- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 15.037146%


# Wav2Vec2-Large-XLSR-53-Portuguese


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


## 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", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")

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

# Preprocessing the datasets.
# We need to read the aduio 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["speech"][:2], 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 Portuguese test data of Common Voice.


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

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

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\Ã¢â‚¬Å“\'\Ã¯Â¿Â½]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    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)

# Preprocessing the datasets.
# We need to read the aduio 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)

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


**Test Result (wer)**: 15.037146%


## Training


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py


model name: model-sid-voxforge-cv-cetuc-0


model author: joorock12


task: automatic-speech-recognition


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


tags:


- conversational


# Harry Potter DialoGPT Model


model name: DialoGPT-small-harrypotter


model author: DueLinx0402


task: conversational


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


Ver Escuadrón suicida Online HD (2021 ) En Español - Película Completa en Castellano, Gratis y Subtitulada.


Repelis pelicula Escuadrón suicida (2021) ver online, Escuadrón suicida (2021) nuevo cine descargar Película completa 1080p HD español , Escuadrón suicida (2021) Película completa Película completo online, Ver Escuadrón suicida (2021) Pelicula Completa Online En Español Latino | Repelis ver Escuadrón suicida película completa en Español y Latino, Subtitulado


♦ 𝐕𝐄𝐑 𝐀𝐇𝐎𝐑𝐀 𝐄𝐍 𝐇𝐃 ♦ https://bit.ly/3yDvoJK


♦ 𝐃𝐄𝐒𝐂𝐀𝐑𝐆𝐀𝐑 𝐄𝐍 𝐇𝐃 ♦ https://bit.ly/3yDvoJK


Escuadrón suicida Película Completa en Español,Escuadrón suicida netflix,Escuadrón suicida /cinepolis,Escuadrón suicida película completa en español netflix,Escuadrón suicida tráiler 2 español latino,Escuadrón suicida película completa en español latino facebook,ver Escuadrón suicida cinepolis


Ver Escuadrón suicida Película Completa HD — Descargar Mega


97 | 4K ULTRAHD | FULL HD (1080P) | SD


Antes de ver y seguir el cómo ver películas completas gratis y hablar en español ,entonces aquí está el detalle y la pieza de la historia que he delineado en película completa con subtítulos en español ,y a continuación hay una sinopsis de la historia y una colección de actores que protagonizaron las películas Escuadrón suicida


Escuadrón suicida 2021 De La Pelicula Completa En Español Latino


Categorías: Películas en español Películas de Argentina Películas de 2021 Películas de suspenso Películas policíacas Películas de cine de comedia Películas dirigidas por Ariel Winograd Películas ambientadas en Buenos Aires Películas dramáticas de Argentina Películas dramáticas de los años 2000 Películas rodadas en Buenos Aires Películas sociales y políticas Películas basadas en hechos reales


La verdad Ver O Descargar AQUI**


Ver Toda la calidad con GoogleVid


Ver Toda la calidad con OpenLoad


Descargar Toda la calidad con Mega


Esto es cómo descargar y ver Escuadrón suicida película completa En Español Latino


Seguramnte en más de una ocasión has buscado en Google “cómo Escuadrón suicida Película online gratis Película en Español” o “dónde ver pelis Escuadrón suicida


de estreno en castellano HD”.


Tal vez hasta has escrito en el buscador “las mejores Escuadrón suicida Película


online completas”, “Escuadrón suicida Película en Español latino” o “dónde puedo Escuadrón suicida Película gratis completas sin interrupciones”. No lo niegues. No eres el único. Todos los días, millones de personas intentan ver


Película online desde sus computadoras, laptops, smartphones, tablets o cual sea el dispositivo móvil de su preferencia. Sin embargo, la


navegación muchas veces termina en páginas web que no cumplen lo


prometido, que aseguran tener los últimos estrenos, pero que solo te


derivan de un site a otro, que te obligan a dar clic tras clic mientras


te llenan la pantalla de publicidad, para finalmente dirigirte hasta un


enlace que no funciona o que demora mucho en cargar. Además, la calidad


de imagen en estas páginas informales de cine es muy baja. Y repetimos,


informales. ¿Por qué? Porque son páginas piratas, que violan derechos de autor y que incluso pueden representar un riesgo. ¿Sabías que muchos de estos sitios esconden virus que podrían dañar tus dispositivos y hasta


robar tu información? En todo caso, muchas veces te obligan a


registrarte con tus cuentas de Facebook, Gmail u Outlook (Hotmail) para


que recién puedas comenzar a Escuadrón suicida pelis en Español latino. Por


tanto, te sugerimos solo visitar las siguientes plataformas, legales,


seguras y sacramentadas. Algunas incluso permiten escuchar y descargar


música MP3 gratis de tus artistas favoritos. ¿Cuáles son las mejores


páginas para Escuadrón suicida Película HD online gratis? En sí hay muchas de


este tipo, pero para efectos prácticos hemos elegido algunas de las más


populares en la red de redes. Ya dependerá de ti elegir la que mejor se


adapte a tus necesidades, ya sea por catálogo, por interfaz o velocidad


de Internet. Es decir, la que te permita ver Película gratis en Español con mayor facilidad. Incluso algunas tienen versiones para teléfono si


buscas dónde ver Película online móvil. ¿Quieres saber cuál es la mejor app para ver Película online? Esa ya no será ninguna preocupación de


aquí en adelante. ¿Qué velocidad necesitas para ver Película online? En estas páginas, con una conexión básica te alcanzará y sobrará. ¿Qué


plugin necesito para ver Película online? En la mayoría de casos,


¡ninguno! ¿Puedo encontrar dónde ver Película 3D online? Eso quizá está un poco más difícil. Escuadrón suicida Ver Película online gratis A continuación


todo lo que debes saber para Escuadrón suicida Película online Ojo, la lista


solo contempla páginas online legales, que albergan contenido de dominio público, independiente, liberado por sus mismos realizadores o con


licencias como Creative Commons. Es decir, si quieres ver Animales


Fantásticos 2 completa en Español o Escuadrón suicida, La chica en la telaraña,


Overlord, Escuadrón suicida o Escuadrón suicida con subtítulos, puede que te decepciones. Pero si


aún te interesan títulos de reciente estreno como estos, aquí puedes


revisar la cartelera de tu país de origen, incluidos horarios y precios


de entradas por cine. También descubre los próximos estrenos. Eso sí,


¿sabías que hasta puedes ver Película gratis en YouTube? Puedes


suscribirte al servicio de paga de YouTube para acceder a contenido


exclusivo que jamás has imaginado. Los tres primeros meses son gratis.


Classic Cin Escuadrón suicida es una de las páginas de curaduría de clásicos más populares en la red. El sitio está dedicado por completo a la


distribución de Película de libre acceso, liberadas de derechos de


autor. Por ejemplo, su catálogo de cine mudo es excepcional. ¿Lo mejor


de todo? Puedes ver las Película desde YouTube, por lo que navegar es


sencillísimo.


Ver Escuadrón suicida Película subtítulos Español Latino


castellano o con subtítulos en tu idioma y de todos los géneros: terror, comedia, acción, thriller, @VER AQUI ?> @VER AQUI ?> drama y


ciencia ficción. También series online o descargar pelis y más… mucho


más


VER Película Escuadrón suicida GRATIS en Español o con subtítulos en tu


idioma, en HD –y hasta en calidad de imagen 4K–y sin cortes ni


interrupciones es sencillo en las mejores páginas de cine y televisión


gratuitas del año. ¿Cuáles son exactamente estas webs? A continuación te detallamos todo lo que debes saber para ver las mejores pelis cuando


quieras, donde quieras y con quien quieras. Incluso aprenderás a


descargar Película gratis online de forma absolutamente legal y segura


este Película, sin necesidad de pagar mensualmente una suscripción a


servicios de streaming Escuadrón suicida premium como Netflix, HBO GO, Amazon Prime


Video, Hulu, Claro Video, Fox Premium, Movistar Play, DirecTV, Crackle o Blim, o de bajar apps de Google Play o App Store que no te ayudarán


mucho a satisfacer esa sed cinéfila y seriéfila. ¿No te es suficiente?


¿Quieres más trucos? También te enseñaremos a usar los sitios premium de Película Escuadrón suicida, series y documentales sin pagar absolutamente nada. Sí, es posible. ¿Y los códigos secretos de Netflix? También. ¿En cuanto a


series? Podrás ver series de acción, terror, aventura, telenovelas


mexicanas y turcas, doramas, anime y más, mucho más, como las más


recientes novedades: Narcos: México, The Sinner 2 y La reina del flow.


Incluso te contaremos qué Película están en la cartelera de los cines


del Perú, México, España, Estados Escuadrón suicida, Colombia, Argentina, Español y demás países del mundo. Sí, ¡los últimos estrenos! ¿Por ejemplo? Escuadrón suicida,


Escuadrón suicida, Escuadrón suicida, ¡Asu mare 3! y Escuadrón suicida ya están disponibles en las mejores salas.


KEYWORD SEARCH


Escuadrón suicida pelicula completa


Escuadrón suicida pelicula completa en español


Escuadrón suicida la película completa


Escuadrón suicida pelicula completa en español latino online


pelicula completa de Escuadrón suicida


Escuadrón suicida película completa


Escuadrón suicida la película completa en español


Escuadrón suicida pelicula completa en español latino


model name: Escuadron


model author: ziilpenkfyyvhyxpkz


tags:


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: wav2vec2-base-demo-colab
results: []


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


# wav2vec2-base-demo-colab


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


- Loss: 0.4500
- Wer: 0.3391


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5329        | 4.0   | 500  | 1.5741          | 1.0400 |
| 0.6432        | 8.0   | 1000 | 0.4571          | 0.4418 |
| 0.2214        | 12.0  | 1500 | 0.4381          | 0.3823 |
| 0.1294        | 16.0  | 2000 | 0.4706          | 0.3911 |
| 0.0868        | 20.0  | 2500 | 0.5252          | 0.3662 |
| 0.0616        | 24.0  | 3000 | 0.4828          | 0.3458 |
| 0.0461        | 28.0  | 3500 | 0.4500          | 0.3391 |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: wav2vec2-base-demo-colab


model author: asakawa


task: automatic-speech-recognition


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


language: pt
license: apache-2.0


widget:


- text: "O futuro de DI caiu 20 bps nesta manhã"
example_title: "Example 1"
- text: "O Nubank decidiu cortar a faixa de preço da oferta pública inicial (IPO) após revés no humor dos mercados internacionais com as fintechs."
example_title: "Example 2"
- text: "O Ibovespa acompanha correção do mercado e fecha com alta moderada"
example_title: "Example 3"


# FinBertPTBR : Financial Bert PT BR


FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.


## Usage


```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR")
model = AutoModel.from_pretrained("turing-usp/FinBertPTBR")
```


## Authors


- [Vinicius Carmo](https://www.linkedin.com/in/vinicius-cleves/)
- [Julia Pocciotti](https://www.linkedin.com/in/juliapocciotti/)
- [Luísa Heise](https://www.linkedin.com/in/lu%C3%ADsa-mendes-heise/)
- [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/)


model name: FinBertPTBR


model author: turing-usp


task: text-classification


tags: pytorch, bert, text-classification, pt, transformers, license:apache-2.0, infinity_compatible


# MarkupLM Large fine-tuned on WebSRC to allow Question Answering.


This model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft.


**Fine-tuned Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**


## Introduction (From Microsoft MarkupLM Large Model Card)


MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:


[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518)  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei


Fine-tuning args:
--per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4


## Training was performed on only a small subset of the WebSRC:


\
The number of total websites is 60


The train websites list is ['ga09']


The test websites list is []


The dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08']


The number of processed websites is 60


Inference test here may not work. Use the transformers markuplm branch from [NielsRogge transformers markuplm branch](https://github.com/NielsRogge/transformers/tree/modeling_markuplm)


After installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict)


     model = MarkupLMForQuestionAnswering.from_pretrained("FuriouslyAsleep/markuplm-large-finetuned-qa")

     tokenizer = MarkupLMTokenizer(
      vocab_file="vocab.json",
      merges_file="merges.txt",
      tags_dict= {"a": 0, "abbr": 1, "acronym": 2, "address": 3, "altGlyph": 4, "altGlyphDef": 5, "altGlyphItem": 6, "animate": 7, "animateColor": 8, "animateMotion": 9, "animateTransform": 10, "applet": 11, "area": 12, "article": 13, "aside": 14, "audio": 15, "b": 16, "base": 17, "basefont": 18, "bdi": 19, "bdo": 20, "bgsound": 21, "big": 22, "blink": 23, "blockquote": 24, "body": 25, "br": 26, "button": 27, "canvas": 28, "caption": 29, "center": 30, "circle": 31, "cite": 32, "clipPath": 33, "code": 34, "col": 35, "colgroup": 36, "color-profile": 37, "content": 38, "cursor": 39, "data": 40, "datalist": 41, "dd": 42, "defs": 43, "del": 44, "desc": 45, "details": 46, "dfn": 47, "dialog": 48, "dir": 49, "div": 50, "dl": 51, "dt": 52, "ellipse": 53, "em": 54, "embed": 55, "feBlend": 56, "feColorMatrix": 57, "feComponentTransfer": 58, "feComposite": 59, "feConvolveMatrix": 60, "feDiffuseLighting": 61, "feDisplacementMap": 62, "feDistantLight": 63, "feFlood": 64, "feFuncA": 65, "feFuncB": 66, "feFuncG": 67, "feFuncR": 68, "feGaussianBlur": 69, "feImage": 70, "feMerge": 71, "feMergeNode": 72, "feMorphology": 73, "feOffset": 74, "fePointLight": 75, "feSpecularLighting": 76, "feSpotLight": 77, "feTile": 78, "feTurbulence": 79, "fieldset": 80, "figcaption": 81, "figure": 82, "filter": 83, "font-face-format": 84, "font-face-name": 85, "font-face-src": 86, "font-face-uri": 87, "font-face": 88, "font": 89, "footer": 90, "foreignObject": 91, "form": 92, "frame": 93, "frameset": 94, "g": 95, "glyph": 96, "glyphRef": 97, "h1": 98, "h2": 99, "h3": 100, "h4": 101, "h5": 102, "h6": 103, "head": 104, "header": 105, "hgroup": 106, "hkern": 107, "hr": 108, "html": 109, "i": 110, "iframe": 111, "image": 112, "img": 113, "input": 114, "ins": 115, "kbd": 116, "keygen": 117, "label": 118, "legend": 119, "li": 120, "line": 121, "linearGradient": 122, "link": 123, "main": 124, "map": 125, "mark": 126, "marker": 127, "marquee": 128, "mask": 129, "math": 130, "menu": 131, "menuitem": 132, "meta": 133, "metadata": 134, "meter": 135, "missing-glyph": 136, "mpath": 137, "nav": 138, "nobr": 139, "noembed": 140, "noframes": 141, "noscript": 142, "object": 143, "ol": 144, "optgroup": 145, "option": 146, "output": 147, "p": 148, "param": 149, "path": 150, "pattern": 151, "picture": 152, "plaintext": 153, "polygon": 154, "polyline": 155, "portal": 156, "pre": 157, "progress": 158, "q": 159, "radialGradient": 160, "rb": 161, "rect": 162, "rp": 163, "rt": 164, "rtc": 165, "ruby": 166, "s": 167, "samp": 168, "script": 169, "section": 170, "select": 171, "set": 172, "shadow": 173, "slot": 174, "small": 175, "source": 176, "spacer": 177, "span": 178, "stop": 179, "strike": 180, "strong": 181, "style": 182, "sub": 183, "summary": 184, "sup": 185, "svg": 186, "switch": 187, "symbol": 188, "table": 189, "tbody": 190, "td": 191, "template": 192, "text": 193, "textPath": 194, "textarea": 195, "tfoot": 196, "th": 197, "thead": 198, "time": 199, "title": 200, "tr": 201, "track": 202, "tref": 203, "tspan": 204, "tt": 205, "u": 206, "ul": 207, "use": 208, "var": 209, "video": 210, "view": 211, "vkern": 212, "wbr": 213, "xmp": 214},
      add_prefix_space=True,)


Go to [https://github.com/uwts/ProjectRisk](https://github.com/uwts/ProjectRisk) for sample script.


model name: markuplm-large-finetuned-qa


model author: FuriouslyAsleep


task: question-answering


tags: pytorch, markuplm, question-answering, arxiv:2110.08518, transformers, autonlp_compatible


language: ar
thumbnail: https://raw.githubusercontent.com/mawdoo3/Multi-dialect-Arabic-BERT/master/multidialct_arabic_bert.png
datasets:


- nadi


# Multi-dialect-Arabic-BERT


This is a repository of Multi-dialect Arabic BERT model.


By [Mawdoo3-AI](https://ai.mawdoo3.com/).


### About our Multi-dialect-Arabic-BERT model


Instead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT) and trained it on 10M arabic tweets from the unlabled data of [The Nuanced Arabic Dialect Identification (NADI) shared task](https://sites.google.com/view/nadi-shared-task).


### To cite this work


```
@misc{talafha2020multidialect,
    title={Multi-Dialect Arabic BERT for Country-Level Dialect Identification},
    author={Bashar Talafha and Mohammad Ali and Muhy Eddin Za'ter and Haitham Seelawi and Ibraheem Tuffaha and Mostafa Samir and Wael Farhan and Hussein T. Al-Natsheh},
    year={2020},
    eprint={2007.05612},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


### Usage


The model weights can be loaded using `transformers` library by HuggingFace.


```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic")
model = AutoModel.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic")
```


Example using `pipeline`:


```python
from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model="bashar-talafha/multi-dialect-bert-base-arabic ",
    tokenizer="bashar-talafha/multi-dialect-bert-base-arabic "
)

fill_mask(" سافر الرحالة من مطار [MASK] ")
```


```
[{'sequence': '[CLS] سافر الرحالة من مطار الكويت [SEP]', 'score': 0.08296813815832138, 'token': 3226},
 {'sequence': '[CLS] سافر الرحالة من مطار دبي [SEP]', 'score': 0.05123933032155037, 'token': 4747},
 {'sequence': '[CLS] سافر الرحالة من مطار مسقط [SEP]', 'score': 0.046838656067848206, 'token': 13205},
 {'sequence': '[CLS] سافر الرحالة من مطار القاهرة [SEP]', 'score': 0.03234650194644928, 'token': 4003},
 {'sequence': '[CLS] سافر الرحالة من مطار الرياض [SEP]', 'score': 0.02606341242790222, 'token': 2200}]
```


### Repository


Please check the [original repository](https://github.com/mawdoo3/Multi-dialect-Arabic-BERT) for more information.


model name: multi-dialect-bert-base-arabic


model author: bashar-talafha


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ar, dataset:nadi, arxiv:2007.05612, transformers, autonlp_compatible, infinity_compatible


language:


- vi
tags:
- pytorch
- causal-lm
- text-generation


# GPT-J 6B for Vietnamese News


Details will be available soon.


For more information, please contact anhduongng.1001@gmail.com / imthanhlv@gmail.com / nguyenvulebinh@gmail.com.


model name: gpt-j-6B-vietnamese-news


model author: VietAI


task: text-generation


tags: pytorch, gptj, text-generation, vi, transformers, causal-lm


- vi
tags:
- pytorch
- causal-lm
- gpt


# GPT-Neo 1.3B for Vietnamese News


model name: gpt-neo-1.3B-vietnamese-news


tags: pytorch, gpt_neo, text-generation, vi, transformers, causal-lm, gpt


tags:


- conversational


#Phoebe From Friends


model name: DialoGPT-small-Phoebe


model author: wanderer


task: conversational


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


language: english
widget:


- text: "They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,"
- text: "\" We 'll talk go by now ; \" says Shucksmith ;"
- text: "\" Warren Gatland is a professional person and it wasn 't a case of 's I 'll phone my mate Rob up to if he wants a coaching job ' , he would done a fair amount of homework about , \" Howley air said ."


This model can be used to more accurately detokenize the moses tokenizer (it does a better job with certain lossy quotes and things)


batched usage:


```python

sentences = [
    "They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,",
    "\" We 'll talk go by now ; \" says Shucksmith ;",
    "He 'll enjoy it more now that this he be dead , if put 'll pardon the expression .",
    "I think you 'll be amazed at this way it finds ,",
    "Michigan voters ^ are so frightened of fallen in permanent economic collapse that they 'll grab onto anything .",
    "You 'll finding outs episode 4 .",
    "\" Warren Gatland is a professional person and it wasn 't a case of 's I 'll phone my mate Rob up to if he wants a coaching job ' , he would done a fair amount of homework about , \" Howley air said .",
    "You can look at the things I 'm saying about my record and about the events of campaign and history and you 'll find if now and and then I miss a words or I get something slightly off , I 'll correct it , acknowledge where it are wrong .",
    "Wonder if 'll alive to see .",
    "We 'll have to combine and a numbered of people ."
]

def sentences_to_input_tokens(sentences):
    all_tokens = []
    max_length = 0
    sents_tokens = []
    iids = tokenizer(sentences)
    for sent_tokens in iids['input_ids']:
        sents_tokens.append(sent_tokens)

        if len(sent_tokens) > max_length:
            max_length = len(sent_tokens)

        attention_mask = [1] * len(sent_tokens)
        pos_ids = list(range(len(sent_tokens)))

        encoding = {
            "iids": sent_tokens,
            "am": attention_mask,
            "pos": pos_ids
        }

        all_tokens.append(encoding)

    input_ids = []
    attention_masks = []
    position_ids = []
    for i in range(len(all_tokens)):

        encoding = all_tokens[i]

        pad_len = max_length - len(encoding['iids'])
        attention_masks.append(encoding['am'] + [0] * pad_len)
        position_ids.append(encoding['pos'] + [0] * pad_len)
        input_ids.append(encoding['iids'] + [tokenizer.pad_token_id] * pad_len)

    encoding = {
        "input_ids": torch.tensor(input_ids).to(device),
        "attention_mask": torch.tensor(attention_masks).to(device),
        "position_ids": torch.tensor(position_ids).to(device)
    }

    return encoding, sents_tokens

def run_token_predictor_sentences(sentences):
    encoding, at = sentences_to_input_tokens(sentences)
    predictions = model(**encoding)[0].cpu().tolist()
    outstrs = []

    for i in range(len(predictions)):
        outstr = ""
        for p in zip(tokenizer.convert_ids_to_tokens(at[i][1:-1]), predictions[i][1:-1]):
            if not "▁" in p[0]:
                outstr+=p[0]
            else:
                if p[1][0] > p[1][1]:
                    outstr+=p[0].replace("▁", " ")
                else:
                    outstr+=p[0].replace("▁", "")
        outstrs.append(outstr.strip())
    return outstrs

outs = run_token_predictor_sentences(sentences)
for p in zip(outs, sentences):
    print(p[1])
    print(p[0])
    print('\n------\n')

```


model name: detok-deberta-xl


model author: dsilin


task: token-classification


tags: pytorch, deberta-v2, token-classification, english, transformers, autonlp_compatible


## language: tr


# Turkish Language Models with Huggingface's Transformers


As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).


# Turkish ELECTRA-Small-discriminator (uncased)


This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layer size trained on uncased Turkish dataset.


## Usage


Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.


```python
from transformers import AutoModel, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-uncased-discriminator", do_lower_case=False)

model = AutoModelWithLMHead.from_pretrained("loodos/electra-small-turkish-uncased-discriminator")

normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)

tokenizer.tokenize(normalized_text)
```


### Notes on Tokenizers


Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.


1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.


2- Python's default `string.lower()` and `string.upper()` make the conversions


- "I" and "İ" to 'i'
- 'i' and 'ı' to 'I'


respectively. However, in Turkish, 'I' and 'İ' are two different letters.


We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).


## Details and Contact


You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).


## Acknowledgments


Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.


model name: electra-small-turkish-uncased-discriminator


model author: loodos


tags: pytorch, tf, electra, pretraining, tr, transformers, infinity_compatible


## language: tr


# Turkish ELECTRA-Base-discriminator (uncased/64k)


This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset. This version has a vocab of size 64k, different from default 32k.


## Usage


```python
from transformers import AutoModel, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator", do_lower_case=False)

model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator")

normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)

tokenizer.tokenize(normalized_text)
```


### Notes on Tokenizers


model name: electra-base-turkish-64k-uncased-discriminator


# Turkish Language Models with Huggingface's Transformers


As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).


# Turkish ALBERT-Base (uncased)


This is ALBERT-Base model which has 12 repeated encoder layers with 768 hidden layer size trained on uncased Turkish dataset.


Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.


```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("loodos/albert-base-turkish-uncased", do_lower_case=False, keep_accents=True)

model = AutoModel.from_pretrained("loodos/albert-base-turkish-uncased")

normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)

tokenizer.tokenize(normalized_text)
```


Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.


1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.


2- Python's default `string.lower()` and `string.upper()` make the conversions


- "I" and "İ" to 'i'
- 'i' and 'ı' to 'I'


respectively. However, in Turkish, 'I' and 'İ' are two different letters.


We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).


## Details and Contact


You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).


## Acknowledgments


Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.


model name: albert-base-turkish-uncased


model author: loodos


tags: pytorch, tf, albert, tr, transformers, infinity_compatible


## language: tr


# Turkish ELECTRA-Base-discriminator (uncased)


This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset.


## Usage


Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.


```python
from transformers import AutoModel, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-uncased-discriminator", do_lower_case=False)

model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-uncased-discriminator")

normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)

tokenizer.tokenize(normalized_text)
```


### Notes on Tokenizers


model name: electra-base-turkish-uncased-discriminator


tags: pytorch, tf, electra, pretraining, tr, transformers, infinity_compatible


# Turkish Language Models with Huggingface's Transformers


As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).


# Turkish BERT-Base (cased)


This is BERT-Base model which has 12 encoder layers with 768 hidden layer size trained on cased Turkish dataset.


Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.


```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("loodos/bert-base-turkish-cased")

model = AutoModel.from_pretrained("loodos/bert-base-turkish-cased")
```


## Details and Contact


## Acknowledgments


model name: bert-base-turkish-cased


model author: loodos


tags: pytorch, tf, jax, bert, tr, transformers, infinity_compatible


## language: tr


# Turkish BERT-Base (uncased)


This is BERT-Base model which has 12 encoder layers with 768 hidden layer size trained on uncased Turkish dataset.


## Usage


```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("loodos/bert-base-turkish-uncased", do_lower_case=False)

model = AutoModel.from_pretrained("loodos/bert-base-turkish-uncased")

normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)

tokenizer.tokenize(normalized_text)
```


### Notes on Tokenizers


Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.


1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.


2- Python's default `string.lower()` and `string.upper()` make the conversions


- "I" and "İ" to 'i'
- 'i' and 'ı' to 'I'


respectively. However, in Turkish, 'I' and 'İ' are two different letters.


We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).


## Details and Contact


You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).


## Acknowledgments


Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.


model name: bert-base-turkish-uncased


model author: loodos


## language: tr


# Turkish ELECTRA-Small-discriminator (cased)


This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layers size trained on cased Turkish dataset.


## Usage


Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.


```python
from transformers import AutoModel, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-cased-discriminator")

model = AutoModelWithLMHead.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
```


model name: electra-small-turkish-cased-discriminator


tags: pytorch, tf, electra, pretraining, tr, transformers, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9840500738716699


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


# distilbert-base-uncased-finetuned-ner


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


- Loss: 0.0615
- Precision: 0.9251
- Recall: 0.9363
- F1: 0.9307
- Accuracy: 0.9841


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2473        | 1.0   | 878  | 0.0714          | 0.9154    | 0.9178 | 0.9166 | 0.9808   |
| 0.0522        | 2.0   | 1756 | 0.0620          | 0.9201    | 0.9348 | 0.9274 | 0.9832   |
| 0.031         | 3.0   | 2634 | 0.0615          | 0.9251    | 0.9363 | 0.9307 | 0.9841   |


### Framework versions


- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


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


model author: zhihao


task: token-classification


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


language: en
license: mit
datasets:


- AI4Bharat IndicNLP Corpora


# IndicBERT


IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R etc.) while it also achieves a performance on-par or better than these models.


The 12 languages covered by IndicBERT are: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.


The code can be found [here](https://github.com/divkakwani/indic-bert). For more information, checkout our [project page](https://indicnlp.ai4bharat.org/) or our [paper](https://indicnlp.ai4bharat.org/papers/arxiv2020_indicnlp_corpus.pdf).


## Pretraining Corpus


We pre-trained indic-bert on AI4Bharat's monolingual corpus. The corpus has the following distribution of languages:


| Language          | as     | bn     | en     | gu     | hi     | kn     |         |
| ----------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------- |
| **No. of Tokens** | 36.9M  | 815M   | 1.34B  | 724M   | 1.84B  | 712M   |         |
| **Language**      | **ml** | **mr** | **or** | **pa** | **ta** | **te** | **all** |
| **No. of Tokens** | 767M   | 560M   | 104M   | 814M   | 549M   | 671M   | 8.9B    |


## Evaluation Results


IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our [official repo](https://github.com/divkakwani/indic-bert)


#### IndicGLUE


Task | mBERT | XLM-R | IndicBERT
-----| ----- | ----- | ------
News Article Headline Prediction | 89.58 | 95.52 | **95.87**
Wikipedia Section Title Prediction| **73.66** | 66.33 | 73.31
Cloze-style multiple-choice QA | 39.16 | 27.98 | **41.87**
Article Genre Classification | 90.63 | 97.03 | **97.34**
Named Entity Recognition (F1-score) | **73.24** | 65.93 | 64.47
Cross-Lingual Sentence Retrieval Task | 21.46 | 13.74 | **27.12**
Average | 64.62 | 61.09 | **66.66**


#### Additional Tasks


Task | Task Type | mBERT | XLM-R | IndicBERT
-----| ----- | ----- | ------ | -----
BBC News Classification | Genre Classification | 60.55 | **75.52** | 74.60
IIT Product Reviews | Sentiment Analysis | 74.57 | **78.97** | 71.32
IITP Movie Reviews | Sentiment Analaysis | 56.77 | **61.61** | 59.03
Soham News Article | Genre Classification | 80.23 | **87.6** | 78.45
Midas Discourse | Discourse Analysis | 71.20 | **79.94** | 78.44
iNLTK Headlines Classification | Genre Classification | 87.95 | 93.38 | **94.52**
ACTSA Sentiment Analysis | Sentiment Analysis | 48.53 | 59.33 | **61.18**
Winograd NLI | Natural Language Inference | 56.34 | 55.87 | **56.34**
Choice of Plausible Alternative (COPA) | Natural Language Inference | 54.92 | 51.13 | **58.33**
Amrita Exact Paraphrase | Paraphrase Detection | **93.81** | 93.02 | 93.75
Amrita Rough Paraphrase | Paraphrase Detection | 83.38 | 82.20 | **84.33**
Average |  |  69.84 | **74.42** | 73.66


\* Note: all models have been restricted to a max_seq_length of 128.


## Downloads


The model can be downloaded [here](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/models/indic-bert-v1.tar.gz). Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from [Huggingface](https://huggingface.co/ai4bharat/indic-bert).


## Citing


If you are using any of the resources, please cite the following article:


```
@inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
```


We would like to hear from you if:


- You are using our resources. Please let us know how you are putting these resources to use.
- You have any feedback on these resources.


## License


The IndicBERT code (and models) are released under the MIT License.


## Contributors


- Divyanshu Kakwani
- Anoop Kunchukuttan
- Gokul NC
- Satish Golla
- Avik Bhattacharyya
- Mitesh Khapra
- Pratyush Kumar


This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org).


## Contact


- Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com))
- Mitesh Khapra ([miteshk@cse.iitm.ac.in](mailto:miteshk@cse.iitm.ac.in))
- Pratyush Kumar ([pratyush@cse.iitm.ac.in](mailto:pratyush@cse.iitm.ac.in))


model name: indic-bert-512


model author: vaishnavi


tags: pytorch, albert, en, dataset:AI4Bharat IndicNLP Corpora, transformers, license:mit, infinity_compatible


license: gpl-3.0
tags:


- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Bob Dillan beit Maríu Markan á barkann.
model-index:
- name: IceBERT-finetuned-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
    - name: Precision
type: precision
value: 0.8873049035270985
    - name: Recall
type: recall
value: 0.8627076114231091
    - name: F1
type: f1
value: 0.8748333939173634
    - name: Accuracy
type: accuracy
value: 0.9848076353832492


<!-- 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. -->


# IceBERT-finetuned-ner


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


- Loss: 0.0783
- Precision: 0.8873
- Recall: 0.8627
- F1: 0.8748
- Accuracy: 0.9848


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0539        | 1.0   | 2904 | 0.0768          | 0.8732    | 0.8453 | 0.8590 | 0.9833   |
| 0.0281        | 2.0   | 5808 | 0.0737          | 0.8781    | 0.8492 | 0.8634 | 0.9838   |
| 0.0166        | 3.0   | 8712 | 0.0783          | 0.8873    | 0.8627 | 0.8748 | 0.9848   |


### Framework versions


- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: IceBERT-finetuned-ner


model author: bergurth


task: token-classification


tags: pytorch, tensorboard, roberta, token-classification, dataset:mim_gold_ner, transformers, license:gpl-3.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: agpl-3.0
tags:


- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Bónus feðgarnir Jóhannes Jónsson og Jón Ásgeir Jóhannesson opnuðu fyrstu Bónusbúðina í 400 fermetra húsnæði við Skútuvog laugardaginn 8. apríl 1989
model-index:
- name: XLMR-ENIS-finetuned-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
    - name: Precision
type: precision
value: 0.861851332398317
    - name: Recall
type: recall
value: 0.8384309266628767
    - name: F1
type: f1
value: 0.849979828251974
    - name: Accuracy
type: accuracy
value: 0.9830620929487668


# XLMR-ENIS-finetuned-ner


This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0938
- Precision: 0.8619
- Recall: 0.8384
- F1: 0.8500
- Accuracy: 0.9831


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0574        | 1.0   | 2904 | 0.0983          | 0.8374    | 0.8061 | 0.8215 | 0.9795   |
| 0.0321        | 2.0   | 5808 | 0.0991          | 0.8525    | 0.8235 | 0.8378 | 0.9811   |
| 0.0179        | 3.0   | 8712 | 0.0938          | 0.8619    | 0.8384 | 0.8500 | 0.9831   |


### Framework versions


model name: XLMR-ENIS-finetuned-ner


model author: bergurth


task: token-classification


tags: pytorch, tensorboard, xlm-roberta, token-classification, dataset:mim_gold_ner, transformers, license:agpl-3.0, generated_from_trainer, model-index, autonlp_compatible


# SpERT


SpERT is the Relation Extraction model [(SpERT)Span-based Entity and Relation Transformer](https://github.com/lavis-nlp/spert).This is the model trained with CoNLL04 Dataset.


## Use


## References


```
Markus Eberts, Adrian Ulges. Span-based Joint Entity and Relation Extraction with Transformer Pre-training. 24th European Conference on Artificial Intelligence, 2020.
```


model name: spert


model author: Zichuu


tags: pytorch, bert, transformers, infinity_compatible


tags:


- conversational


# Monika Discord Chatbot


model name: monikAI


model author: Kryptone


task: conversational


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


# MoniKA unstable


model name: monikAI-Unstable


# Buro discord bot


model name: Burobot


# Rin chatbot


model name: RinAI


language:


- en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
metrics:
- rouge


# Try out in the Hosted inference API


In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.


# Model Loading


The model (based on a GPT2 base architecture) can be loaded in the following way:


```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop46")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop46")
```


# Example Use


```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```


# Example output


```
[Score: -0.153]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the Red Planet.
[Score: -0.154]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the planet.
[Score: -0.154]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls have been spotted elsewhere on the Red Planet.
[Score: -0.195]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the Red Planet. A rockfalls have been spotted everywhere on the red planet.
```


# Github repo


You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop


model name: summary_loop46


model author: philippelaban


task: summarization


tags: pytorch, gpt2, text-generation, en, dataset:cnn_dailymail, transformers, summarization, license:apache-2.0, autonlp_compatible


language:


- en
tags:
- simplification
license: apache-2.0
datasets:
- cnn_dailymail
widget:
- text: "A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>"
example_title: "Example 1"


# Try out in the Hosted inference API


In the right panel, you can try the model (although it only handles a short sequence length).
Feel free to try Example 1, and modify it to inspect model ability.


# Model Loading


The model can be loaded in the following way:


```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("philippelaban/keep_it_simple")
kis_model = AutoModelForCausalLM.from_pretrained("philippelaban/keep_it_simple")
```


# Example use


And then used by first inputting a paragraph for simplification, followed by a `bos_token` to indicate to the model to start simplifying.
Imagine we want to simplify the following paragraph:


```
A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space
by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6.
The extremely high precision required to carry out the mission thrilled many in Japan,
who said they took pride in its success.
```


The following code can be run:


```
paragraph = """A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6. The extremely high precision required to carry out the mission thrilled many in Japan, who said they took pride in its success."""

start_id = tokenizer.bos_token_id
tokenized_paragraph = [(tokenizer.encode(text=paragraph) + [start_id])]
input_ids = torch.LongTensor(tokenized_paragraph)

output_ids = kis_model.generate(input_ids, max_length=150, num_beams=4, do_sample=True, num_return_sequences=8)
output_ids = output_ids[:, input_ids.shape[1]:]
output = tokenizer.batch_decode(output_ids)
output = [o.replace(tokenizer.eos_token, "") for o in output]

for o in output:
    print("----")
    print(o)
```


# Example output


When run, an output similar to the following should be obtained:


A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise, said many in Japan, and they took pride in its success.


A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise and well thought-out, said many in Japan, who took pride in the mission.


A small capsule containing soil samples that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was designed to test the performance of the country's space fleet, which many said took pride in its success.


A small capsule containing soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 probe was followed by a landing on the Outback. The precise timing of the mission thrilled many in Japan, who said they took pride in its success.


# Github repo


You can access more information, access to the scoring function, the training script, or an example training log on the Github repo:
https://github.com/tingofurro/keep_it_simple


model name: keep_it_simple


model author: philippelaban


task: text-generation


tags: pytorch, gpt2, text-generation, en, dataset:cnn_dailymail, transformers, simplification, license:apache-2.0


language:


- en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail


# Try out in the Hosted inference API


In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.


# Model Loading


The model (based on a GPT2 base architecture) can be loaded in the following way:


```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop10")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop10")
```


# Example Use


```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```


# Example output


```
[Score: -0.084]  Here's what you need to know about rockfalls
[Score: -0.087]  Here's what you need to know about these tracks
[Score: -0.091]  Here's what we know so far about these tracks
[Score: -0.101]  Here's what you need to know about rockfall
```


# Github repo


You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop


model name: summary_loop10


model author: philippelaban


task: summarization


tags: pytorch, gpt2, text-generation, en, dataset:cnn_dailymail, transformers, summarization, license:apache-2.0, autonlp_compatible


language:


```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop46")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop46")
```


```
[Score: -0.113]  These tracks have been spotted elsewhere in the solar system, including on the red planet, and no one is there to see it, does it leave a trace? Yes, and
[Score: -0.119]  Now researchers have spotted thousands of tracks on the red planet created by tumbling boulders in Mars, and no one is there to see it, does it leave a trace?
[Score: -0.214]  Here are answers to those questions posed by scientists investigating the tracks discovered by scientists examining the tracks discovered by scientists exploring the tracks discovered by scientists exploring the tracks discovered by scientists exploring the
[Score: -0.388]  These are the kinds of questions swirling around whether these tracks exist on Mars, and whether they should be noticed sooner rather than later. Here are some answers: -- The tracks detected
```


model name: summary_loop24


tags:


- conversational


#GPT-2 model trained on Dana Scully's dialog.


model name: DialoGPT-small-scully


model author: hireddivas


task: conversational


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


GPT-2 chatbot - talk to Fox Mulder


model name: dialoGPT-small-mulder


GPT-2 chatbot - talk to Ray Smuckles


model name: DialoGPT-small-ray


GPT-2 chatbot - talk to Sonic


model name: dialoGPT-small-sonic


GPT-2 model trained on Phil from Eastenders


model name: dialoGPT-small-phil


- generated_from_trainer
model-index:
- name: translation-en-pt-t5-Duolingo-Subtitles
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. -->


# translation-en-pt-t5-Duolingo-Subtitles


This model is a fine-tuned version of [unicamp-dl/translation-en-pt-t5](https://huggingface.co/unicamp-dl/translation-en-pt-t5) on an unknown dataset.
It achieves the following results on the evaluation set:


- eval_loss: 0.7469
- eval_bleu: 39.9403
- eval_gen_len: 8.98
- eval_runtime: 997.6641
- eval_samples_per_second: 150.351
- eval_steps_per_second: 4.699
- epoch: 0.49
- step: 56000


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Framework versions


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


model name: translation-en-pt-t5-Duolingo-Subtitles


model author: ykliu1892


task: text2text-generation


tags: pytorch, tensorboard, t5, text2text-generation, transformers, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-zh-de-tuned-Tatoeba-small
results: []


# opus-mt-zh-de-tuned-Tatoeba-small


This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md.
It achieves the following results on the evaluation set:


- Loss: 2.2703
- Bleu: 16.504
- Gen Len: 16.6531


## Model description


More information needed


## Intended uses & limitations


Prefix used during fine-tuning: "将中文翻译成德语". This prefix is also recommended in prediction.


## Training and evaluation data


## Training procedure


### Training hyperparameters


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


### Training results


| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 2.7229        | 0.24  | 16000  | 2.5605          | 14.1956 | 16.2206 |
| 2.5988        | 0.49  | 32000  | 2.4447          | 14.8619 | 16.2726 |
| 2.515         | 0.73  | 48000  | 2.3817          | 15.3212 | 16.2823 |
| 2.4683        | 0.97  | 64000  | 2.3367          | 15.9043 | 16.7138 |
| 2.3873        | 1.22  | 80000  | 2.3115          | 16.1037 | 16.6369 |
| 2.3792        | 1.46  | 96000  | 2.2919          | 16.2957 | 16.6304 |
| 2.3626        | 1.7   | 112000 | 2.2790          | 16.2995 | 16.6235 |
| 2.3353        | 1.95  | 128000 | 2.2703          | 16.504  | 16.6531 |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: opus-mt-zh-de-tuned-Tatoeba-small


model author: ykliu1892


task: text2text-generation


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


tags:


- generated_from_trainer
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles
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. -->


# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles


This model was trained from scratch on an unknown dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


### Framework versions


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


model name: translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles


model author: ykliu1892


task: text2text-generation


tags: pytorch, tensorboard, t5, text2text-generation, transformers, generated_from_trainer, model-index, autonlp_compatible


tags:


- generated_from_trainer
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo-Subtitles
results: []


# translation-en-pt-t5-finetuned-Duolingo-Subtitles


This model is a fine-tuned version of [ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles](https://huggingface.co/ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles) on an unknown dataset.
It achieves the following results on the evaluation set:


- eval_loss: 1.0932
- eval_bleu: 28.4269
- eval_gen_len: 8.816
- eval_runtime: 1404.5946
- eval_samples_per_second: 106.792
- eval_steps_per_second: 3.338
- epoch: 0.52
- step: 28000


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


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


### Framework versions


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


model name: translation-en-pt-t5-finetuned-Duolingo-Subtitles


model author: ykliu1892


task: text2text-generation


tags:


- generated_from_trainer
metrics:
- bleu
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo
results: []


# translation-en-pt-t5-finetuned-Duolingo


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.7362
- Bleu: 39.4725
- Gen Len: 9.002


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.5429        | 0.24  | 9000  | 0.7461          | 39.4744 | 9.0     |
| 0.5302        | 0.48  | 18000 | 0.7431          | 39.7559 | 8.97    |
| 0.5309        | 0.72  | 27000 | 0.7388          | 39.6751 | 8.998   |
| 0.5336        | 0.96  | 36000 | 0.7362          | 39.4725 | 9.002   |


### Framework versions


model name: translation-en-pt-t5-finetuned-Duolingo


model author: ykliu1892


task: text2text-generation


tags:


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


model-index:


- name: shirt_identifier
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.6875


# shirt_identifier


Autogenerated by HuggingPics🤗🖼


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


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


## Example Images


#### Big Check shirt


![Big Check shirt](images/Big_Check_shirt.jpg)


#### Formal Shirt


![Formal Shirt](images/Formal_Shirt.jpg)


#### casual shirt


![casual shirt](images/casual_shirt.jpg)


#### denim shirt


![denim shirt](images/denim_shirt.jpg)


model name: shirt_identifier


model author: b25mayank3


task: image-classification


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


tags:


- conversational


# Harry Botter Model


model name: Genesis-harrybotter


model author: Ilyabarigou


task: conversational


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


## language: no
thumbnail: https://i.imgur.com/QqSEC5I.png


# Norwegian Electra


![Image of norwegian electra](https://i.imgur.com/QqSEC5I.png)


Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)


Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.


# Model


## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators


Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning


- https://openreview.net/pdf?id=r1xMH1BtvB
- https://github.com/google-research/electra


# Acknowledgments


### TensorFlow Research Cloud


Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤


- https://www.tensorflow.org/tfrc


#### OSCAR corpus


- https://oscar-corpus.com/


#### OPUS


- http://opus.nlpl.eu/
- http://www.opensubtitles.org/


model name: electra-base-norwegian-uncased-discriminator


model author: ViktorAlm


tags: pytorch, tf, electra, pretraining, no, transformers, infinity_compatible


tags:


- conversational


#Ally DialoGPT Model


model name: DialoGPT-small-ally


model author: SarahhhUwU


task: conversational


language:


- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"


model name: convbert-base-turkish-mc4-cased-snli_tr


model author: emrecan


task: zero-shot-classification


tags: pytorch, convbert, text-classification, tr, dataset:nli_tr, transformers, zero-shot-classification, nli, license:apache-2.0


model name: distilbert-base-turkish-cased-snli_tr


tags: pytorch, distilbert, text-classification, tr, dataset:nli_tr, transformers, zero-shot-classification, nli, license:apache-2.0, infinity_compatible


- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"


<!-- 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-turkish-cased_allnli_tr


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


- Loss: 0.6481
- Accuracy: 0.7381


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.94          | 0.03  | 1000  | 0.9074          | 0.5813   |
| 0.8102        | 0.07  | 2000  | 0.8802          | 0.5949   |
| 0.7737        | 0.1   | 3000  | 0.8491          | 0.6155   |
| 0.7576        | 0.14  | 4000  | 0.8283          | 0.6261   |
| 0.7286        | 0.17  | 5000  | 0.8150          | 0.6362   |
| 0.7162        | 0.2   | 6000  | 0.7998          | 0.6400   |
| 0.7092        | 0.24  | 7000  | 0.7830          | 0.6565   |
| 0.6962        | 0.27  | 8000  | 0.7653          | 0.6629   |
| 0.6876        | 0.31  | 9000  | 0.7630          | 0.6687   |
| 0.6778        | 0.34  | 10000 | 0.7475          | 0.6739   |
| 0.6737        | 0.37  | 11000 | 0.7495          | 0.6781   |
| 0.6712        | 0.41  | 12000 | 0.7350          | 0.6826   |
| 0.6559        | 0.44  | 13000 | 0.7274          | 0.6897   |
| 0.6493        | 0.48  | 14000 | 0.7248          | 0.6902   |
| 0.6483        | 0.51  | 15000 | 0.7263          | 0.6858   |
| 0.6445        | 0.54  | 16000 | 0.7070          | 0.6978   |
| 0.6467        | 0.58  | 17000 | 0.7083          | 0.6981   |
| 0.6332        | 0.61  | 18000 | 0.6996          | 0.7004   |
| 0.6288        | 0.65  | 19000 | 0.6979          | 0.6978   |
| 0.6308        | 0.68  | 20000 | 0.6912          | 0.7040   |
| 0.622         | 0.71  | 21000 | 0.6904          | 0.7092   |
| 0.615         | 0.75  | 22000 | 0.6872          | 0.7094   |
| 0.6186        | 0.78  | 23000 | 0.6877          | 0.7075   |
| 0.6183        | 0.82  | 24000 | 0.6818          | 0.7111   |
| 0.6115        | 0.85  | 25000 | 0.6856          | 0.7122   |
| 0.608         | 0.88  | 26000 | 0.6697          | 0.7179   |
| 0.6071        | 0.92  | 27000 | 0.6727          | 0.7181   |
| 0.601         | 0.95  | 28000 | 0.6798          | 0.7118   |
| 0.6018        | 0.99  | 29000 | 0.6854          | 0.7071   |
| 0.5762        | 1.02  | 30000 | 0.6697          | 0.7214   |
| 0.5507        | 1.05  | 31000 | 0.6710          | 0.7185   |
| 0.5575        | 1.09  | 32000 | 0.6709          | 0.7226   |
| 0.5493        | 1.12  | 33000 | 0.6659          | 0.7191   |
| 0.5464        | 1.15  | 34000 | 0.6709          | 0.7232   |
| 0.5595        | 1.19  | 35000 | 0.6642          | 0.7220   |
| 0.5446        | 1.22  | 36000 | 0.6709          | 0.7202   |
| 0.5524        | 1.26  | 37000 | 0.6751          | 0.7148   |
| 0.5473        | 1.29  | 38000 | 0.6642          | 0.7209   |
| 0.5477        | 1.32  | 39000 | 0.6662          | 0.7223   |
| 0.5522        | 1.36  | 40000 | 0.6586          | 0.7227   |
| 0.5406        | 1.39  | 41000 | 0.6602          | 0.7258   |
| 0.54          | 1.43  | 42000 | 0.6564          | 0.7273   |
| 0.5458        | 1.46  | 43000 | 0.6780          | 0.7213   |
| 0.5448        | 1.49  | 44000 | 0.6561          | 0.7235   |
| 0.5418        | 1.53  | 45000 | 0.6600          | 0.7253   |
| 0.5408        | 1.56  | 46000 | 0.6616          | 0.7274   |
| 0.5451        | 1.6   | 47000 | 0.6557          | 0.7283   |
| 0.5385        | 1.63  | 48000 | 0.6583          | 0.7295   |
| 0.5261        | 1.66  | 49000 | 0.6468          | 0.7325   |
| 0.5364        | 1.7   | 50000 | 0.6447          | 0.7329   |
| 0.5294        | 1.73  | 51000 | 0.6429          | 0.7320   |
| 0.5332        | 1.77  | 52000 | 0.6508          | 0.7272   |
| 0.5274        | 1.8   | 53000 | 0.6492          | 0.7326   |
| 0.5286        | 1.83  | 54000 | 0.6470          | 0.7318   |
| 0.5359        | 1.87  | 55000 | 0.6393          | 0.7354   |
| 0.5366        | 1.9   | 56000 | 0.6445          | 0.7367   |
| 0.5296        | 1.94  | 57000 | 0.6413          | 0.7313   |
| 0.5346        | 1.97  | 58000 | 0.6393          | 0.7315   |
| 0.5264        | 2.0   | 59000 | 0.6448          | 0.7357   |
| 0.4857        | 2.04  | 60000 | 0.6640          | 0.7335   |
| 0.4888        | 2.07  | 61000 | 0.6612          | 0.7318   |
| 0.4964        | 2.11  | 62000 | 0.6516          | 0.7337   |
| 0.493         | 2.14  | 63000 | 0.6503          | 0.7356   |
| 0.4961        | 2.17  | 64000 | 0.6519          | 0.7348   |
| 0.4847        | 2.21  | 65000 | 0.6517          | 0.7327   |
| 0.483         | 2.24  | 66000 | 0.6555          | 0.7310   |
| 0.4857        | 2.28  | 67000 | 0.6525          | 0.7312   |
| 0.484         | 2.31  | 68000 | 0.6444          | 0.7342   |
| 0.4792        | 2.34  | 69000 | 0.6508          | 0.7330   |
| 0.488         | 2.38  | 70000 | 0.6513          | 0.7344   |
| 0.472         | 2.41  | 71000 | 0.6547          | 0.7346   |
| 0.4872        | 2.45  | 72000 | 0.6500          | 0.7342   |
| 0.4782        | 2.48  | 73000 | 0.6585          | 0.7358   |
| 0.481         | 2.51  | 74000 | 0.6477          | 0.7356   |
| 0.4822        | 2.55  | 75000 | 0.6587          | 0.7346   |
| 0.4728        | 2.58  | 76000 | 0.6572          | 0.7340   |
| 0.4841        | 2.62  | 77000 | 0.6443          | 0.7374   |
| 0.4885        | 2.65  | 78000 | 0.6494          | 0.7362   |
| 0.4752        | 2.68  | 79000 | 0.6509          | 0.7382   |
| 0.4883        | 2.72  | 80000 | 0.6457          | 0.7371   |
| 0.4888        | 2.75  | 81000 | 0.6497          | 0.7364   |
| 0.4844        | 2.79  | 82000 | 0.6481          | 0.7376   |
| 0.4833        | 2.82  | 83000 | 0.6451          | 0.7389   |
| 0.48          | 2.85  | 84000 | 0.6423          | 0.7373   |
| 0.4832        | 2.89  | 85000 | 0.6477          | 0.7357   |
| 0.4805        | 2.92  | 86000 | 0.6464          | 0.7379   |
| 0.4775        | 2.96  | 87000 | 0.6477          | 0.7380   |
| 0.4843        | 2.99  | 88000 | 0.6481          | 0.7381   |


### Framework versions


- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


model name: distilbert-base-turkish-cased-allnli_tr


model author: emrecan


task: zero-shot-classification


language:


- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: mit
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"


# bert-base-turkish-cased_allnli_tr


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


- Loss: 0.5771
- Accuracy: 0.7978


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559        | 0.03  | 1000  | 0.7577          | 0.6798   |
| 0.6612        | 0.07  | 2000  | 0.7263          | 0.6958   |
| 0.6115        | 0.1   | 3000  | 0.6431          | 0.7364   |
| 0.5916        | 0.14  | 4000  | 0.6347          | 0.7407   |
| 0.5719        | 0.17  | 5000  | 0.6317          | 0.7483   |
| 0.5575        | 0.2   | 6000  | 0.6034          | 0.7544   |
| 0.5521        | 0.24  | 7000  | 0.6148          | 0.7568   |
| 0.5393        | 0.27  | 8000  | 0.5931          | 0.7610   |
| 0.5382        | 0.31  | 9000  | 0.5866          | 0.7665   |
| 0.5306        | 0.34  | 10000 | 0.5881          | 0.7594   |
| 0.5295        | 0.37  | 11000 | 0.6120          | 0.7632   |
| 0.5225        | 0.41  | 12000 | 0.5620          | 0.7759   |
| 0.5112        | 0.44  | 13000 | 0.5641          | 0.7769   |
| 0.5133        | 0.48  | 14000 | 0.5571          | 0.7798   |
| 0.5023        | 0.51  | 15000 | 0.5719          | 0.7722   |
| 0.5017        | 0.54  | 16000 | 0.5482          | 0.7844   |
| 0.5111        | 0.58  | 17000 | 0.5503          | 0.7800   |
| 0.4929        | 0.61  | 18000 | 0.5502          | 0.7836   |
| 0.4923        | 0.65  | 19000 | 0.5424          | 0.7843   |
| 0.4894        | 0.68  | 20000 | 0.5417          | 0.7851   |
| 0.4877        | 0.71  | 21000 | 0.5514          | 0.7841   |
| 0.4818        | 0.75  | 22000 | 0.5494          | 0.7848   |
| 0.4898        | 0.78  | 23000 | 0.5450          | 0.7859   |
| 0.4823        | 0.82  | 24000 | 0.5417          | 0.7878   |
| 0.4806        | 0.85  | 25000 | 0.5354          | 0.7875   |
| 0.4779        | 0.88  | 26000 | 0.5338          | 0.7848   |
| 0.4744        | 0.92  | 27000 | 0.5277          | 0.7934   |
| 0.4678        | 0.95  | 28000 | 0.5507          | 0.7871   |
| 0.4727        | 0.99  | 29000 | 0.5603          | 0.7789   |
| 0.4243        | 1.02  | 30000 | 0.5626          | 0.7894   |
| 0.3955        | 1.05  | 31000 | 0.5324          | 0.7939   |
| 0.4022        | 1.09  | 32000 | 0.5322          | 0.7925   |
| 0.3976        | 1.12  | 33000 | 0.5450          | 0.7920   |
| 0.3913        | 1.15  | 34000 | 0.5464          | 0.7948   |
| 0.406         | 1.19  | 35000 | 0.5406          | 0.7958   |
| 0.3875        | 1.22  | 36000 | 0.5489          | 0.7878   |
| 0.4024        | 1.26  | 37000 | 0.5427          | 0.7925   |
| 0.3988        | 1.29  | 38000 | 0.5335          | 0.7904   |
| 0.393         | 1.32  | 39000 | 0.5415          | 0.7923   |
| 0.3988        | 1.36  | 40000 | 0.5385          | 0.7962   |
| 0.3912        | 1.39  | 41000 | 0.5383          | 0.7950   |
| 0.3949        | 1.43  | 42000 | 0.5415          | 0.7931   |
| 0.3902        | 1.46  | 43000 | 0.5438          | 0.7893   |
| 0.3948        | 1.49  | 44000 | 0.5348          | 0.7906   |
| 0.3921        | 1.53  | 45000 | 0.5361          | 0.7890   |
| 0.3944        | 1.56  | 46000 | 0.5419          | 0.7953   |
| 0.3959        | 1.6   | 47000 | 0.5402          | 0.7967   |
| 0.3926        | 1.63  | 48000 | 0.5429          | 0.7925   |
| 0.3854        | 1.66  | 49000 | 0.5346          | 0.7959   |
| 0.3864        | 1.7   | 50000 | 0.5241          | 0.7979   |
| 0.385         | 1.73  | 51000 | 0.5149          | 0.8002   |
| 0.3871        | 1.77  | 52000 | 0.5325          | 0.8002   |
| 0.3819        | 1.8   | 53000 | 0.5332          | 0.8022   |
| 0.384         | 1.83  | 54000 | 0.5419          | 0.7873   |
| 0.3899        | 1.87  | 55000 | 0.5225          | 0.7974   |
| 0.3894        | 1.9   | 56000 | 0.5358          | 0.7977   |
| 0.3838        | 1.94  | 57000 | 0.5264          | 0.7988   |
| 0.3881        | 1.97  | 58000 | 0.5280          | 0.7956   |
| 0.3756        | 2.0   | 59000 | 0.5601          | 0.7969   |
| 0.3156        | 2.04  | 60000 | 0.5936          | 0.7925   |
| 0.3125        | 2.07  | 61000 | 0.5898          | 0.7938   |
| 0.3179        | 2.11  | 62000 | 0.5591          | 0.7981   |
| 0.315         | 2.14  | 63000 | 0.5853          | 0.7970   |
| 0.3122        | 2.17  | 64000 | 0.5802          | 0.7979   |
| 0.3105        | 2.21  | 65000 | 0.5758          | 0.7979   |
| 0.3076        | 2.24  | 66000 | 0.5685          | 0.7980   |
| 0.3117        | 2.28  | 67000 | 0.5799          | 0.7944   |
| 0.3108        | 2.31  | 68000 | 0.5742          | 0.7988   |
| 0.3047        | 2.34  | 69000 | 0.5907          | 0.7921   |
| 0.3114        | 2.38  | 70000 | 0.5723          | 0.7937   |
| 0.3035        | 2.41  | 71000 | 0.5944          | 0.7955   |
| 0.3129        | 2.45  | 72000 | 0.5838          | 0.7928   |
| 0.3071        | 2.48  | 73000 | 0.5929          | 0.7949   |
| 0.3061        | 2.51  | 74000 | 0.5794          | 0.7967   |
| 0.3068        | 2.55  | 75000 | 0.5892          | 0.7954   |
| 0.3053        | 2.58  | 76000 | 0.5796          | 0.7962   |
| 0.3117        | 2.62  | 77000 | 0.5763          | 0.7981   |
| 0.3062        | 2.65  | 78000 | 0.5852          | 0.7964   |
| 0.3004        | 2.68  | 79000 | 0.5793          | 0.7966   |
| 0.3146        | 2.72  | 80000 | 0.5693          | 0.7985   |
| 0.3146        | 2.75  | 81000 | 0.5788          | 0.7982   |
| 0.3079        | 2.79  | 82000 | 0.5726          | 0.7978   |
| 0.3058        | 2.82  | 83000 | 0.5677          | 0.7988   |
| 0.3055        | 2.85  | 84000 | 0.5701          | 0.7982   |
| 0.3049        | 2.89  | 85000 | 0.5809          | 0.7970   |
| 0.3044        | 2.92  | 86000 | 0.5741          | 0.7986   |
| 0.3057        | 2.96  | 87000 | 0.5743          | 0.7980   |
| 0.3081        | 2.99  | 88000 | 0.5771          | 0.7978   |


### Framework versions


model name: bert-base-turkish-cased-allnli_tr


model author: emrecan


task: zero-shot-classification


tags: pytorch, bert, text-classification, tr, dataset:nli_tr, transformers, zero-shot-classification, nli, license:mit, infinity_compatible


language:


- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"


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


tags: pytorch, bert, text-classification, tr, dataset:nli_tr, transformers, zero-shot-classification, nli, license:apache-2.0, infinity_compatible


model name: bert-base-turkish-cased-multinli_tr


<!-- 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. -->


# bert-base-multilingual-cased_allnli_tr


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


- Loss: 0.6144
- Accuracy: 0.7662


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8623        | 0.03  | 1000  | 0.9076          | 0.5917   |
| 0.7528        | 0.07  | 2000  | 0.8587          | 0.6119   |
| 0.7074        | 0.1   | 3000  | 0.7867          | 0.6647   |
| 0.6949        | 0.14  | 4000  | 0.7474          | 0.6772   |
| 0.6681        | 0.17  | 5000  | 0.7661          | 0.6814   |
| 0.6597        | 0.2   | 6000  | 0.7264          | 0.6943   |
| 0.6495        | 0.24  | 7000  | 0.7841          | 0.6781   |
| 0.6323        | 0.27  | 8000  | 0.7256          | 0.6952   |
| 0.6308        | 0.31  | 9000  | 0.7319          | 0.6958   |
| 0.6254        | 0.34  | 10000 | 0.7054          | 0.7004   |
| 0.6233        | 0.37  | 11000 | 0.7069          | 0.7085   |
| 0.6165        | 0.41  | 12000 | 0.6880          | 0.7181   |
| 0.6033        | 0.44  | 13000 | 0.6844          | 0.7197   |
| 0.6014        | 0.48  | 14000 | 0.6753          | 0.7129   |
| 0.5947        | 0.51  | 15000 | 0.7000          | 0.7039   |
| 0.5965        | 0.54  | 16000 | 0.6708          | 0.7263   |
| 0.5979        | 0.58  | 17000 | 0.6562          | 0.7285   |
| 0.5787        | 0.61  | 18000 | 0.6554          | 0.7297   |
| 0.58          | 0.65  | 19000 | 0.6544          | 0.7315   |
| 0.574         | 0.68  | 20000 | 0.6549          | 0.7339   |
| 0.5751        | 0.71  | 21000 | 0.6545          | 0.7289   |
| 0.5659        | 0.75  | 22000 | 0.6467          | 0.7371   |
| 0.5732        | 0.78  | 23000 | 0.6448          | 0.7362   |
| 0.5637        | 0.82  | 24000 | 0.6520          | 0.7355   |
| 0.5648        | 0.85  | 25000 | 0.6412          | 0.7345   |
| 0.5622        | 0.88  | 26000 | 0.6350          | 0.7358   |
| 0.5579        | 0.92  | 27000 | 0.6347          | 0.7393   |
| 0.5518        | 0.95  | 28000 | 0.6417          | 0.7392   |
| 0.5547        | 0.99  | 29000 | 0.6321          | 0.7437   |
| 0.524         | 1.02  | 30000 | 0.6430          | 0.7412   |
| 0.4982        | 1.05  | 31000 | 0.6253          | 0.7458   |
| 0.5002        | 1.09  | 32000 | 0.6316          | 0.7418   |
| 0.4993        | 1.12  | 33000 | 0.6197          | 0.7487   |
| 0.4963        | 1.15  | 34000 | 0.6307          | 0.7462   |
| 0.504         | 1.19  | 35000 | 0.6272          | 0.7480   |
| 0.4922        | 1.22  | 36000 | 0.6410          | 0.7433   |
| 0.5016        | 1.26  | 37000 | 0.6295          | 0.7461   |
| 0.4957        | 1.29  | 38000 | 0.6183          | 0.7506   |
| 0.4883        | 1.32  | 39000 | 0.6261          | 0.7502   |
| 0.4985        | 1.36  | 40000 | 0.6315          | 0.7496   |
| 0.4885        | 1.39  | 41000 | 0.6189          | 0.7529   |
| 0.4909        | 1.43  | 42000 | 0.6189          | 0.7473   |
| 0.4894        | 1.46  | 43000 | 0.6314          | 0.7433   |
| 0.4912        | 1.49  | 44000 | 0.6184          | 0.7446   |
| 0.4851        | 1.53  | 45000 | 0.6258          | 0.7461   |
| 0.4879        | 1.56  | 46000 | 0.6286          | 0.7480   |
| 0.4907        | 1.6   | 47000 | 0.6196          | 0.7512   |
| 0.4884        | 1.63  | 48000 | 0.6157          | 0.7526   |
| 0.4755        | 1.66  | 49000 | 0.6056          | 0.7591   |
| 0.4811        | 1.7   | 50000 | 0.5977          | 0.7582   |
| 0.4787        | 1.73  | 51000 | 0.5915          | 0.7621   |
| 0.4779        | 1.77  | 52000 | 0.6014          | 0.7583   |
| 0.4767        | 1.8   | 53000 | 0.6041          | 0.7623   |
| 0.4737        | 1.83  | 54000 | 0.6093          | 0.7563   |
| 0.4836        | 1.87  | 55000 | 0.6001          | 0.7568   |
| 0.4765        | 1.9   | 56000 | 0.6109          | 0.7601   |
| 0.4776        | 1.94  | 57000 | 0.6046          | 0.7599   |
| 0.4769        | 1.97  | 58000 | 0.5970          | 0.7568   |
| 0.4654        | 2.0   | 59000 | 0.6147          | 0.7614   |
| 0.4144        | 2.04  | 60000 | 0.6439          | 0.7566   |
| 0.4101        | 2.07  | 61000 | 0.6373          | 0.7527   |
| 0.4192        | 2.11  | 62000 | 0.6136          | 0.7575   |
| 0.4128        | 2.14  | 63000 | 0.6283          | 0.7560   |
| 0.4204        | 2.17  | 64000 | 0.6187          | 0.7625   |
| 0.4114        | 2.21  | 65000 | 0.6127          | 0.7621   |
| 0.4097        | 2.24  | 66000 | 0.6188          | 0.7626   |
| 0.4129        | 2.28  | 67000 | 0.6156          | 0.7639   |
| 0.4085        | 2.31  | 68000 | 0.6232          | 0.7616   |
| 0.4074        | 2.34  | 69000 | 0.6240          | 0.7605   |
| 0.409         | 2.38  | 70000 | 0.6153          | 0.7591   |
| 0.4046        | 2.41  | 71000 | 0.6375          | 0.7587   |
| 0.4117        | 2.45  | 72000 | 0.6145          | 0.7629   |
| 0.4002        | 2.48  | 73000 | 0.6279          | 0.7610   |
| 0.4042        | 2.51  | 74000 | 0.6176          | 0.7646   |
| 0.4055        | 2.55  | 75000 | 0.6277          | 0.7643   |
| 0.4021        | 2.58  | 76000 | 0.6196          | 0.7642   |
| 0.4081        | 2.62  | 77000 | 0.6127          | 0.7659   |
| 0.408         | 2.65  | 78000 | 0.6237          | 0.7638   |
| 0.3997        | 2.68  | 79000 | 0.6190          | 0.7636   |
| 0.4093        | 2.72  | 80000 | 0.6152          | 0.7648   |
| 0.4095        | 2.75  | 81000 | 0.6155          | 0.7627   |
| 0.4088        | 2.79  | 82000 | 0.6130          | 0.7641   |
| 0.4063        | 2.82  | 83000 | 0.6072          | 0.7646   |
| 0.3978        | 2.85  | 84000 | 0.6128          | 0.7662   |
| 0.4034        | 2.89  | 85000 | 0.6157          | 0.7627   |
| 0.4044        | 2.92  | 86000 | 0.6127          | 0.7661   |
| 0.403         | 2.96  | 87000 | 0.6126          | 0.7664   |
| 0.4033        | 2.99  | 88000 | 0.6144          | 0.7662   |


### Framework versions


- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


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


model author: emrecan


task: zero-shot-classification


language:


- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"


# convbert-base-turkish-mc4-cased_allnli_tr


This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/dbmdz/convbert-base-turkish-mc4-cased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5541
- Accuracy: 0.8111


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results