Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=ag_news).


## Training procedure


## Eval results


model name: bert-base-uncased-ag-news


model author: nateraw


task: text-classification


tags: pytorch, jax, bert, text-classification, en, dataset:ag_news, transformers, ag_news, license:mit, infinity_compatible


tags:


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


model-index:


- name: donut-or-bagel
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9375


# donut-or-bagel


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


#### bagel


![bagel](images/bagel.jpg)


#### donut


![donut](images/donut.jpg)


model name: donut-or-bagel


model author: nateraw


task: image-classification


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


tags:


- name: doggos-lol
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9166666865348816


# doggos-lol


#### bernese mountain dog


![bernese mountain dog](images/bernese_mountain_dog.jpg)


#### husky


![husky](images/husky.jpg)


#### saint bernard


![saint bernard](images/saint_bernard.jpg)


model name: doggos-lol


license: apache-2.0
tags:


- image-classification
- huggingpics
- generated_from_trainer
model-index:
- name: pasta-shapes
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. -->


# pasta-shapes


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


- Loss: 0.3761
- Acc: 0.9403


## 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: 4.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0328        | 1.0   | 24   | 0.9442          | 0.7463 |
| 0.8742        | 2.0   | 48   | 0.7099          | 0.9403 |
| 0.6451        | 3.0   | 72   | 0.5050          | 0.9403 |
| 0.508         | 4.0   | 96   | 0.3761          | 0.9403 |


### Framework versions


- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3


model name: pasta-shapes


model author: nateraw


task: image-classification


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


tags:


model-index:


- name: rare-puppers
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9583333134651184


# rare-puppers


Autogenerated by HuggingPics🤗🖼


## Example Images


#### corgi


![corgi](images/corgi.jpg)


#### samoyed


![samoyed](images/samoyed.jpg)


#### shiba inu


![shiba inu](images/shiba_inu.jpg)


model name: rare-puppers


license: apache-2.0
tags:


- image-classification
- huggingpics
- generated_from_trainer


# huggingpics-package-demo-2


- Loss: 0.3761
- Acc: 0.9403


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


### Framework versions


model name: huggingpics-package-demo-2


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, transformers, license:apache-2.0, huggingpics, generated_from_trainer


tags:


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


model-index:


- name: denver-nyc-paris
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.8657407164573669


# denver-nyc-paris


Autogenerated by HuggingPics🤗🖼


Create your own image classifier for **anything** by running [the demo](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


#### denver


![denver](images/denver.jpg)


#### new york city


![new york city](images/new_york_city.jpg)


#### paris


![paris](images/paris.jpg)


model name: denver-nyc-paris


model author: nateraw


task: image-classification


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


tags:


- image-classification
- timm
library_tag: timm


# Model card for resnet18-random-classifier


model name: resnet18-random-classifier


tags: pytorch, timm, image-classification


model-index:


- name: pasta-pizza-ravioli
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9375


# pasta-pizza-ravioli


Autogenerated by HuggingPics🤗🖼


## Example Images


#### pasta


![pasta](images/pasta.jpg)


#### pizza


![pizza](images/pizza.jpg)


#### ravioli


![ravioli](images/ravioli.jpg)


model name: pasta-pizza-ravioli


model author: nateraw


task: image-classification


license: apache-2.0
tags:


- image-classification
- keras
library_name: keras


# Model Card for nateraw/keras-mobilevit-xxs-flowers


model name: keras-mobilevit-xxs-flowers


tags: keras, license:apache-2.0, image-classification


- image-classification
- huggingpics
- generated_from_trainer
model-index:
- name: huggingpics-package-demo
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. -->


# huggingpics-package-demo


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


- Loss: 0.3761
- Acc: 0.9403


## 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: 4.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0328        | 1.0   | 24   | 0.9442          | 0.7463 |
| 0.8742        | 2.0   | 48   | 0.7099          | 0.9403 |
| 0.6451        | 3.0   | 72   | 0.5050          | 0.9403 |
| 0.508         | 4.0   | 96   | 0.3761          | 0.9403 |


### Framework versions


- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3


model name: huggingpics-package-demo


model author: nateraw


task: image-classification


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


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- imdb
model-index:
- name: codecarbon-text-classification
results: []


# codecarbon-text-classification


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4


### Training results


### Framework versions


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


model name: codecarbon-text-classification


model author: nateraw


task: text-classification


tags: pytorch, tensorboard, bert, text-classification, dataset:imdb, transformers, license:apache-2.0, generated_from_trainer, model-index, infinity_compatible


license: apache-2.0
tags:


- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v2
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
    - name: Accuracy
type: accuracy
value: 1.0


# vit-base-beans-demo-v2


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


- Loss: 0.0099
- Accuracy: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0705        | 1.54  | 100  | 0.0562          | 0.9925   |
| 0.0123        | 3.08  | 200  | 0.0124          | 1.0      |
| 0.008         | 4.62  | 300  | 0.0099          | 1.0      |


### Framework versions


- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


model name: vit-base-beans-demo-v2


model author: nateraw


task: image-classification


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


tags:


- audio
- audio-to-audio
library_tag: generic


# Audio-to-Audio Test


Nothing to see here!


model name: audio-to-audio-test2


task: audio-to-audio


tags: audio, audio-to-audio


- image-classification
- timm
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model_index:
- name: timm-resnet18-beans-test
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metric:
name: Accuracy
type: accuracy
value: 0.3609022556390977


<!-- 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. -->


# timm-resnet18-beans-test


This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.
It achieves the following results on the evaluation set:


- Loss: 1.2126
- Accuracy: 0.3609


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


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


### Training results


### Framework versions


- Transformers 4.9.1
- Pytorch 1.9.0
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3


model name: timm-resnet18-beans-test


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, dataset:beans, timm, image-classification, generated_from_trainer


tags:


- audio-to-audio
library_name: generic


model name: audio-test


task: audio-to-audio


tags: generic, audio-to-audio


- generated_from_trainer
datasets:
- image_folder
model_index:
- name: test_model_a
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default


# test_model_a


This model is a fine-tuned version of [lysandre/tiny-vit-random](https://huggingface.co/lysandre/tiny-vit-random) on the image_folder dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 40


### Framework versions


- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3


model name: test_model_a


model author: nateraw


task: image-classification


tags: pytorch, vit, image-classification, dataset:image_folder, transformers, generated_from_trainer


tags:


- image-classification
- timm
library_tag: timm


# Model card for resnet18-random-classifier-new


model name: resnet18-random-classifier-new


tags: pytorch, timm, image-classification


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


model-index:


- name: baked-goods
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.875


# baked-goods


Autogenerated by HuggingPics🤗🖼


Create your own image classifier for **anything** by running [the demo](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


#### cake


![cake](images/cake.jpg)


#### cookie


![cookie](images/cookie.jpg)


#### pie


![pie](images/pie.jpg)


model name: baked-goods


model author: nateraw


task: image-classification


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


tags:


- image-classification
- timm
- generated_from_trainer
datasets:
- beans
model-index:
- name: model
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
library_tag: timm


<!-- 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. -->


# model


This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.
It achieves the following results on the evaluation set:


- Loss: 1.0219
- Acc1: 56.3910
- Acc5: 100.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20


### Training results


### Framework versions


- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: resnet50-beans-dummy-sagemaker


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, dataset:beans, timm, image-classification, generated_from_trainer, model-index


license: apache-2.0
tags:


- image-classification
- keras
library_name: keras


Keras Dog vs Cat based on the [official Keras documentation](https://keras.io/examples/vision/image_classification_from_scratch/)


model name: keras-mnist-convnet-demo


tags: keras, license:apache-2.0, image-classification


tags:


- image-classification
- vision
- pytorch
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4


# Vision Transformer Fine Tuned on CIFAR10


Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and **fine-tuned on CIFAR10** at resolution 224x224.


Check out the code at my [my Github repo](https://github.com/nateraw/huggingface-vit-finetune).


## Usage


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

url = 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
model = ViTForImageClassification.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
preds = outputs.logits.argmax(dim=1)

classes = [
    'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
]
classes[preds[0]]
```


## Model description


The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).


By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.


model name: vit-base-patch16-224-cifar10


model author: nateraw


task: image-classification


tags: pytorch, vit, image-classification, dataset:cifar10, transformers, vision, license:apache-2.0


- image-classification
- timm
library_tag: timm


# Model card for timm-resnet18-imagenette-160px-5-epochs


model name: timm-resnet18-imagenette-160px-5-epochs


tags: pytorch, timm, image-classification


tags:


- image-classification
- timm
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model_index:
- name: timm-resnet18-beans-test-2
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metric:
name: Accuracy
type: accuracy
value: 0.5789473684210527


<!-- 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. -->


# timm-resnet18-beans-test-2


This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.
It achieves the following results on the evaluation set:


- Loss: 1.3225
- Accuracy: 0.5789


## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2601        | 0.02  | 5    | 2.8349          | 0.5113   |
| 1.8184        | 0.04  | 10   | 1.3225          | 0.5789   |


### Framework versions


- Transformers 4.9.1
- Pytorch 1.9.0
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3


model name: timm-resnet18-beans-test-2


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, dataset:beans, timm, image-classification, generated_from_trainer


tags:


- image-classification
- pytorch
datasets:
- imagenet


# Resnet50 Model from Torchvision


## Using the model


```
pip install modelz
```


```python
from modelz import ResnetModel

model = ResnetModel.from_pretrained('nateraw/resnet50')
ex_input = torch.rand(4, 3, 224, 224)
out = model(ex_input)
```


model name: resnet50


tags: pytorch, resnet, dataset:imagenet, transformers, image-classification


license: apache-2.0
tags:


- image-classification
- keras
library_name: keras


# Test


model name: keras-cats-vs-dogs


tags: keras, license:apache-2.0, image-classification


- text-classification
library_name: generic


model name: text-classification-flash-demo-2


model author: nateraw


task: text-classification


tags: pytorch, generic, text-classification


tags:


- image-classification
- timm
library_tag: timm


# Model card for resnet18-random-classifier-123


model name: resnet18-random-classifier-123


task: image-classification


tags: pytorch, timm, image-classification


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


model-index:


- name: rare-puppers-09-04-2021
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.8657407164573669


# rare-puppers-09-04-2021


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


#### corgi


![corgi](images/corgi.jpg)


#### samoyed


![samoyed](images/samoyed.jpg)


#### shiba inu


![shiba inu](images/shiba_inu.jpg)


model name: rare-puppers-09-04-2021


model author: nateraw


task: image-classification


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


tags:


model-index:


- name: rare-puppers-demo
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9101123809814453


# rare-puppers-demo


Autogenerated by HuggingPics🤗🖼


#### husky


![husky](images/husky.jpg)


model name: rare-puppers-demo


- object-detection
- pytorch


# hot-dog


Ignore me...I'm broken.


model name: hot-dog


task: object-detection


tags: pytorch, detr, object-detection, transformers


# Model card for resnet50-oxford-iiit-pet


![boxer](boxer.jpg)


model name: resnet50-oxford-iiit-pet


model author: nateraw


task: image-classification


tags:


# Model Card for `guesslang`


model name: guesslang


task: text-classification


tags: generic, text-classification


license: apache-2.0
tags:


- generated_from_trainer
model_index:
- name: trainer-rare-puppers
results:
  - task:
name: Image Classification
type: image-classification


<!-- 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. -->


# trainer-rare-puppers


This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the huggingpics dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 48   | 0.4087          | 0.8806   |


### Framework versions


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


model name: trainer-rare-puppers


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, transformers, license:apache-2.0, generated_from_trainer


license: apache-2.0
tags:


- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy


model-index:


- name: vit-base-beans-demo
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
    - name: Accuracy
type: accuracy
value: 0.9774436090225563


# vit-base-beans-demo


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


- Loss: 0.0853
- Accuracy: 0.9774


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0545        | 1.54  | 100  | 0.1436          | 0.9624   |
| 0.006         | 3.08  | 200  | 0.1058          | 0.9699   |
| 0.0038        | 4.62  | 300  | 0.0853          | 0.9774   |


### Framework versions


model name: vit-base-beans-demo


model author: nateraw


task: image-classification


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


tags:


- image-classification
library_name: generic


# Test


model name: test-classifier-flash-2


tags: pytorch, generic, image-classification


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


model-index:


- name: ex-for-evan
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9791666865348816


# ex-for-evan


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


#### cheetah


![cheetah](images/cheetah.jpg)


#### elephant


![elephant](images/elephant.jpg)


#### giraffe


![giraffe](images/giraffe.jpg)


#### rhino


![rhino](images/rhino.jpg)


model name: ex-for-evan


model author: nateraw


task: image-classification


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


license: apache-2.0
tags:


- huggingpics
- image-classification
- generated_from_trainer
metrics:
- accuracy
model_index:
- name: planes-trains-automobiles
results:
  - task:
name: Image Classification
type: image-classification
metric:
name: Accuracy
type: accuracy
value: 0.9850746268656716


<!-- 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. -->


# planes-trains-automobiles


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


- Loss: 0.0534
- Accuracy: 0.9851


## Model description


Autogenerated by HuggingPics🤗🖼


## Example Images


#### automobiles


![automobiles](images/automobiles.jpg)


#### planes


![planes](images/planes.jpg)


#### trains


![trains](images/trains.jpg)


## Intended uses & limitations


More information needed


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0283        | 1.0   | 48   | 0.0434          | 0.9851   |
| 0.0224        | 2.0   | 96   | 0.0548          | 0.9851   |
| 0.0203        | 3.0   | 144  | 0.0445          | 0.9851   |
| 0.0195        | 4.0   | 192  | 0.0534          | 0.9851   |


### Framework versions


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


model name: planes-trains-automobiles


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, transformers, license:apache-2.0, huggingpics, generated_from_trainer


tags:


model-index:


- name: rare-puppers-123
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.9701492786407471


# rare-puppers-123


Autogenerated by HuggingPics🤗🖼


## Example Images


#### corgi


![corgi](images/corgi.jpg)


#### samoyed


![samoyed](images/samoyed.jpg)


#### shiba inu


![shiba inu](images/shiba_inu.jpg)


model name: rare-puppers-123


license: apache-2.0
tags:


- generated_from_trainer
- image-classification
- pytorch
datasets:
- food101
metrics:
- accuracy
model_index:
- name: food101_outputs
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: nateraw/food101
type: food101
args: default
metric:
name: Accuracy
type: accuracy
value: 0.8912871287128713


# nateraw/food


This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the nateraw/food101 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.4501
- Accuracy: 0.8913


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0002
- train_batch_size: 128
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8271        | 1.0   | 592  | 0.6070          | 0.8562   |
| 0.4376        | 2.0   | 1184 | 0.4947          | 0.8691   |
| 0.2089        | 3.0   | 1776 | 0.4876          | 0.8747   |
| 0.0882        | 4.0   | 2368 | 0.4639          | 0.8857   |
| 0.0452        | 5.0   | 2960 | 0.4501          | 0.8913   |


### Framework versions


- Transformers 4.9.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3


model name: food


model author: nateraw


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, dataset:food101, transformers, license:apache-2.0, generated_from_trainer


license: apache-2.0
tags:


- generated_from_trainer
- image-classification
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
  - task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
    - name: Accuracy
type: accuracy
value: 0.9774436090225563


# vit-base-beans


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


- Loss: 0.0942
- Accuracy: 0.9774


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2809        | 1.0   | 130  | 0.2287          | 0.9699   |
| 0.1097        | 2.0   | 260  | 0.1676          | 0.9624   |
| 0.1027        | 3.0   | 390  | 0.0942          | 0.9774   |
| 0.0923        | 4.0   | 520  | 0.1104          | 0.9699   |
| 0.1726        | 5.0   | 650  | 0.1030          | 0.9699   |


### Framework versions


- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3


model name: vit-base-beans


model author: nateraw


task: image-classification


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


language: hr
datasets:


- parlaspeech-hr
tags:
- audio
- automatic-speech-recognition
- parlaspeech
widget:
- example_title: example 1
src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/SBiNG.wav
- example_title: example 2
src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav


# wav2vec2-xls-r-parlaspeech-hr


This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 72 hours of recordings and transcripts from the Croatian parliament. This training dataset is an early result of the second iteration of the [ParlaMint project](https://www.clarin.eu/content/parlamint-towards-comparable-parliamentary-corpora) inside which the dataset will be extended and published under the name of ParlaSpeech-HR and an open licence.


The efforts resulting in this model were coordinated by Nikola Ljubešić, the rough manual data alignment was performed by Ivo-Pavao Jazbec, the method for fine automatic data alignment from [Plüss et al.](https://arxiv.org/abs/2010.02810) was applied by Vuk Batanović and Lenka Bajčetić, the transcripts were normalised by Danijel Korzinek, while the final modelling was performed by Peter Rupnik.


Initial evaluation on partially noisy data showed the model to achieve a word error rate of 13.68% and a character error rate of 4.56%.


## Usage in `transformers`


```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import Audio
import soundfile as sf
import torch
import os

# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained(
    "classla/wav2vec2-xls-r-parlaspeech-hr")
model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr")

# download the example wav files:
os.system("curl https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav")

# read the wav file as datasets.Audio object
audio = Audio(sampling_rate=16000).decode_example("00020570a.flac.wav")

# remove the raw wav file
os.system("rm 00020570a.flac.wav")

# tokenize
input_values = processor(
        audio["array"],  return_tensors="pt", padding=True,
        sampling_rate=16000).input_values

# retrieve logits
logits = model(input_values).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)

# transcription: ['veliki broj poslovnih subjekata posluje sa minusom velik dio']
```


## Training hyperparameters


In fine-tuning, the following arguments were used:


| arg                           | value |
|-------------------------------|-------|
| `per_device_train_batch_size` | 16    |
| `gradient_accumulation_steps` | 4     |
| `num_train_epochs`            | 8     |
| `learning_rate`               | 3e-4  |
| `warmup_steps`                | 500   |


model name: wav2vec2-xls-r-parlaspeech-hr


model author: classla


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, hr, dataset:parlaspeech-hr, arxiv:2010.02810, transformers, audio, parlaspeech


language: "en"


tags:


- text-classification
- hate-speech


widget:


- text: "Gay is okay."


# roberta-base-frenk-hate


Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).


## Fine-tuning hyperparameters


Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:


```python
model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}
```


## Performance


The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.


| model | average accuracy | average macro F1|
|---|---|---|
|roberta-base-frenk-hate|0.7915|0.7785|
|xlm-roberta-large |0.7904|0.77876|
|xlm-roberta-base |0.7577|0.7402|
|fasttext|0.725 |0.707 |


From recorded accuracies and macro F1 scores p-values were also calculated:


Comparison with `xlm-roberta-base`:


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U-test|0.00108|0.00108|
|Student t-test | 1.35e-08 | 1.05e-07|


Comparison with `xlm-roberta-large` yielded inconclusive results.  `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below.


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.188|0.406|
|Mann Whithey|0.375|0.649|
|Student t-test | 0.681| 0.934|


With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another.


## Use examples


```python
from simpletransformers.classification import ClassificationModel
model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}

model = ClassificationModel(
    "roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
    args=model_args

)

predictions, logit_output = model.predict(["Build the wall",
                                        "Build the wall of trust"]
                                        )
predictions
### Output:
### array([1, 0])
```


## Citation


If you use the model, please cite the following paper on which the original model is based:


```
@article{DBLP:journals/corr/abs-1907-11692,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


and the dataset used for fine-tuning:


```
@misc{ljubešić2019frenk,
      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
      year={2019},
      eprint={1906.02045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1906.02045}
}
```


model name: roberta-base-frenk-hate


model author: classla


task: text-classification


tags: pytorch, roberta, text-classification, en, arxiv:1907.11692, arxiv:1906.02045, transformers, hate-speech, infinity_compatible


language:


- hr
- bs
- sr
- cnr
- hbs


## license: apache-2.0


# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian


* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).


This Electra model was trained on more than 8 billion tokens of Bosnian, Croatian, Montenegrin and Serbian text.


***new*** We have published a version of this model fine-tuned on the named entity recognition task ([bcms-bertic-ner](https://huggingface.co/classla/bcms-bertic-ner)) and on the hate speech detection task ([bcms-bertic-frenk-hate](https://huggingface.co/classla/bcms-bertic-frenk-hate)).


If you use the model, please cite the following paper:


```
@inproceedings{ljubesic-lauc-2021-bertic,
    title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and Lauc, Davor",
    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Kiyv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
    pages = "37--42",
}
```


## Benchmarking


Comparing this model to [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) and [CroSloEngual BERT](https://huggingface.co/EMBEDDIA/crosloengual-bert) on the tasks of (1) part-of-speech tagging, (2) named entity recognition, (3) geolocation prediction, and (4) commonsense causal reasoning, shows the BERTić model to be superior to the other two.


### Part-of-speech tagging


Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (* p<=0.05, ** p<=0.01, *** p<=0.001, ***** p<=0.0001).


Dataset  | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić
---|---|---|---|---|---|---
hr500k | Croatian | standard | 93.87 | 94.60 | 95.74 | **95.81*****
reldi-hr | Croatian | internet non-standard | - | 88.87 | 91.63 | **92.28*****
SETimes.SR | Serbian | standard | 95.00 | 95.50 | **96.41** | 96.31
reldi-sr | Serbian | internet non-standard | - | 91.26 |  93.54 | **93.90*****


### Named entity recognition


Dataset  | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić
---|---|---|---|---|---|---
hr500k | Croatian | standard | 80.13 | 85.67 | 88.98 | **89.21******
reldi-hr | Croatian | internet non-standard | - | 76.06 | 81.38 | **83.05******
SETimes.SR | Serbian | standard | 84.64 | **92.41** | 92.28 | 92.02
reldi-sr | Serbian | internet non-standard | - | 81.29 | 82.76 | **87.92******


### Geolocation prediction


The dataset comes from the VarDial 2020 evaluation campaign's shared task on [Social Media variety Geolocation prediction](https://sites.google.com/view/vardial2020/evaluation-campaign). The task is to predict the latitude and longitude of a tweet given its text.


Evaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset.


System | Median | Mean
---|---|---
centroid | 107.10 | 145.72
mBERT | 42.25 | 82.05
cseBERT | 40.76 | 81.88
BERTić | **37.96** | **79.30**


### Choice Of Plausible Alternatives


The dataset is a translation of the [COPA dataset](https://people.ict.usc.edu/~gordon/copa.html) into Croatian ([link to the dataset](http://hdl.handle.net/11356/1404)).


Evaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (* p<=0.05, ** p<=0.01, *** p<=0.001, ***** p<=0.0001).


System | Accuracy
---|---
random | 50.00
mBERT | 54.12
cseBERT | 61.80
BERTić | **65.76****


model name: bcms-bertic


model author: classla


tags: pytorch, electra, pretraining, hr, bs, sr, cnr, hbs, transformers, license:apache-2.0, infinity_compatible


language:


- hr
- bs
- sr
- cnr
- hbs


widget:


- text: "Zovem se Marko i živim u Zagrebu. Studirao sam u Beogradu na Filozofskom fakultetu. Obožavam album Moanin."


license: apache-2.0


# The [BERTić](https://huggingface.co/classla/bcms-bertic)* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)


* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).


This is a fine-tuned version of the [BERTić](https://huggingface.co/classla/bcms-bertic) model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets:


- the [hr500k](http://hdl.handle.net/11356/1183) dataset, 500 thousand tokens in size, standard Croatian
- the [SETimes.SR](http://hdl.handle.net/11356/1200) dataset, 87 thousand tokens in size, standard Serbian
- the [ReLDI-hr](http://hdl.handle.net/11356/1241) dataset, 89 thousand tokens in size, Internet (Twitter) Croatian
- the [ReLDI-sr](http://hdl.handle.net/11356/1240) dataset, 92 thousand tokens in size, Internet (Twitter) Serbian


The data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the [main model page](https://huggingface.co/classla/bcms-bertic).


If you use this fine-tuned model, please cite the following paper:


```
@inproceedings{ljubesic-lauc-2021-bertic,
    title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and Lauc, Davor",
    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Kiyv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
    pages = "37--42",
}
```


When running the model in `simpletransformers`, the order of labels has to be set as well.


```
from simpletransformers.ner import NERModel, NERArgs
model_args = NERArgs()
model_args.labels_list = ['B-LOC','B-MISC','B-ORG','B-PER','I-LOC','I-MISC','I-ORG','I-PER','O']
model = NERModel('electra', 'classla/bcms-bertic-ner', args=model_args)
```


model name: bcms-bertic-ner


model author: classla


task: token-classification


tags: pytorch, electra, token-classification, hr, bs, sr, cnr, hbs, transformers, license:apache-2.0, autonlp_compatible, infinity_compatible


language: "hr"


tags:


- text-classification
- hate-speech


widget:


- text: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."


# bcms-bertic-frenk-hate


Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).


## Fine-tuning hyperparameters


Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:


```python

model_args = {
        "num_train_epochs": 12,
        "learning_rate": 1e-5,
        "train_batch_size": 74}
```


## Performance


The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.


| model | average accuracy | average macro F1|
|---|---|---|
|bcms-bertic-frenk-hate|0.8313|0.8219|
|EMBEDDIA/crosloengual-bert |0.8054|0.796|
|xlm-roberta-base |0.7175|0.7049|
|fasttext|0.771 |0.754 |


From recorded accuracies and macro F1 scores p-values were also calculated:


Comparison with `crosloengual-bert`:


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney|0.00108|0.00108|
|Student t-test |2.43e-10 |1.27e-10|


Comparison with `xlm-roberta-base`:


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney|0.00107|0.00108|
|Student t-test |4.83e-11 | 5.61e-11 |


## Use examples


```python
from simpletransformers.classification import ClassificationModel
model_args = {
        "num_train_epochs": 12,
        "learning_rate": 1e-5,
        "train_batch_size": 74}

model = ClassificationModel(
    "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
    args=model_args

)

predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala',
                                           "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "])
predictions
### Output:
### array([0, 0])
```


## Citation


If you use the model, please cite the following paper on which the original model is based:


and the dataset used for fine-tuning:


```
@misc{ljubešić2019frenk,
      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
      year={2019},
      eprint={1906.02045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1906.02045}
}
```


model name: bcms-bertic-frenk-hate


model author: classla


task: text-classification


tags: pytorch, bert, text-classification, hr, arxiv:1906.02045, transformers, hate-speech, infinity_compatible


language:


- hr
- bs
- sr
- cnr
- hbs


tags:


- masked-lm


widget:


- text: "Zovem se Marko i radim u [MASK]."


license: apache-2.0


# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian


* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).


This is the smaller generator of the main [discriminator model](https://huggingface.co/classla/bcms-bertic), useful if you want to continue pre-training the discriminator model.


If you use the model, please cite the following paper:


```
@inproceedings{ljubesic-lauc-2021-bertic,
    title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and Lauc, Davor",
    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Kiyv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
    pages = "37--42",

}
```


model name: bcms-bertic-generator


tags: pytorch, electra, pretraining, hr, bs, sr, cnr, hbs, transformers, masked-lm, license:apache-2.0, infinity_compatible


language: "sl"


- text-classification
- hate-speech


- text: "Silva, ti si grda in neprijazna"


Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).


## Fine-tuning hyperparameters


Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:


```python
model_args = {
        "num_train_epochs": 14,
        "learning_rate": 1e-5,
        "train_batch_size": 21,
        }
```


## Performance


The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.


| model | average accuracy | average macro F1|
|---|---|---|
|sloberta-frenk-hate|0.7785|0.7764|
|EMBEDDIA/crosloengual-bert |0.7616|0.7585|
|xlm-roberta-base |0.686|0.6827|
|fasttext|0.709 |0.701 |


From recorded accuracies and macro F1 scores p-values were also calculated:


Comparison with `crosloengual-bert`:


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00163|0.00108|
|Student t-test |0.000101|3.95e-05|


Comparison with `xlm-roberta-base`:


| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00108|0.00108|
|Student t-test |9.46e-11|6.94e-11|


## Use examples


```python
from simpletransformers.classification import ClassificationModel
model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}

model = ClassificationModel(
    "camembert", "5roop/sloberta-frenk-hate", use_cuda=True,
    args=model_args

)

predictions, logit_output = model.predict(["Silva, ti si grda in neprijazna", "Naša hiša ima dimnik"])
predictions
### Output:
### array([1, 0])
```


## Citation


If you use the model, please cite the following paper on which the original model is based:


```
@article{DBLP:journals/corr/abs-1907-11692,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

```


and the dataset used for fine-tuning:


```
@misc{ljubešić2019frenk,
      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
      year={2019},
      eprint={1906.02045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1906.02045}
}
```


model name: sloberta-frenk-hate


model author: classla


task: text-classification


tags: pytorch, camembert, text-classification, sl, arxiv:1907.11692, arxiv:1906.02045, transformers, hate-speech


### Model information


* language : English
* fine tuning data : [squad 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
* License : CC-BY-SA 4.0
* Base model : [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
* input : question, context
* output : answer


### Train information


* train_runtime : 7562.859
* train_steps_per_second : 1.077
* training_loss : 0.9661213896603117
* epoch: 3.0


### How to use


```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained (
"seongju/squadv2-xlm-roberta-base"
)

model = AutoModelForSequenceClassification.from_pretrained (
"seongju/squadv2-xlm-roberta-base"
)
```


model name: squadv2-xlm-roberta-base


model author: seongju


task: question-answering


tags: pytorch, xlm-roberta, question-answering, transformers, autonlp_compatible


* language : Korean
* fine tuning data : [klue-tc (a.k.a. YNAT) ](https://klue-benchmark.com/tasks/66/overview/description)
* License : CC-BY-SA 4.0
* Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
* input : news headline
* output : topic


* train_runtime: 1477.3876
* train_steps_per_second: 2.416
* train_loss: 0.3722160959110207
* epoch: 5.0


```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained (
"seongju/klue-tc-bert-base-multilingual-cased"
)

model = AutoModelForSequenceClassification.from_pretrained (
"seongju/klue-tc-bert-base-multilingual-cased"
)
mapping = {0: 'IT과학', 1: '경제', 2: '사회',
3: '생활문화', 4: '세계', 5: '스포츠', 6: '정치'}
inputs = tokenizer(
"백신 회피 가능성? 남미에서 새로운 변이 바이러스 급속 확산 ",
 padding=True, truncation=True, max_length=128, return_tensors="pt"
 )
outputs = model(**inputs)
probs = outputs[0].softmax(1)
output = mapping[probs.argmax().item()]
```


model name: klue-tc-bert-base-multilingual-cased


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


* language : Korean
* fine tuning data : [kor_3i4k](https://huggingface.co/datasets/kor_3i4k)
* License : CC-BY-SA 4.0
* Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
* input : sentence
* output : intent


* train_runtime: 2376.638
* train_steps_per_second: 2.175
* train_loss: 0.356829648599977
* epoch: 3.0


```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained (
"seongju/kor-3i4k-bert-base-cased"
)

model = AutoModelForSequenceClassification.from_pretrained (
"seongju/kor-3i4k-bert-base-cased"
)
inputs = tokenizer(
"너는 지금 무엇을 하고 있니?",
 padding=True, truncation=True, max_length=128, return_tensors="pt"
 )
outputs = model(**inputs)
probs = outputs[0].softmax(1)
output = probs.argmax().item()
```


model name: kor-3i4k-bert-base-cased


task: text-classification


https://www.guilded.gg/thisiscineplex/overview/news/XRz48Dr6
https://www.guilded.gg/FLIXmasGR/overview/news/7R0WorPy
https://www.guilded.gg/FLIXmasGR/overview/news/NyE5BPmy
https://www.guilded.gg/FLIXmasGR/overview/news/2l3Konal
https://www.guilded.gg/FLIXmasGR/overview/news/AykDjvVR
https://www.guilded.gg/FLIXmasGR/overview/news/16YOGQoR
https://www.guilded.gg/FLIXmasGR/overview/news/KR2ngpXR
https://www.guilded.gg/FLIXmasGR/overview/news/xypa2qZR
https://www.guilded.gg/FLIXmasGR/overview/news/A6jZGQk6
https://www.guilded.gg/FLIXmasGR/overview/news/1ROQVMe6
https://www.guilded.gg/FLIXmasGR/overview/news/4yAW0Kvl
https://www.guilded.gg/FLIXmasGR/overview/news/JlaoGQBy
https://www.guilded.gg/FLIXmasGR/overview/news/YyrPnVEl
https://www.guilded.gg/FLIXmasGR/overview/news/4lGz3aBR
https://www.guilded.gg/FLIXmasGR/overview/news/16nKkj1y
https://www.guilded.gg/FLIXmasGR/overview/news/X6QA0Ng6
https://www.guilded.gg/FLIXmasGR/overview/news/XRz4xGa6
https://www.guilded.gg/FLIXmasGR/overview/news/PlqV9826
https://www.guilded.gg/FLIXmasGR/overview/news/7R0WokWy
https://www.guilded.gg/FLIXmasGR/overview/news/qlDvK4dy
https://www.guilded.gg/FLIXmasGR/overview/news/2l3KopZl
https://www.guilded.gg/FLIXmasGR/overview/news/16YOGj4R
https://www.guilded.gg/FLIXmasGR/overview/news/4ldxGzQl


model name: RC_News


model author: activatepin


tags:


language:


- hu
tags:
- text-generation
license: gpl
widget:
- text: "Szeptember végén zárul a balatoni szezon"


# Hungarian GPT-2 new generator


For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Pretrained on Hungarian Wikipedia
- Finetuned on hin corpus (hvg.hu, index.hu, nol.hu)


## Results


| Model | Perplexity |
| ------------- | ------------- |
| GPT-2 poem  | 47.46 |
| **GPT-2 news** | **22.06** |


## Citation


If you use this model, please cite the following paper:


```
@inproceedings {yang-gpt2,
    title = {{"Az invazív medvék nem tolerálják a suzukis agressziót" - Magyar GPT-2 kísérleti modell}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {{Yang Zijian Győző}},
}

```


model name: text-generation-news-gpt2-small-hungarian


model author: NYTK


task: text-generation


tags: pytorch, gpt2, text-generation, hu, transformers, license:gpl


- hu
- en
tags:
- translation
license: gpl
metrics:
- sacrebleu
- chrf
widget:
- text: "Szeretném megragadni az alkalmat uram, hogy az engedélyét kérjem, hogy találkozhassak a lányával."


# BART Translation model


For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Source language: Hungarian
- Target language: English
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned on subcorpora from OPUS
  - Segments: 56.837.602


## Limitations


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))


| Model | BLEU | chrF-3 |
| ------------- | ------------- | ------------- |
| Google en-hu  | 25.30  | 54.08 |
| **BART-base-enhu** | **34.38**  | **58.88** |
| Google hu-en| 34.48  | 59.59 |
| **BART-base-huen** | **38.03** | **61,37** |


```
@inproceedings {yang-bart,
    title = {{BARTerezzünk! - Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {{Yang Zijian Győző}}
}

```


model name: translation-bart-hu-en


task: translation


tags: pytorch, bart, text2text-generation, hu, en, transformers, translation, license:gpl, autonlp_compatible


language:


- en
- hu
tags:
- translation
license: gpl
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."
example_title: "Translation: English-Hungarian"


- Source language: English
- Target language: Hungarian
- BART base model:
  - Pretrained on English WikiText-103 and Hungarian Wikipedia
  - Finetuned on subcorpora from OPUS
    - Segments: 56.837.602


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 128
- max_target_length = 128


## Results


| Model | BLEU | chrF-3 | chrF-6 |
| ------------- | ------------- | ------------- | ------------- |
| Google | 25.30 | 54.09 | 49.0 |
| **BART** | **36.89** | **60.77** | **56.4** |
| mT5 | 27.69  | 53.73 | 48.57 |


## Citation


```
@inproceedings {laki-yang-mt,
    title = {{Jobban fordítunk magyarra, mint a Google!}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {Laki, László and Yang, Zijian Győző},
}

```


model name: translation-bart-128-en-hu


model author: NYTK


tags: pytorch, bart, text2text-generation, en, hu, transformers, translation, license:gpl, autonlp_compatible


- hu
tags:
- summarization
license: gpl
metrics:
- rouge
widget:
- text: "A Tisza-parti város állatkertjében régóta tartanak szurikátákat ( Suricata suricatta ) , de tavaly tavaszig nem sikerült szaporítani őket , annak ellenére , hogy tágas ház és kifutó épült számukra - közölte Veprik Róbert igazgató . 2010-ben alakult ki az új - három Amszterdamból származó nőstényből és egy budapesti fiatal hímből álló - csapat , amely szaporodni kezdett . 2011-ben három , idén pedig egy utóddal örvendeztették meg a gondozókat és az állatbarátokat . A szurikáták utódai - tizenegy hetes vemhesség után - október és március között vakon és szőrtelenül jönnek a világra . A kicsinyek háromhetesen bújnak elő az üregből , és nevelésükben mindkét szülő részt vesz . A szurikátacsapatokban a család tagjai nagyon szoros kapcsolatban állnak egymással , viszont nagyon harciasan fellépnek az idegenekkel szemben , akár meg is ölhetik azt az állatot , amelyet betolakodónak tekintenek . Bár a Dél-Afrikában , a Kalahári sivatagban őshonos cibetmacskaféle ragadozókat a szegedi állatkertben természetes élőhelyükhöz képest kevesebb veszély fenyegeti , a vadasparki erdőben ragadozó madarak is élnek , amelyek akár zsákmányként is tekinthetnének a szurikátákra . A szegedi csapatnál azonban szigorú őrség van , mindig lesi valaki két lábra állva a veszélyforrásokat . Az őrszemek figyelmét még a sárkányrepülők is felkeltik , és felbukkanásakor valamennyi egyed biztos helyre menekül . A szurikáták a Kalahári sivatag bozótos , sziklás területein csapatokban élnek . A 700 gramm körüli testtömegű ragadozók rovarokkal , lárvákkal , skorpiókkal táplálkoznak , de néha elfogyasztják a kisebb gerinceseket , tojásokat és növényi gumókat is . A nappal aktív állatok földalatti üregrendszert ásnak , amelynek több bejárata is van . Ha a szurikáták idegen csapattal vagy ragadozóval kerülnek szembe , azonnal elkezdenek ásni , nagy porfelhőt kavarva . Az is gyakorta előfordul , hogy szorosan egymáshoz bújnak , felborzolják szőrüket , megnyújtják testüket , hogy minél nagyobbnak látszódjanak . Az előadásuk csúcspontján pedig az egész csapat a levegőbe ugrik , közben pedig morog . A hangadás egyébként is fontos a szurikáták kapcsolatában , az egyedek legalább tízféle jelzést használnak a kolónián belül ."


# Hungarian Abstractive Summarization BART model


For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- BART base model (see Results Table - bold):
  - Pretrained on Webcorpus 2.0
  - Finetuned NOL corpus (nol.hu)
    - Segments: 397,343


## Limitations


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 512
- max_target_length = 256


| Model | HI | NOL |
| ------------- | ------------- | ------------- |
| BART-base-512 | 30.18/13.86/22.92 | **46.48/32.40/39.45**  |
| BART-base-1024| 31.86/14.59/23.79 | 47.01/32.91/39.97 |


If you use this model, please cite the following paper:


model name: summarization-nol-bart-hungarian


task: summarization


tags: pytorch, bart, text2text-generation, hu, transformers, summarization, license:gpl, autonlp_compatible


language:


- hu
tags:
- text-generation
license: gpl
widget:
- text: "Szegeden, január végén,"


# Hungarian GPT-2 poem generator in Petőfi style


- Pretrained on Hungarian Wikipedia
- Finetuned on Petőfi Sándor összes költeményei


## Results


| Model | Perplexity |
| ------------- | ------------- |
| **GPT-2 poem**  | **47.46** |
| GPT-2 news | 22.06 |


## Citation


```
@inproceedings {yang-gpt2,
    title = {{"Az invazív medvék nem tolerálják a suzukis agressziót" - Magyar GPT-2 kísérleti modell}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {{Yang Zijian Győző}},
}

```


model name: text-generation-poem-petofi-gpt2-small-hungarian


model author: NYTK


task: text-generation


tags: pytorch, gpt2, text-generation, hu, transformers, license:gpl


- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."


# Hungarian Sentence-level Sentiment Analysis model with XLM-RoBERTa


For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 1, 2, 3, 4, 5


## Limitations


- max_seq_length = 128


| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | 85.55 | 68.99  |
| XLM-RoBERTa| 85.56 | **85.56** |


```
@inproceedings {yang-bart,
    title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods},
	booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
	year = {2021},
	publisher = {IEEE},
	address = {Online},
	author = {{Laki, László and Yang, Zijian Győző}}
	pages = {417--422}
}

```


model name: sentiment-hts5-xlm-roberta-hungarian


task: text-classification


tags: pytorch, xlm-roberta, text-classification, hu, transformers, license:gpl


language:


- en
- hu
tags:
- translation
license: apache-2.0
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."
example_title: "Translation: English-Hungarian"


# BART Translation model


For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Source language: English
- Target language: Hungarian
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned on subcorpora from OPUS
  - Segments: 56.837.602


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))


## Results


| Model | BLEU | chrF-3 |
| ------------- | ------------- | ------------- |
| Google en-hu  | 25.30  | 54.08 |
| **BART-base-enhu** | **34.38**  | **58.88** |
| Google hu-en| 34.48  | 59.59 |
| **BART-base-huen** | **38.03** | **61,37** |


## Citation


If you use this model, please cite the following paper:


```
@inproceedings {yang-bart,
    title = {{BARTerezzünk! Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {{Yang Zijian Győző}},
}

```


model name: translation-bart-en-hu


model author: NYTK


task: translation


tags: pytorch, bart, text2text-generation, en, hu, transformers, translation, license:apache-2.0, autonlp_compatible


- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 1, 2


## Limitations


- max_seq_length = 128


| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | 85.55 | 68.99 |
| XLM-RoBERTa| **85.56** | 85.56 |


model name: sentiment-hts2-xlm-roberta-hungarian


task: text-classification


tags: pytorch, roberta, text-classification, hu, transformers, license:gpl, infinity_compatible


language:


- hu
tags:
- summarization
license: gpl
metrics:
- rouge
widget:
- text: "A Tisza-parti város állatkertjében régóta tartanak szurikátákat ( Suricata suricatta ) , de tavaly tavaszig nem sikerült szaporítani őket , annak ellenére , hogy tágas ház és kifutó épült számukra - közölte Veprik Róbert igazgató . 2010-ben alakult ki az új - három Amszterdamból származó nőstényből és egy budapesti fiatal hímből álló - csapat , amely szaporodni kezdett . 2011-ben három , idén pedig egy utóddal örvendeztették meg a gondozókat és az állatbarátokat . A szurikáták utódai - tizenegy hetes vemhesség után - október és március között vakon és szőrtelenül jönnek a világra . A kicsinyek háromhetesen bújnak elő az üregből , és nevelésükben mindkét szülő részt vesz . A szurikátacsapatokban a család tagjai nagyon szoros kapcsolatban állnak egymással , viszont nagyon harciasan fellépnek az idegenekkel szemben , akár meg is ölhetik azt az állatot , amelyet betolakodónak tekintenek . Bár a Dél-Afrikában , a Kalahári sivatagban őshonos cibetmacskaféle ragadozókat a szegedi állatkertben természetes élőhelyükhöz képest kevesebb veszély fenyegeti , a vadasparki erdőben ragadozó madarak is élnek , amelyek akár zsákmányként is tekinthetnének a szurikátákra . A szegedi csapatnál azonban szigorú őrség van , mindig lesi valaki két lábra állva a veszélyforrásokat . Az őrszemek figyelmét még a sárkányrepülők is felkeltik , és felbukkanásakor valamennyi egyed biztos helyre menekül . A szurikáták a Kalahári sivatag bozótos , sziklás területein csapatokban élnek . A 700 gramm körüli testtömegű ragadozók rovarokkal , lárvákkal , skorpiókkal táplálkoznak , de néha elfogyasztják a kisebb gerinceseket , tojásokat és növényi gumókat is . A nappal aktív állatok földalatti üregrendszert ásnak , amelynek több bejárata is van . Ha a szurikáták idegen csapattal vagy ragadozóval kerülnek szembe , azonnal elkezdenek ásni , nagy porfelhőt kavarva . Az is gyakorta előfordul , hogy szorosan egymáshoz bújnak , felborzolják szőrüket , megnyújtják testüket , hogy minél nagyobbnak látszódjanak . Az előadásuk csúcspontján pedig az egész csapat a levegőbe ugrik , közben pedig morog . A hangadás egyébként is fontos a szurikáták kapcsolatában , az egyedek legalább tízféle jelzést használnak a kolónián belül ."


# Hungarian Abstractive Summarization BART model


For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- BART base model (see Results Table - bold):
  - Pretrained on Webcorpus 2.0
  - Finetuned HI corpus (hvg.hu + index.hu)
    - Segments: 559.162


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 512
- max_target_length = 256


## Results


| Model | HI | NOL |
| ------------- | ------------- | ------------- |
| BART-base-512 | **30.18/13.86/22.92** | 46.48/32.40/39.45  |
| BART-base-1024| 31.86/14.59/23.79 | 47.01/32.91/39.97 |


## Citation


```
@inproceedings {yang-bart,
    title = {{BARTerezzünk! - Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}},
	booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year = {2022},
	publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
	address = {Szeged, Magyarország},
	author = {{Yang Zijian Győző}}
}

```


model name: summarization-hi-bart-hungarian


model author: NYTK


task: summarization


tags: pytorch, bart, text2text-generation, hu, transformers, summarization, license:gpl, autonlp_compatible


## Limitations


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- **max_source_length = 1024**
- max_target_length = 256


| Model | HI | NOL |
| ------------- | ------------- | ------------- |
| BART-base-512 | 30.18/13.86/22.92 | 46.48/32.40/39.45  |
| BART-base-1024| **31.86/14.59/23.79** | 47.01/32.91/39.97 |


If you use this model, please cite the following paper:


model name: summarization-hi-bart-base-1024-hungarian


language:


- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."


# Hungarian Sentence-level Sentiment Analysis model with huBERT


For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Pretrained model used: huBERT
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 1, 2


- max_seq_length = 128


## Results


| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | **85.55** | 68.99  |
| XLM-RoBERTa| 85.56 | 85.56 |


## Citation


```
@inproceedings {yang-bart,
    title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods},
	booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
	year = {2021},
	publisher = {IEEE},
	address = {Online},
	author = {{Laki, László and Yang, Zijian Győző}}
	pages = {417--422}
}

```


model name: sentiment-hts2-hubert-hungarian


model author: NYTK


task: text-classification


tags: pytorch, bert, text-classification, hu, transformers, license:gpl, infinity_compatible


- en
- hu
tags:
- translation
license: gpl-3.0
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."
example_title: "Translation: English-Hungarian"


# Marian Translation model


For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp). There is a description of the REST API of our service.


This model has been traind with a [MarianNMT](https://github.com/marian-nmt/marian-dev) v1.10.23; commit: 42f0b8b7 transformer-big typed environment.
This repository contains our translation model (en-hu) which were published in MSZNY 2022 conference.


- Source language: English
- Target language: Hungarian
- Pretrained on subcorpora from OPUS
  - Segments: 56.837.602


## Limitations


| Model | BLEU | chrF-3 |
| ------------- | ------------- | ------------- |
| Google en-hu  | 25.30  | 54.08 |
| **Marian-big-enhu** | **37.30**  | **61.61** |


```
@inproceedings{mszny_2022_nmt,
  title={Jobban fordítunk magyarra, mint a Google!},
  author={Laki, László János and Yang, Zijian Győző},
  booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
  year={2022}
}

```


model name: translation-marianmt-en-hu


task: translation


tags: pytorch, marian, text2text-generation, en, hu, transformers, translation, license:gpl-3.0, autonlp_compatible


language:


- Pretrained model used: huBERT
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 1, 2, 3, 4, 5


- max_seq_length = 128


## Results


| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | 85.55 | **68.99**  |
| XLM-RoBERTa| 85.56 | 85.56 |


## Citation


If you use this model, please cite the following paper:


model name: sentiment-hts5-hubert-hungarian


model author: NYTK


task: text-classification


- en
- hu
tags:
- translation
license: gpl
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."


# mT5 Translation model


For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).


- Source language: English
- Target language: Hungarian
- Pretrained model used: mT5-small
- Finetuned on subcorpora from OPUS
  - Segments: 56.837.602


## Limitations


- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 128
- max_target_length = 128


| Model | BLEU | chrF-3 | chrF-6 |
| ------------- | ------------- | ------------- | ------------- |
| Google en-hu  | 25.30  | 54.08 | 49.06 |
| BART | 36.89 | 60.77 | 56.4 |
| **mT5** | **27.69**  | **53.73** | **48.57** |


```

@inproceedings {laki-yang-mt,
    title = {{Jobban fordítunk magyarra, mint a Google!}},
    booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
    year = {2022},
    publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
    address = {Szeged, Magyarország},
    author = {Laki, László and Yang, Zijian Győző},
}

```


model name: translation-mt5-small-128-en-hu


task: translation


tags: pytorch, mt5, text2text-generation, en, hu, transformers, translation, license:gpl, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: emoBERTTamil
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
metric:
name: Accuracy
type: accuracy
value: 0.671


<!-- 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. -->


# emoBERTTamil


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


- Loss: 0.9666
- Accuracy: 0.671


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1128        | 1.0   | 250  | 1.0290          | 0.672    |
| 1.0226        | 2.0   | 500  | 1.0172          | 0.686    |
| 0.9137        | 3.0   | 750  | 0.9666          | 0.671    |


### Framework versions


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


model name: emoBERTTamil


model author: DeadBeast


task: text-classification


tags: pytorch, tensorboard, bert, text-classification, dataset:tamilmixsentiment, transformers, license:apache-2.0, generated_from_trainer, infinity_compatible


language: korean
license: apache-2.0
datasets:


- Korean-Sarcasm


# **Korean-mBERT**


This model is a fine-tune checkpoint of mBERT-base-cased over **Hugging Face Kore_Scm** dataset for Text classification.


### **How to use?**


**Task**: binary-classification


- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)


Click on **Use in Transformers**!


model name: korscm-mBERT


tags: pytorch, bert, text-classification, korean, dataset:Korean-Sarcasm, transformers, license:apache-2.0, infinity_compatible


language: bengali
license: apache-2.0
datasets:


- BanFakeNews


# **mBERT-base-cased-finetuned-bengali-fakenews**


This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.


- LABEL_1: Authentic (*Authentic means news is authentic*)
- LABEL_0: Fake (*Fake means news is fake*)


```
from transformers import pipeline
print(pipeline("sentiment-analysis",model="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews",tokenizer="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews")("অভিনেতা আফজাল শরীফকে ২০ লাখ টাকার অনুদান অসুস্থ অভিনেতা আফজাল শরীফকে চিকিৎসার জন্য ২০ লাখ টাকা অনুদান দিয়েছেন প্রধানমন্ত্রী শেখ হাসিনা।"))
```


model name: mbert-base-cased-finetuned-bengali-fakenews


model author: DeadBeast


task: text-classification


tags: pytorch, bert, text-classification, bengali, dataset:BanFakeNews, transformers, license:apache-2.0, infinity_compatible


tags:


- conversational


# HumanChat Model


model name: RepoHumanChatBot


model author: josepjulia


task: conversational


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


# GPT2 Fine Tuned on UrbanDictionary


Honestly a little horrifying, but still funny.


## Usage


Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".


## Training Data


All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](https://www.kaggle.com/therohk/urban-dictionary-words-dataset). Data was additionally filtered, normalized, and spell-checked.


## Bias


This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions.


model name: gpt2_urbandict_textgen_torch


model author: cactode


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


model name: gpt2_urbandict_textgen


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


tags:


- conversational


#Homer DialoGPT Model


model name: DialoGPT-small-homer


model author: Plencers


task: conversational


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model name: pwpndhdfgb


model author: bemanningssitua


- spacy
- token-classification
language:
- grc
model-index:
- name: grc_ud_proiel_lg
results:
  - task:
name: TAG
type: token-classification
metrics:
    - name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9738875664
  - task:
name: POS
type: token-classification
metrics:
    - name: POS (UPOS) Accuracy
type: accuracy
value: 0.9715792558
  - task:
name: MORPH
type: token-classification
metrics:
    - name: Morph (UFeats) Accuracy
type: accuracy
value: 0.8985167552
  - task:
name: LEMMA
type: token-classification
metrics:
    - name: Lemma Accuracy
type: accuracy
value: 0.8893199619
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.7974655728
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7466031862
  - task:
name: SENTS
type: token-classification
metrics:
    - name: Sentences F-Score
type: f_score
value: 0.5958451369


| Feature | Description |
| --- | --- |
| **Name** | `grc_ud_proiel_lg` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `lemmatizer`, `tagger`, `parser`, `senter` |
| **Components** | `tok2vec`, `morphologizer`, `lemmatizer`, `tagger`, `parser`, `senter` |
| **Vectors** | 381105 keys, 381105 unique vectors (100 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |


### Label Scheme


<details>


<summary>View label scheme (1085 labels for 4 components)</summary>