# 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.

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/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)

## 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).

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_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]
```


# 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.

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/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)

## 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 2 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_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]
```


# 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)

## Model links for each 1 to 5 star
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)

## 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 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)

## Usage
## In Transformers

```
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]
```


# 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.

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/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)

## 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 1 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_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]
```


# 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.

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 4 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_star4",
    tokenizer=tokenizer
)

context = "your context"

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


# 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
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)

## 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 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]
```


# 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.

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 5 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_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]
```


# 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)

## Overview

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]
```


# 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)

## Usage
## In Transformers

```
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)
```


---
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


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: marian-finetuned-en-map
  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. -->

# 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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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


---
language:
- en
- zu
license: mit
tags:
- generated_from_trainer
model-index:
- name: m2m100_small_en_zu
  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. -->

# 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

More information needed

## Training and evaluation data

More information needed

## 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


KINO » The Many Saints of Newark (2021) ganzer film deutsch Online Anschauen , The Many Saints of Newark Stream Deutsch , Ganzer Film "The Many Saints of Newark" Stream Deutsch komplett, The Many Saints of Newark (2021) ganzer film deutsch Online Anschauen.

📀 📺 Schau jetzt ▶▶ https://world.zmovies25.com/de/524369/the-many-saints-of-newark.html

📀 📥 Jetzt Downloaden ▶▶ https://v.ht/eza_Ganteng

VIDEOS: TRAILERS, TEASERS, FEATURETTES

Veröffentlicht: 2021-09-23

Genre: Drama, Krimi,

Sterne: Michael Gandolfini, Alessandro Nivola, Jon Bernthal, Vera Farmiga, Ray Liotta, Corey Stoll, Billy Magnussen, John Magaro, Leslie Odom Jr., Michela De Rossi, Joey Diaz, William Ludwig, Matteo Russo, Spenser Granese, Phyllis Pastore, Danny Schoch, Rob Coletti, Chase Vacnin, Alex Morf, Nick Vallelonga, Lesli Margherita,

Direktor: James Wan, James Wan, Christian Wagner, Anne McCarthy, Carey Hayes

Tags: The Many Saints of Newark deutsche filme online gucken kostenlos,The Many Saints of Newark filme streamen movie2k,The Many Saints of Newark kostenlose filme online gratis kinofilme,The Many Saints of Newark filme online schauen movie2k, The Many Saints of Newark deutsche filme auf youtube in voller länge,The Many Saints of Newark film komplett deutsch youtube,The Many Saints of Newark youtube ganze filme deutsch liebesfilme,The Many Saints of Newark filme in voller länge kostenlos ansehen youtube ,The Many Saints of Newark youtube filme anschauen kostenlos deutsch,The Many Saints of Newark gute filme auf youtube in voller länge,The Many Saints of Newark youtube filme anschauen legal,The Many Saints of Newark komplette filme auf youtube ,The Many Saints of Newark youtube ganze filme deutsch

Familienvater Hutch (Bob Odenkirk) ist ein Niemand. Der Vorstadt-Familienvater wird von den meisten Leuten einfach ignoriert und zieht auch niemals die Aufmerksamkeit auf sich. Als zwei Verbrecher eines Nachts in sein Haus einsteigen, sieht er sich außerstande, sich oder seine Familie zu verteidigen. Schließlich will er Gewalt um jeden Preis verhindern. Sein Sohn Brady (Gage Munroe) wendet sich daraufhin enttäuscht von seinem Vater ab. Seine Frau Becca (Connie Nielsen) tut es ihm gleich und zieht sich noch mehr zurück. Doch der Einbruch löst bei Hutch etwas aus. Er sieht plötzlich rot und enthüllt eine dunkle Seite, die ihn auf einen Pfad der Gewalt schickt: Einst arbeitete der vermeintliche Versager nämlich als Killer für die Mafia. Und von nun an stellt er mit allen Mitteln sicher, dass seiner Familie nie wieder etwas geschehen und er als Niemand so schnell nicht mehr unterschätzt wird...

„The Many Saints of Newark“ - Hintergründe, Besetzung und Kinostart

Fünf Jahre sind vergangen, seit Disney mit „Vaiana“ den letzten Animationsfilm mit einem Original-Drehbuch in die Kinos gebracht hat. „The Many Saints of Newark“ tritt nun in die Fußstapfen und erzählt nach den Fortsetzungen im Jahr 2019 („Die Eiskönigin 2“ und „Ralph Reichts 2“) eine eigene Geschichte. Für das Drehbuch war die „Crazy Rich“-Autorin Adele Lim sowie Qui Nguyen verantwortlich.

Die beiden Hauptrollen von Raya und Wasserdrache Sisu werden von Kelly Marie Tran („Star Wars“-Reihe) und GThe Many Saints of Newarken-Globe-Gewinnerin Awkwafina („The Farewell“) eingesprochen. „The Many Saints of Newark“ ist der 62. abendfüllende Disney-Film und der erste in der neuen Dekade der 2020er. Das Regieduo Don Hall und Carlos López Estrada hat ganz unterschiedliche Erfahrungen in der Filmwelt. Don Hall führte Regie bei den Disney-Filmen „Baymax“ und „Vaiana“, Estrada ist für Indie-Filme „Summertime“ und „Blindspotting“ verantwortlich.

Der deutsche Kinostart von „The Many Saints of Newark“ ist mit etwas Verspätung am 11. März 2021.

Etikett

The Many Saints of Newark 2021 Ganzer Film Deutsch

The Many Saints of Newark 2021 kostenlos sehen

The Many Saints of Newark Anschauen Film

The Many Saints of Newark komplette Film Deutsch

The Many Saints of Newark komplett online sehen

The Many Saints of Newark Online Anschauen

The Many Saints of Newark Online Kostenlos

The Many Saints of Newark 2021 komplette Stream Deutsch

The Many Saints of Newark(2021) Deutsch HD Stream Online anschauen

The Many Saints of Newark stream Deutsch kinox

The Many Saints of Newark kinox Deutsch HD

The Many Saints of Newark ganzer film Deutsch

The Many Saints of Newark online stream

The Many Saints of Newark stream kostenlos

The Many Saints of Newark hd film germany

The Many Saints of Newark stream kkiste

The Many Saints of Newark kinostart.de

The Many Saints of Newark Deutsch Film

The Many Saints of Newark Ganzer Film Deutsch

The Many Saints of Newark Deutsch kinox

The Many Saints of Newark Deutsch Film HD

The Many Saints of Newark Deutsch Film online

The Many Saints of Newark Deutsch Film voll kostenlos

Uhr The Many Saints of Newark (2021) Röhrenuhr

The Many Saints of Newark (2021) HDQ Voller

The Many Saints of Newark Austria

Uhr The Many Saints of Newark (2021) herunterladen Untertitel

📺 TELEVISION SHOW AND HISTORY

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.


---
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


---
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

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 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

The following hyperparameters were used during training:
- 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


---
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).


---
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

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: ['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

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")
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.

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).


---
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


---
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


---
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).


---
tags:
- conversational
---

# Kotonoha Katsura DialoGPT Model


---
tags:
- 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

More information needed

## Training and evaluation data

More information needed

## 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


---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-ALBERT
  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-ALBERT

This model was trained from scratch on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: test-squad-trained-finetuned-squad
  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. -->

# test-squad-trained-finetuned-squad

This model was trained from scratch on the squad dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

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

### Framework versions

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


---
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

More information needed

## Training and evaluation data

More information needed

## 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

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


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

The following hyperparameters were used during training:
- 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


---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa
  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

This model was trained from scratch on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3


---
tags:
- 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.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3


---
tags:
- keras
---

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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)


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

Title


---
tags:
- Keras
---
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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


---
library_name:
- keras
---

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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| 0.10180462151765823|  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>


---
library_name:
- keras
---

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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>


---
tags:
- Keras
---
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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

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>


---
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

More information needed

## Training and evaluation data

More information needed

## 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


---
tags:
- object-detection
library_name: keras
---

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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| 0.09961754083633423|  0.09323594719171524|
| 2| 0.09265559166669846|  0.09110038727521896|
| 3| 0.09106099605560303|  0.08995018154382706|
| 4| 0.09000983089208603|  0.08897411823272705|
| 5| 0.0892450213432312|  0.08839458972215652|
 ## Model Plot

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

![Model Image](./model.png)

</details>


---
tags:
- Keras
---
# TODO: Fill this model card


Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx

This is based on bert-base-uncased model and pre-trained for text input


Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx

This is based on roberta-base model and pre-trained for text input


Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx

This is based on tapas-base(no_reset) model and pre-trained for table input


---
tags:
 - conversational
---

This is an improved version of the Joshua bot


---
tags:
- conversational
---

#This is a chatbot trained on the transcript of the game "The World Ends with You"


---
license: mit
tags:
- generated_from_trainer
model-index:
- name: output_mlm
  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. -->

# output_mlm

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2024

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.5832        | 0.19  | 15000  | 1.4992          |
| 1.5325        | 0.39  | 30000  | 1.4653          |
| 1.4979        | 0.58  | 45000  | 1.4359          |
| 1.4715        | 0.77  | 60000  | 1.4039          |
| 1.4448        | 0.97  | 75000  | 1.3877          |
| 1.4191        | 1.16  | 90000  | 1.3603          |
| 1.3988        | 1.35  | 105000 | 1.3425          |
| 1.3699        | 1.54  | 120000 | 1.3230          |
| 1.3493        | 1.74  | 135000 | 1.3012          |
| 1.3201        | 1.93  | 150000 | 1.2773          |
| 1.2993        | 2.12  | 165000 | 1.2617          |
| 1.2745        | 2.32  | 180000 | 1.2490          |
| 1.2614        | 2.51  | 195000 | 1.2283          |
| 1.2424        | 2.7   | 210000 | 1.2152          |
| 1.2296        | 2.9   | 225000 | 1.2052          |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3


Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)

Fine tuned for dialogue response generation on the [Schema Guided Dialogue Dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) (Rastogi et al., 2019)

Three additional special tokens were added during the fine-tuning process:
 - <|pad|> padding token
 - <|user|> speaker control token to prompt user responses
 - <|system|> speaker control token to prompt system responses


Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)

Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)

Three additional special tokens were added during the fine-tuning process:
 - <|pad|> padding token
 - <|user|> speaker control token to prompt user responses
 - <|system|> speaker control token to prompt system responses

 The following Dialogues were excluded:
 - Those with donation amounts outside of the task range of [$0, $2].
 - Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue.
 - Those with more than 800 words.

 Stats:
 - Training set: 519 dialogues
 - Validation set: 58 dialogues
 - ~20 utterances per dialogue


Base model: [gpt2-xl](https://huggingface.co/gpt2-xl)

Domain-adapted for dialogue response and narrative generation on a [narrative-aligned variant](https://github.com/AbrahamSanders/gutenberg-dialog#download-narrative-aligned-datasets) of the [Gutenberg Dialogue Dataset (Csaky & Recski, 2021)](https://aclanthology.org/2021.eacl-main.11.pdf)

Fine-tuned for dialogue response generation on [Persuasion For Good (Wang et al., 2019)](https://aclanthology.org/P19-1566.pdf) ([dataset](https://gitlab.com/ucdavisnlp/persuasionforgood))


Base model: [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large)

Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)

Three additional special tokens were added during the fine-tuning process:
 - <|pad|> padding token
 - <|user|> speaker control token to prompt user responses
 - <|system|> speaker control token to prompt system responses

 The following Dialogues were excluded:
 - Those with donation amounts outside of the task range of [$0, $2].
 - Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue.
 - Those with more than 800 words.

 Stats:
 - Training set: 519 dialogues
 - Validation set: 58 dialogues
 - ~20 utterances per dialogue


---
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: koelectra-sts-v0.4
  results:
  - task:
      name: Text Classification
      type: text-classification
    metrics:
    - name: Spearmanr
      type: spearmanr
      value: 0.9286505242442783
---

<!-- 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. -->

# koelectra-sts-v0.4

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3368
- Pearson: 0.9303
- Spearmanr: 0.9287

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.0345        | 1.0   | 730  | 0.3368          | 0.9303  | 0.9287    |
| 0.0343        | 2.0   | 1460 | 0.3368          | 0.9303  | 0.9287    |
| 0.0337        | 3.0   | 2190 | 0.3368          | 0.9303  | 0.9287    |
| 0.0345        | 4.0   | 2920 | 0.3368          | 0.9303  | 0.9287    |
| 0.0347        | 5.0   | 3650 | 0.3368          | 0.9303  | 0.9287    |

### Framework versions

- Transformers 4.10.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3


---
language: hu
metrics: rouge
---

[Paper](https://hlt.bme.hu/en/publ/foszt2oszt)

We publish an abstractive summarizer for Hungarian, an
encoder-decoder model initialized with [huBERT](huggingface.co/SZTAKI-HLT/hubert-base-cc), and fine-tuned on the
[ELTE.DH](https://elte-dh.hu/) corpus of former Hungarian news portals. The model produces fluent output in the correct topic, but it hallucinates frequently.
Our quantitative evaluation on automatic and human transcripts of news
(with automatic and human-made punctuation, [Tündik et al. (2019)](https://www.isca-speech.org/archive/interspeech_2019/tundik19_interspeech.html), [Tündik and Szaszák (2019)](https://www.isca-speech.org/archive/interspeech_2019/szaszak19_interspeech.html)) shows that the model is
robust with respect to errors in either automatic speech recognition or
automatic punctuation restoration. In fine-tuning and inference, we followed [a jupyter notebook by Patrick von
Platen](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb). Most hyper-parameters are the same as those by von Platen, but we
found it advantageous to change the minimum length of the summary to 8 word-
pieces (instead of 56), and the number of beams in beam search to 5 (instead
of 4). Our model was fine-tuned on a server of the [SZTAKI-HLT](hlt.bme.hu/) group, which kindly
provided access to it.


# Building a HuggingFace Transformer NLP Model

## Running this Repo


---
language: es
tags:
- token-classification
- nli
- pytorch
datasets:
- our own
pipeline_tag: token-classification
widget:
- text: "La recaudación del ministerio del interior fue de 20,000,000 euros así constatado por el artículo 24 de la Constitución Española."
---


---
tags:
- conversational
---

#Lelouch DialoGPT model


---
tags:
- conversational
---

# Tony Stark dialoGPT model


---
tags:
- conversational
---
# Rachel Green DialoGPT Model


---
tags:
- conversational
---
# Chandler Bing DialoGPT Model


---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: amazon_reviews_multi
      type: amazon_reviews_multi
      args: es
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9335
---

<!-- 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-base-bne-finetuned-amazon_reviews_multi

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2275
- Accuracy: 0.9335

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1909        | 1.0   | 1250 | 0.1717          | 0.9333   |
| 0.0932        | 2.0   | 2500 | 0.2275          | 0.9335   |

### Framework versions

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


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: cola
    metrics:
    - name: Matthews Correlation
      type: matthews_correlation
      value: 0.5451837431775948
---

<!-- 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-cola

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8508
- Matthews Correlation: 0.5452

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221        | 1.0   | 535  | 0.5370          | 0.4246               |
| 0.3462        | 2.0   | 1070 | 0.5157          | 0.5183               |
| 0.2332        | 3.0   | 1605 | 0.6324          | 0.5166               |
| 0.1661        | 4.0   | 2140 | 0.7616          | 0.5370               |
| 0.1263        | 5.0   | 2675 | 0.8508          | 0.5452               |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3