 9.5 seconds. The data used totals about 100k examples, 20% of the available data. Training proceeded for 30k global steps, on 1 V100 GPU provided by OVHcloud. For validation, 2048 examples of the validation dataset were used.


model name: wav2vec2-large-xlsr-kinyarwanda


language:


- uz
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Uzbek CV8
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: uz
metrics:
    - name: Test WER (with LM)
type: wer
value: 15.065
    - name: Test CER (with LM)
type: cer
value: 3.077
    - name: Test WER (no LM)
type: wer
value: 32.88
    - name: Test CER (no LM)
type: cer
value: 6.53


# XLS-R-300M Uzbek CV8


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 - UZ dataset.
It achieves the following results on the validation set:


- Loss: 0.3063
- Wer: 0.3852
- Cer: 0.0777


## Model description


For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)


The model vocabulary consists of the [Modern Latin alphabet for Uzbek](https://en.wikipedia.org/wiki/Uzbek_alphabet), with punctuation removed.
Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies \<o\> and \<g\>, and <’> indicates the glottal stop or a long vowel.


The decoder uses a kenlm language model built on common_voice text.


## Intended uses & limitations


This model is expected to be of some utility for low-fidelity use cases such as:


- Draft video captions
- Indexing of recorded broadcasts


The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.


## Training and evaluation data


The 50% of the `train` common voice official split was used as training data. The 50% of the official `dev` split was used as validation data, and the full `test` set was used for final evaluation of the model without LM, while the model with LM was evaluated only on 500 examples from the `test` set.


The kenlm language model was compiled from the target sentences of the train + other dataset splits.


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.1401        | 3.25  | 500   | 3.1146          | 1.0    | 1.0    |
| 2.7484        | 6.49  | 1000  | 2.2842          | 1.0065 | 0.7069 |
| 1.0899        | 9.74  | 1500  | 0.5414          | 0.6125 | 0.1351 |
| 0.9465        | 12.99 | 2000  | 0.4566          | 0.5635 | 0.1223 |
| 0.8771        | 16.23 | 2500  | 0.4212          | 0.5366 | 0.1161 |
| 0.8346        | 19.48 | 3000  | 0.3994          | 0.5144 | 0.1102 |
| 0.8127        | 22.73 | 3500  | 0.3819          | 0.4944 | 0.1051 |
| 0.7833        | 25.97 | 4000  | 0.3705          | 0.4798 | 0.1011 |
| 0.7603        | 29.22 | 4500  | 0.3661          | 0.4704 | 0.0992 |
| 0.7424        | 32.47 | 5000  | 0.3529          | 0.4577 | 0.0957 |
| 0.7251        | 35.71 | 5500  | 0.3410          | 0.4473 | 0.0928 |
| 0.7106        | 38.96 | 6000  | 0.3401          | 0.4428 | 0.0919 |
| 0.7027        | 42.21 | 6500  | 0.3355          | 0.4353 | 0.0905 |
| 0.6927        | 45.45 | 7000  | 0.3308          | 0.4296 | 0.0885 |
| 0.6828        | 48.7  | 7500  | 0.3246          | 0.4204 | 0.0863 |
| 0.6706        | 51.95 | 8000  | 0.3250          | 0.4233 | 0.0868 |
| 0.6629        | 55.19 | 8500  | 0.3264          | 0.4159 | 0.0849 |
| 0.6556        | 58.44 | 9000  | 0.3213          | 0.4100 | 0.0835 |
| 0.6484        | 61.69 | 9500  | 0.3182          | 0.4124 | 0.0837 |
| 0.6407        | 64.93 | 10000 | 0.3171          | 0.4050 | 0.0825 |
| 0.6375        | 68.18 | 10500 | 0.3150          | 0.4039 | 0.0822 |
| 0.6363        | 71.43 | 11000 | 0.3129          | 0.3991 | 0.0810 |
| 0.6307        | 74.67 | 11500 | 0.3114          | 0.3986 | 0.0807 |
| 0.6232        | 77.92 | 12000 | 0.3103          | 0.3895 | 0.0790 |
| 0.6216        | 81.17 | 12500 | 0.3086          | 0.3891 | 0.0790 |
| 0.6174        | 84.41 | 13000 | 0.3082          | 0.3881 | 0.0785 |
| 0.6196        | 87.66 | 13500 | 0.3059          | 0.3875 | 0.0782 |
| 0.6174        | 90.91 | 14000 | 0.3084          | 0.3862 | 0.0780 |
| 0.6169        | 94.16 | 14500 | 0.3070          | 0.3860 | 0.0779 |
| 0.6166        | 97.4  | 15000 | 0.3066          | 0.3855 | 0.0778 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: xls-r-uzbek-cv8


model author: lucio


task: automatic-speech-recognition


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


language:


- ky
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Kyrgiz CV8
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ky
metrics:
    - name: Test WER (with LM)
type: wer
value: 19.01
    - name: Test CER (with LM)
type: cer
value: 5.38
    - name: Test WER (no LM)
type: wer
value: 31.28
    - name: Test CER (no LM)
type: cer
value: 7.66


<!-- 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. -->


# XLS-R-300M Kyrgiz CV8


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 - KY dataset.
It achieves the following results on the validation set:


- Loss: 0.5497
- Wer: 0.2945
- Cer: 0.0791


## Model description


For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)


The model vocabulary consists of the cyrillic alphabet with punctuation removed.


The kenlm language model is built using the text of the train and invalidated corpus splits.


## Intended uses & limitations


This model is expected to be of some utility for low-fidelity use cases such as:


- Draft video captions
- Indexing of recorded broadcasts


The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.


## Training and evaluation data


The combination of `train`, `dev` and `other` of common voice official splits were used as training data. The half of the official `test` split was used as validation data, as and the full `test` set was used for final evaluation.


## Training procedure


The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Kyrgiz CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 500 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 8100 steps (300 epochs).


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- 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: 500
- num_epochs: 300.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 3.1079        | 18.51  | 500  | 2.6795          | 0.9996 | 0.9825 |
| 0.8506        | 37.04  | 1000 | 0.4323          | 0.3718 | 0.0961 |
| 0.6821        | 55.55  | 1500 | 0.4105          | 0.3311 | 0.0878 |
| 0.6091        | 74.07  | 2000 | 0.4281          | 0.3168 | 0.0851 |
| 0.5429        | 92.58  | 2500 | 0.4525          | 0.3147 | 0.0842 |
| 0.5063        | 111.11 | 3000 | 0.4619          | 0.3144 | 0.0839 |
| 0.4661        | 129.62 | 3500 | 0.4660          | 0.3039 | 0.0818 |
| 0.4353        | 148.15 | 4000 | 0.4695          | 0.3083 | 0.0820 |
| 0.4048        | 166.65 | 4500 | 0.4909          | 0.3085 | 0.0824 |
| 0.3852        | 185.18 | 5000 | 0.5074          | 0.3048 | 0.0812 |
| 0.3567        | 203.69 | 5500 | 0.5111          | 0.3012 | 0.0810 |
| 0.3451        | 222.22 | 6000 | 0.5225          | 0.2982 | 0.0804 |
| 0.325         | 240.73 | 6500 | 0.5270          | 0.2955 | 0.0796 |
| 0.3089        | 259.25 | 7000 | 0.5381          | 0.2929 | 0.0793 |
| 0.2941        | 277.76 | 7500 | 0.5565          | 0.2923 | 0.0794 |
| 0.2945        | 296.29 | 8000 | 0.5495          | 0.2951 | 0.0789 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: xls-r-kyrgiz-cv8


model author: lucio


task: automatic-speech-recognition


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


language: lg
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large Luganda by Lucio
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lg
type: common_voice
args: lg
metrics:
    - name: Test WER
type: wer
value: 29.52


# Wav2Vec2-Large-XLSR-53-lg


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Luganda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using train, validation and other (excluding voices that are in the test set), and taking the test data for validation as well as test.
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "lg", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-luganda")
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-luganda")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Luganda test data of Common Voice. (Available in Colab [here](https://colab.research.google.com/drive/1XxZ3mJOEXwIn-QH3C23jD_Qpom9aA1vH?usp=sharing).)


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

test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-luganda")
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-luganda")
model.to("cuda")

chars_to_ignore_regex = '[\[\],?.!;:%"“”(){}‟ˮʺ″«»/…‽�–]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

def remove_special_characters(batch):
    # word-internal apostrophes are marking contractions
    batch["norm_text"] = re.sub(r'[‘’´`]', r"'", batch["sentence"])
    # most other punctuation is ignored
    batch["norm_text"] = re.sub(chars_to_ignore_regex, "", batch["norm_text"]).lower().strip()
    batch["norm_text"] = re.sub(r"(-|' | '|  +)", " ", batch["norm_text"])
    # remove accents from a few characters (from loanwords, not tones)
    batch["norm_text"] = unidecode.unidecode(batch["norm_text"])
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
test_dataset = test_dataset.map(remove_special_characters)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["norm_text"])))
```


**Test Result**: 29.52 %


## Training


The Common Voice `train`, `validation` and `other` datasets were used for training, excluding voices that are in both the `other` and `test` datasets. The data was augmented to twice the original size with added noise and manipulated pitch, phase and intensity.
Training proceeded for 60 epochs, on 1 V100 GPU provided by OVHcloud. The `test` data was used for validation.


The [script used for training](https://github.com/serapio/transformers/blob/feature/xlsr-finetune/examples/research_projects/wav2vec2/run_common_voice.py) is adapted from the [example script provided in the transformers repo](https://github.com/huggingface/transformers/blob/master/examples/research_projects/wav2vec2/run_common_voice.py).


model name: wav2vec2-large-xlsr-luganda


model author: lucio


task: automatic-speech-recognition


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


tags:


- conversational


# Kotonoha Katsura DialoGPT Model


model name: DialoGPT-small-kotonoha


model author: saintseer121323


task: conversational


tags: conversational


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-distilBERT
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. -->


# ai-club-inductions-21-nlp-distilBERT


This model was trained from scratch on an unknown dataset.


## 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: 10


### Framework versions


- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: ai-club-inductions-21-nlp-distilBERT


model author: AyushPJ


task: question-answering


tags: pytorch, distilbert, question-answering, transformers, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-ALBERT
results: []


# ai-club-inductions-21-nlp-ALBERT


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: ai-club-inductions-21-nlp-ALBERT


tags: pytorch, albert, question-answering, transformers, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


- generated_from_trainer
datasets:
- squad
model-index:
- name: test-squad-trained-finetuned-squad
results: []


# test-squad-trained-finetuned-squad


This model was trained from scratch on the squad dataset.


- 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


### Framework versions


- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.13.3
- Tokenizers 0.10.3


model name: test-squad-trained-finetuned-squad


model author: AyushPJ


task: question-answering


tags: pytorch, tensorboard, distilbert, question-answering, dataset:squad, transformers, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa-base-squad-v2
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. -->


# ai-club-inductions-21-nlp-roBERTa-base-squad-v2


This model was trained from scratch on an unknown dataset.


## 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: 5


### Framework versions


model name: ai-club-inductions-21-nlp-roBERTa-base-squad-v2


model author: AyushPJ


task: question-answering


tags: pytorch, roberta, question-answering, transformers, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-ELECTRA-base-squad
results: []


# ai-club-inductions-21-nlp-ELECTRA-base-squad


This model is the deepset/electra-base-squad2 pre-trained model trained on data from AI Inductions 21 NLP competition (https://www.kaggle.com/c/ai-inductions-21-nlp) for extractive QA.


## Model description


More information needed


## Intended uses & limitations


AI Inductions 21 NLP competition


## Training and evaluation data


AI Inductions 21 NLP competition data


## Training procedure


### Training hyperparameters


- max_length = 512
- doc_stride = 384
- learning_rate: 2e-05
- weight_decay=0.01
- 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: 10


### Framework versions


- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: ai-club-inductions-21-nlp-ELECTRA-base-squad


model author: AyushPJ


task: question-answering


tags: pytorch, electra, question-answering, transformers, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa
results: []


# ai-club-inductions-21-nlp-roBERTa


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


- 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: 10


model name: ai-club-inductions-21-nlp-roBERTa


- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-XLNet
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. -->


# ai-club-inductions-21-nlp-XLNet


This model was trained from scratch on an unknown dataset.


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


### Framework versions


model name: ai-club-inductions-21-nlp-XLNet


model author: AyushPJ


task: question-answering


tags: pytorch, xlnet, question-answering, transformers, generated_from_trainer, model-index, autonlp_compatible


tags:


- keras


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32


## Training Metrics


| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.10008902847766876|  0.09358672052621841|
| 2| 0.09307441115379333|  0.09132333099842072|
| 3| 0.09141746163368225|  0.09007035195827484|
| 4| 0.09042416512966156|  0.08926571905612946|
| 5| 0.0896773487329483|  0.08868413418531418|


## Model Plot


![Model Image](model.png)


model name: model-card-f


model author: merve


tags: tensorboard, keras


- transformers
- text-classification
pipeline-tag:
- text-classification


Title


model name: deberta-small-mrpc


task: text-classification


tags: pytorch, tensorboard, transformers, text-classification


tags:


- Keras


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


model name: model-card


tags: keras, Keras


- keras


## Training Metrics


| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.10180462151765823|  0.09393619745969772|
| 2| 0.09350588172674179|  0.09173807501792908|
| 3| 0.0917932540178299|  0.09045031666755676|
| 4| 0.090699702501297|  0.08965961635112762|
| 5| 0.08992037177085876|  0.08895936608314514|


## Model Plot


<details>
<summary>View Model Plot</summary>


Plot is not available for this model.


</details>


model name: model-card-toggle-test3


model author: merve


tags:


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.10184162855148315|  0.09344744682312012|
| 2| 0.09291943907737732|  0.09124121814966202|
| 3| 0.09122606366872787|  0.08993067592382431|
| 4| 0.09014243632555008|  0.08901000022888184|
| 5| 0.0893004909157753|  0.08826032280921936|


model name: model-card-toggle-test1


- Keras


Model Summary


<details>
<summary>View Model Summary</summary>
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 input_1 (InputLayer)        [(None, 28, 28, 1)]       0


conv2d (Conv2D)             (None, 28, 28, 32)        320


max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0\
)


conv2d_1 (Conv2D)           (None, 14, 14, 32)        9248


max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0\
2D)


conv2d_transpose (Conv2DTra  (None, 14, 14, 32)       9248\
nspose)


conv2d_transpose_1 (Conv2DT  (None, 28, 28, 32)       9248\
ranspose)


conv2d_2 (Conv2D)           (None, 28, 28, 1)         289


=================================================================
Total params: 28,353
Trainable params: 28,353
Non-trainable params: 0


<\details>


model name: model-card-summary


model author: merve


tags: tensorboard, keras, Keras


license: apache-2.0
tags:


- generated_from_keras_callback
model-index:
- name: merve/distilbert-base-uncased-finetuned-ner
results: []
datasets:
- "conll2003"


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


# merve/distilbert-base-uncased-finetuned-ner


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


- Train Loss: 0.2037
- Validation Loss: 0.0703
- Epoch: 0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32


### Training results


| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2037     | 0.0703          | 0     |


### Framework versions


- Transformers 4.16.0.dev0
- TensorFlow 2.7.0
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3


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


model author: merve


task: token-classification


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


- keras


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32


## Training Metrics


| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.10184162855148315|  0.09344744682312012|
| 2| 0.09291943907737732|  0.09124121814966202|
| 3| 0.09122606366872787|  0.08993067592382431|
| 4| 0.09014243632555008|  0.08901000022888184|
| 5| 0.0893004909157753|  0.08826032280921936|


## Model Plot


<details>
<summary>View Model Plot</summary>


Plot is not available for this model.


</details>


model name: model-card-toggle-test


model author: merve


tags:


- Keras


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


## Model Summary


<details>
<summary>View Model Summary</summary>
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 input_1 (InputLayer)        [(None, 28, 28, 1)]       0


conv2d (Conv2D)             (None, 28, 28, 32)        320


max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0\
)


conv2d_1 (Conv2D)           (None, 14, 14, 32)        9248


max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0\
2D)


conv2d_transpose (Conv2DTra  (None, 14, 14, 32)       9248\
nspose)


conv2d_transpose_1 (Conv2DT  (None, 28, 28, 32)       9248\
ranspose)


conv2d_2 (Conv2D)           (None, 28, 28, 1)         289


=================================================================
Total params: 28,353
Trainable params: 28,353
Non-trainable params: 0


<\details>


## Training Metrics


| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 0| 0.10039298236370087|  0.09316997975111008|
| 1| 0.09266144037246704|  0.0910152718424797|
| 2| 0.09103700518608093|  0.08978495746850967|
| 3| 0.08996893465518951|  0.0890018492937088|
| 4| 0.08926930278539658|  0.08835636079311371|


## Model Plot


![Model Image](model.png)


model name: model-card-final


model author: merve


tags: tensorboard, keras, Keras