modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 00:37:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-13 00:35:18
| card
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M-CLIP/Swedish-2M
|
M-CLIP
| 2022-09-15T10:46:07Z | 154 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"sv",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
---
language: sv
---
<br />
<p align="center">
<h1 align="center">Swe-CLIP 2M</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/Swe-CLIP%202M">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the [Multilingual-CLIP Github](https://github.com/FreddeFrallan/Multilingual-CLIP).
Once this is done, you can load and use the model with the following code
```python
from src import multilingual_clip
model = multilingual_clip.load_model('Swe-CLIP-500k')
embeddings = model(['Älgen är skogens konung!', 'Alla isbjörnar är vänsterhänta'])
print(embeddings.shape)
# Yields: torch.Size([2, 640])
```
<!-- ABOUT THE PROJECT -->
## About
A [KB/Bert-Swedish-Cased](https://huggingface.co/KB/bert-base-swedish-cased) tuned to match the embedding space of the CLIP text encoder which accompanies the Res50x4 vision encoder. <br>
Training data pairs was generated by sampling 2 Million sentences from the combined descriptions of [GCC](https://ai.google.com/research/ConceptualCaptions/) + [MSCOCO](https://cocodataset.org/#home) + [VizWiz](https://vizwiz.org/tasks-and-datasets/image-captioning/), and translating them into Swedish.
All translation was done using the [Huggingface Opus Model](https://huggingface.co/Helsinki-NLP/opus-mt-en-sv), which seemingly procudes higher quality translations than relying on the [AWS translate service](https://aws.amazon.com/translate/).
|
M-CLIP/XLM-Roberta-Large-Vit-B-16Plus
|
M-CLIP
| 2022-09-15T10:45:56Z | 57,956 | 27 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"tl",
"te",
"tr",
"tk",
"uk",
"ur",
"ug",
"uz",
"vi",
"xh",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T21:33:14Z |
---
language:
- multilingual
- af
- sq
- am
- ar
- az
- bn
- bs
- bg
- ca
- zh
- hr
- cs
- da
- nl
- en
- et
- fr
- de
- el
- hi
- hu
- is
- id
- it
- ja
- mk
- ml
- mr
- pl
- pt
- ro
- ru
- sr
- sl
- es
- sw
- sv
- tl
- te
- tr
- tk
- uk
- ur
- ug
- uz
- vi
- xh
---
## Multilingual-clip: XLM-Roberta-Large-Vit-B-16Plus
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `Vit-B-16Plus` can be retrieved via instructions found on `mlfoundations` [open_clip repository on Github](https://github.com/mlfoundations/open_clip). We provide a usage example below.
## Requirements
To use both the multilingual text encoder and corresponding image encoder, we need to install the packages [`multilingual-clip`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`open_clip_torch`](https://github.com/mlfoundations/open_clip).
```
pip install multilingual-clip
pip install open_clip_torch
```
## Usage
Extracting embeddings from the text encoder can be done in the following way:
```python
from multilingual_clip import pt_multilingual_clip
import transformers
texts = [
'Three blind horses listening to Mozart.',
'Älgen är skogens konung!',
'Wie leben Eisbären in der Antarktis?',
'Вы знали, что все белые медведи левши?'
]
model_name = 'M-CLIP/XLM-Roberta-Large-Vit-B-16Plus'
# Load Model & Tokenizer
model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
embeddings = model.forward(texts, tokenizer)
print("Text features shape:", embeddings.shape)
```
Extracting embeddings from the corresponding image encoder:
```python
import torch
import open_clip
import requests
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
model.to(device)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
print("Image features shape:", image_features.shape)
```
## Evaluation results
None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following **R@10** results:
| Name | En | De | Es | Fr | Zh | It | Pl | Ko | Ru | Tr | Jp |
| ----------------------------------|:-----: |:-----: |:-----: |:-----: | :-----: |:-----: |:-----: |:-----: |:-----: |:-----: |:-----: |
| [OpenAI CLIP Vit-B/32](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - |
| [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - |
| [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - |
| [LABSE Vit-L/14](https://huggingface.co/M-CLIP/LABSE-Vit-L-14)| 91.6 | 89.6 | 89.5 | 89.9 | 88.9 | 90.1 | 89.8 | 80.8 | 85.5 | 89.8 | 73.9 |
| [XLM-R Large Vit-B/32](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-32)| 91.8 | 88.7 | 89.1 | 89.4 | 89.3 | 89.8| 91.4 | 82.1 | 86.1 | 88.8 | 81.0 |
| [XLM-R Vit-L/14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14)| 92.4 | 90.6 | 91.0 | 90.0 | 89.7 | 91.1 | 91.3 | 85.2 | 85.8 | 90.3 | 81.9 |
| [XLM-R Large Vit-B/16+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **95.0** | **93.0** | **93.6** | **93.1** | **94.0** | **93.1** | **94.4** | **89.0** | **90.0** | **93.0** | **84.2** |
## Training/Model details
Further details about the model training and data can be found in the [model card](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md).
|
M-CLIP/XLM-Roberta-Large-Vit-B-32
|
M-CLIP
| 2022-09-15T10:45:49Z | 13,427 | 15 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"M-CLIP",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"tl",
"te",
"tr",
"tk",
"uk",
"ur",
"ug",
"uz",
"vi",
"xh",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T09:50:54Z |
---
language:
- multilingual
- af
- sq
- am
- ar
- az
- bn
- bs
- bg
- ca
- zh
- hr
- cs
- da
- nl
- en
- et
- fr
- de
- el
- hi
- hu
- is
- id
- it
- ja
- mk
- ml
- mr
- pl
- pt
- ro
- ru
- sr
- sl
- es
- sw
- sv
- tl
- te
- tr
- tk
- uk
- ur
- ug
- uz
- vi
- xh
---
## Multilingual-clip: XLM-Roberta-Large-Vit-B-32
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-B-32` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.com/openai/CLIP). We provide a usage example below.
## Requirements
To use both the multilingual text encoder and corresponding image encoder, we need to install the packages [`multilingual-clip`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`clip`](https://github.com/openai/CLIP).
```
pip install multilingual-clip
pip install git+https://github.com/openai/CLIP.git
```
## Usage
Extracting embeddings from the text encoder can be done in the following way:
```python
from multilingual_clip import pt_multilingual_clip
import transformers
texts = [
'Three blind horses listening to Mozart.',
'Älgen är skogens konung!',
'Wie leben Eisbären in der Antarktis?',
'Вы знали, что все белые медведи левши?'
]
model_name = 'M-CLIP/XLM-Roberta-Large-Vit-B-32'
# Load Model & Tokenizer
model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
embeddings = model.forward(texts, tokenizer)
print("Text features shape:", embeddings.shape)
```
Extracting embeddings from the corresponding image encoder:
```python
import torch
import clip
import requests
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
print("Image features shape:", image_features.shape)
```
## Evaluation results
None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following **R@10** results:
| Name | En | De | Es | Fr | Zh | It | Pl | Ko | Ru | Tr | Jp |
| ----------------------------------|:-----: |:-----: |:-----: |:-----: | :-----: |:-----: |:-----: |:-----: |:-----: |:-----: |:-----: |
| [OpenAI CLIP Vit-B/32](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - |
| [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - |
| [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - |
| [LABSE Vit-L/14](https://huggingface.co/M-CLIP/LABSE-Vit-L-14)| 91.6 | 89.6 | 89.5 | 89.9 | 88.9 | 90.1 | 89.8 | 80.8 | 85.5 | 89.8 | 73.9 |
| [XLM-R Large Vit-B/32](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-32)| 91.8 | 88.7 | 89.1 | 89.4 | 89.3 | 89.8| 91.4 | 82.1 | 86.1 | 88.8 | 81.0 |
| [XLM-R Vit-L/14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14)| 92.4 | 90.6 | 91.0 | 90.0 | 89.7 | 91.1 | 91.3 | 85.2 | 85.8 | 90.3 | 81.9 |
| [XLM-R Large Vit-B/16+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **95.0** | **93.0** | **93.6** | **93.1** | **94.0** | **93.1** | **94.4** | **89.0** | **90.0** | **93.0** | **84.2** |
## Training/Model details
Further details about the model training and data can be found in the [model card](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md).
|
M-CLIP/XLM-Roberta-Large-Vit-L-14
|
M-CLIP
| 2022-09-15T10:44:59Z | 27,133 | 14 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"tl",
"te",
"tr",
"tk",
"uk",
"ur",
"ug",
"uz",
"vi",
"xh",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T14:35:41Z |
---
language:
- multilingual
- af
- sq
- am
- ar
- az
- bn
- bs
- bg
- ca
- zh
- hr
- cs
- da
- nl
- en
- et
- fr
- de
- el
- hi
- hu
- is
- id
- it
- ja
- mk
- ml
- mr
- pl
- pt
- ro
- ru
- sr
- sl
- es
- sw
- sv
- tl
- te
- tr
- tk
- uk
- ur
- ug
- uz
- vi
- xh
---
## Multilingual-clip: XLM-Roberta-Large-Vit-L-14
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-L-14` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.com/openai/CLIP). We provide a usage example below.
## Requirements
To use both the multilingual text encoder and corresponding image encoder, we need to install the packages [`multilingual-clip`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`clip`](https://github.com/openai/CLIP).
```
pip install multilingual-clip
pip install git+https://github.com/openai/CLIP.git
```
## Usage
Extracting embeddings from the text encoder can be done in the following way:
```python
from multilingual_clip import pt_multilingual_clip
import transformers
texts = [
'Three blind horses listening to Mozart.',
'Älgen är skogens konung!',
'Wie leben Eisbären in der Antarktis?',
'Вы знали, что все белые медведи левши?'
]
model_name = 'M-CLIP/XLM-Roberta-Large-Vit-L-14'
# Load Model & Tokenizer
model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
embeddings = model.forward(texts, tokenizer)
print("Text features shape:", embeddings.shape)
```
Extracting embeddings from the corresponding image encoder:
```python
import torch
import clip
import requests
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14", device=device)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
print("Image features shape:", image_features.shape)
```
## Evaluation results
None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following **R@10** results:
| Name | En | De | Es | Fr | Zh | It | Pl | Ko | Ru | Tr | Jp |
| ----------------------------------|:-----: |:-----: |:-----: |:-----: | :-----: |:-----: |:-----: |:-----: |:-----: |:-----: |:-----: |
| [OpenAI CLIP Vit-B/32](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - |
| [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - |
| [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - |
| [LABSE Vit-L/14](https://huggingface.co/M-CLIP/LABSE-Vit-L-14)| 91.6 | 89.6 | 89.5 | 89.9 | 88.9 | 90.1 | 89.8 | 80.8 | 85.5 | 89.8 | 73.9 |
| [XLM-R Large Vit-B/32](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-32)| 91.8 | 88.7 | 89.1 | 89.4 | 89.3 | 89.8| 91.4 | 82.1 | 86.1 | 88.8 | 81.0 |
| [XLM-R Vit-L/14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14)| 92.4 | 90.6 | 91.0 | 90.0 | 89.7 | 91.1 | 91.3 | 85.2 | 85.8 | 90.3 | 81.9 |
| [XLM-R Large Vit-B/16+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **95.0** | **93.0** | **93.6** | **93.1** | **94.0** | **93.1** | **94.4** | **89.0** | **90.0** | **93.0** | **84.2** |
## Training/Model details
Further details about the model training and data can be found in the [model card](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md).
|
mpapucci/it5-age-classification-tag-it
|
mpapucci
| 2022-09-15T10:31:04Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"T5",
"Text Classification",
"it",
"dataset:TAG-IT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-01T20:24:09Z |
---
language:
- it
tags:
- T5
- Text Classification
datasets:
- TAG-IT
---
Write an italian sentence with the prefix "Classifica Età: " to get an age classification of the sentence.
The dataset used for the task is: [TAG-IT](https://sites.google.com/view/tag-it-2020/).
The model is a fine tuned version of [IT5-base](https://huggingface.co/gsarti/it5-base) of Sarti and Nissim.
|
sd-concepts-library/cow-uwu
|
sd-concepts-library
| 2022-09-15T09:36:06Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T09:36:02Z |
---
license: mit
---
### cow uwu on Stable Diffusion
This is the `<cow-uwu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
oeg/esT5s-small
|
oeg
| 2022-09-15T09:31:00Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-09-15T08:50:26Z |
---
license: cc-by-nc-4.0
---
This is the small version (274MB) of the summarization model for the Spanish language presented in the SEMANTiCS 2022 conference (paper entitled "esT5s: A Spanish Model for Text Summarization"). This model was created in less than 1 hour (using a single GPU, specifically an NVIDIA v100 16GB) from the multilingual T5 model using the XL-Sum dataset. It achieves a ROUGE-1 value of 22.21 (mT5 achieves 26.21 after a 96h training using 4 GPUs), ROUGE-2 5.28 (mT5 achieves 8.74), and ROUGE-l 17.44 (mT5 achieves 21.06).
|
sd-concepts-library/renalla
|
sd-concepts-library
| 2022-09-15T09:23:43Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T09:23:40Z |
---
license: mit
---
### Renalla on Stable Diffusion
This is the `Renalla` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
yandex/RuLeanALBERT
|
yandex
| 2022-09-15T09:16:42Z | 36 | 32 |
transformers
|
[
"transformers",
"lean_albert",
"fill-mask",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-15T06:41:21Z |
---
language: ru
license: apache-2.0
---
RuLeanALBERT is a pretrained masked language model for the Russian language using a memory-efficient architecture.
Read more about the model in [this blog post](https://habr.com/ru/company/yandex/blog/688234/) (in Russian).
See its implementation, as well as the pretraining and finetuning code, at [https://github.com/yandex-research/RuLeanALBERT](https://github.com/yandex-research/RuLeanALBERT).
|
sd-concepts-library/style-of-marc-allante
|
sd-concepts-library
| 2022-09-15T07:48:41Z | 0 | 47 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-11T01:25:10Z |
---
license: mit
---
### Style-of-Marc-Allante on Stable Diffusion
This is the `<Marc_Allante>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
racheltong/wav2vec2-large-xlsr-chinese
|
racheltong
| 2022-09-15T07:36:59Z | 109 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-15T05:46:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-chinese
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-large-xlsr-chinese
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3216
- Cer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 16.1908 | 7.83 | 400 | 3.3216 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
lxj616/stable-diffusion-cn-poster
|
lxj616
| 2022-09-15T06:41:48Z | 0 | 9 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-09-15T05:31:45Z |
---
license: bigscience-bloom-rail-1.0
---
## Stable Diffusion With Chinese Characteristics
A finetuned stable diffusion on early chinese posters of farmers and workers
The meme text in sample are added manually afterwards
This prompt generate posters as original
```
a drawing of people in style of chinese propaganda poster
```
<img src="https://huggingface.co/lxj616/stable-diffusion-cn-poster/resolve/main/sample1.jpg" width=50% height=50%>
You may add some creativity to the prompt
```
a drawing of batman/superman in style of chinese propaganda poster
```
<img src="https://huggingface.co/lxj616/stable-diffusion-cn-poster/resolve/main/sample2.jpg" width=50% height=50%>
## License
See LICENSE.TXT from original stable-diffusion model repo
## Technical Details
On a single RTX 3090 Ti, 24G VRAM, 3 hours, 40 images of chinese posters, based on sd-v1.4
[Finetune stable diffusion under 24gb vram in hours](https://lxj616.github.io/jekyll/update/2022/09/12/finetune-stable-diffusion-under-24gb-vram-in-hours.html)
|
sd-concepts-library/ttte
|
sd-concepts-library
| 2022-09-15T06:33:45Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T06:33:31Z |
---
license: mit
---
### TTTE on Stable Diffusion
This is the `<ttte-2>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
slplab/wav2vec2_xlsr50k_english_phoneme
|
slplab
| 2022-09-15T05:10:10Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-29T15:42:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_xlsr50k_english_phoneme
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_xlsr50k_english_phoneme
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [the TIMIT dataset](https://catalog.ldc.upenn.edu/LDC93s1).
It achieves the following results on the evaluation set:
- Loss: 0.5783
- Cer: 0.1178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8403 | 6.94 | 500 | 1.1345 | 0.4657 |
| 0.5795 | 13.88 | 1000 | 0.3579 | 0.1169 |
| 0.3567 | 20.83 | 1500 | 0.3866 | 0.1174 |
| 0.2717 | 27.77 | 2000 | 0.4219 | 0.1169 |
| 0.2135 | 34.72 | 2500 | 0.4861 | 0.1199 |
| 0.1664 | 41.66 | 3000 | 0.5490 | 0.1179 |
| 0.1375 | 48.61 | 3500 | 0.5783 | 0.1178 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1
- Datasets 1.13.3
- Tokenizers 0.12.1
|
sd-concepts-library/babushork
|
sd-concepts-library
| 2022-09-15T04:29:38Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T04:29:27Z |
---
license: mit
---
### babushork on Stable Diffusion
This is the `<babushork>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
xusysh/Reinforce-test-1
|
xusysh
| 2022-09-15T03:12:25Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-15T03:10:22Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-test-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 462.70 +/- 92.29
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
sd-concepts-library/agm-style-nao
|
sd-concepts-library
| 2022-09-15T02:22:22Z | 0 | 35 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T02:13:10Z |
---
license: mit
---
### NOTE: USED WAIFU DIFFUSION
<https://huggingface.co/hakurei/waifu-diffusion>
### agm-style on Stable Diffusion
Artist: <https://www.pixiv.net/en/users/20670939>
This is the `<agm-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/uma-meme-style
|
sd-concepts-library
| 2022-09-15T02:16:00Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-15T02:15:36Z |
---
license: mit
---
### uma-meme-style on Stable Diffusion
This is the `<uma-meme-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
rebolforces/a2c-AntBulletEnv-v0
|
rebolforces
| 2022-09-15T00:55:27Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-14T20:35:00Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 2124.31 +/- 153.87
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Padomin/t5-base-TEDxJP-10front-1body-10rear
|
Padomin
| 2022-09-15T00:29:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-14T06:57:42Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-10front-1body-10rear
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. -->
# t5-base-TEDxJP-10front-1body-10rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4366
- Wer: 0.1693
- Mer: 0.1636
- Wil: 0.2493
- Wip: 0.7507
- Hits: 55904
- Substitutions: 6304
- Deletions: 2379
- Insertions: 2249
- Cer: 0.1332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6166 | 1.0 | 1457 | 0.4595 | 0.2096 | 0.1979 | 0.2878 | 0.7122 | 54866 | 6757 | 2964 | 3819 | 0.1793 |
| 0.4985 | 2.0 | 2914 | 0.4190 | 0.1769 | 0.1710 | 0.2587 | 0.7413 | 55401 | 6467 | 2719 | 2241 | 0.1417 |
| 0.4787 | 3.0 | 4371 | 0.4130 | 0.1728 | 0.1670 | 0.2534 | 0.7466 | 55677 | 6357 | 2553 | 2249 | 0.1368 |
| 0.4299 | 4.0 | 5828 | 0.4085 | 0.1726 | 0.1665 | 0.2530 | 0.7470 | 55799 | 6381 | 2407 | 2357 | 0.1348 |
| 0.3855 | 5.0 | 7285 | 0.4130 | 0.1702 | 0.1644 | 0.2501 | 0.7499 | 55887 | 6309 | 2391 | 2292 | 0.1336 |
| 0.3109 | 6.0 | 8742 | 0.4182 | 0.1732 | 0.1668 | 0.2525 | 0.7475 | 55893 | 6317 | 2377 | 2494 | 0.1450 |
| 0.3027 | 7.0 | 10199 | 0.4256 | 0.1691 | 0.1633 | 0.2486 | 0.7514 | 55949 | 6273 | 2365 | 2283 | 0.1325 |
| 0.2729 | 8.0 | 11656 | 0.4252 | 0.1709 | 0.1649 | 0.2503 | 0.7497 | 55909 | 6283 | 2395 | 2362 | 0.1375 |
| 0.2531 | 9.0 | 13113 | 0.4329 | 0.1696 | 0.1639 | 0.2499 | 0.7501 | 55870 | 6322 | 2395 | 2235 | 0.1334 |
| 0.2388 | 10.0 | 14570 | 0.4366 | 0.1693 | 0.1636 | 0.2493 | 0.7507 | 55904 | 6304 | 2379 | 2249 | 0.1332 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
mlaricheva/roberta-psych
|
mlaricheva
| 2022-09-14T23:37:07Z | 170 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2208.06525",
"doi:10.57967/hf/1497",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-09T22:44:25Z |
# roberta-psych
---
language: en
---
This is a [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf) model pretrained on Alexander Street Database of Counselling and Psychotherapy Transcripts (see more about database and its content [here](https://alexanderstreet.com/products/counseling-and-psychotherapy-transcripts-series)).
Further information about training, parameters and evaluation is available in our paper:
Laricheva, M., Zhang, C., Liu, Y., Chen, G., Tracey, T., Young, R., & Carenini, G. (2022). [Automated Utterance Labeling of Conversations Using Natural Language Processing.](https://arxiv.org/abs/2208.06525) arXiv preprint arXiv:2208.06525
---
license: cc-by-nc-sa-2.0
---
|
Padomin/t5-base-TEDxJP-9front-1body-9rear
|
Padomin
| 2022-09-14T23:16:48Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-14T06:57:11Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-9front-1body-9rear
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. -->
# t5-base-TEDxJP-9front-1body-9rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4361
- Wer: 0.1687
- Mer: 0.1630
- Wil: 0.2486
- Wip: 0.7514
- Hits: 55941
- Substitutions: 6292
- Deletions: 2354
- Insertions: 2252
- Cer: 0.1338
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6124 | 1.0 | 1457 | 0.4613 | 0.2407 | 0.2209 | 0.3091 | 0.6909 | 54843 | 6758 | 2986 | 5804 | 0.2153 |
| 0.4968 | 2.0 | 2914 | 0.4171 | 0.1777 | 0.1716 | 0.2580 | 0.7420 | 55404 | 6354 | 2829 | 2293 | 0.1402 |
| 0.4817 | 3.0 | 4371 | 0.4129 | 0.1731 | 0.1673 | 0.2534 | 0.7466 | 55636 | 6332 | 2619 | 2227 | 0.1349 |
| 0.4257 | 4.0 | 5828 | 0.4089 | 0.1722 | 0.1659 | 0.2520 | 0.7480 | 55904 | 6346 | 2337 | 2437 | 0.1361 |
| 0.3831 | 5.0 | 7285 | 0.4144 | 0.1705 | 0.1646 | 0.2508 | 0.7492 | 55868 | 6343 | 2376 | 2290 | 0.1358 |
| 0.3057 | 6.0 | 8742 | 0.4198 | 0.1690 | 0.1632 | 0.2492 | 0.7508 | 55972 | 6333 | 2282 | 2298 | 0.1350 |
| 0.2919 | 7.0 | 10199 | 0.4220 | 0.1693 | 0.1635 | 0.2492 | 0.7508 | 55936 | 6310 | 2341 | 2281 | 0.1337 |
| 0.2712 | 8.0 | 11656 | 0.4252 | 0.1688 | 0.1632 | 0.2487 | 0.7513 | 55905 | 6286 | 2396 | 2218 | 0.1348 |
| 0.2504 | 9.0 | 13113 | 0.4332 | 0.1685 | 0.1629 | 0.2482 | 0.7518 | 55931 | 6270 | 2386 | 2226 | 0.1331 |
| 0.2446 | 10.0 | 14570 | 0.4361 | 0.1687 | 0.1630 | 0.2486 | 0.7514 | 55941 | 6292 | 2354 | 2252 | 0.1338 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/thunderdome-cover
|
sd-concepts-library
| 2022-09-14T23:12:38Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T23:12:33Z |
---
license: mit
---
### thunderdome-cover on Stable Diffusion
This is the `<thunderdome-cover>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/uma-meme
|
sd-concepts-library
| 2022-09-14T23:08:43Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T23:08:20Z |
---
license: mit
---
### uma-meme on Stable Diffusion
This is the `<uma-object-full>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/mayor-richard-irvin
|
sd-concepts-library
| 2022-09-14T23:07:45Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T23:07:34Z |
---
license: mit
---
### mayor-richard-irvin on Stable Diffusion
This is the `<Richard_Irvin>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/buddha-statue
|
sd-concepts-library
| 2022-09-14T22:23:55Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T22:23:48Z |
---
license: mit
---
### Buddha statue on Stable Diffusion
This is the `<buddha-statue>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/retro-girl
|
sd-concepts-library
| 2022-09-14T21:34:09Z | 0 | 9 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T21:33:58Z |
---
license: mit
---
### Retro-Girl on Stable Diffusion
This is the `<retro-girl>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
michael20at/q-Taxi-v3
|
michael20at
| 2022-09-14T21:20:43Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-14T21:16:38Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sd-concepts-library/collage3-hubcity
|
sd-concepts-library
| 2022-09-14T20:42:35Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T05:37:48Z |
---
license: mit
---
### Collage3-HubCity on Stable Diffusion
This is the `<C3Hub>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
DamianCummins/distilbert-base-uncased-finetuned-ner
|
DamianCummins
| 2022-09-14T20:03:13Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-14T17:54:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: DamianCummins/distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# DamianCummins/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0556
- Validation Loss: 0.0608
- Train Precision: 0.9196
- Train Recall: 0.9304
- Train F1: 0.9250
- Train Accuracy: 0.9820
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.0556 | 0.0608 | 0.9196 | 0.9304 | 0.9250 | 0.9820 | 0 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.9.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
michael20at/q-FrozenLake-v1-4x4-noSlippery
|
michael20at
| 2022-09-14T20:03:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-14T20:03:02Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="michael20at/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sd-concepts-library/rektguy
|
sd-concepts-library
| 2022-09-14T19:39:29Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T19:39:24Z |
---
license: mit
---
### rektguy on Stable Diffusion
This is the `<rektguy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
matemato/ppo-LunarLander-v2
|
matemato
| 2022-09-14T18:58:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-14T18:57:33Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 191.85 +/- 23.17
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sd-concepts-library/joe-whiteford-art-style
|
sd-concepts-library
| 2022-09-14T18:43:02Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T18:42:46Z |
---
license: mit
---
### Joe Whiteford Art Style on Stable Diffusion
This is the `<joe-whiteford-artstyle>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/my-mug
|
sd-concepts-library
| 2022-09-14T17:53:46Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T17:53:40Z |
---
license: mit
---
### My mug on Stable Diffusion
This is the `<my-mug>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
rvidaurre/ddpm-butterflies-128
|
rvidaurre
| 2022-09-14T17:21:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-14T16:06:53Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/rvidaurre/ddpm-butterflies-128/tensorboard?#scalars)
|
sd-concepts-library/sterling-archer
|
sd-concepts-library
| 2022-09-14T17:00:25Z | 0 | 13 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T17:00:11Z |
---
license: mit
---
### Sterling-Archer on Stable Diffusion
This is the `<archer-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
VietAI/vit5-base-vietnews-summarization
|
VietAI
| 2022-09-14T16:46:02Z | 543 | 7 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"summarization",
"vi",
"dataset:cc100",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-09-07T02:47:53Z |
---
language: vi
datasets:
- cc100
tags:
- summarization
license: mit
widget:
- text: "VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
---
# ViT5-Base Finetuned on `vietnews` Abstractive Summarization (No prefix needed)
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
[](https://paperswithcode.com/sota/abstractive-text-summarization-on-vietnews?p=vit5-pretrained-text-to-text-transformer-for)
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5) and [eval script](https://github.com/vietai/ViT5/blob/main/eval/Eval_vietnews_sum.ipynb).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base-vietnews-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base-vietnews-summarization")
model.cuda()
sentence = "VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
sentence = sentence + "</s>"
encoding = tokenizer(sentence, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
early_stopping=True
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(line)
```
## Citation
```
@inproceedings{phan-etal-2022-vit5,
title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.18",
pages = "136--142",
}
```
|
jcastanyo/Reinforce-CP-v0
|
jcastanyo
| 2022-09-14T16:19:16Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-14T16:18:12Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CP-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 62.30 +/- 30.18
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
sd-concepts-library/neon-pastel
|
sd-concepts-library
| 2022-09-14T15:55:45Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T15:47:29Z |
---
license: mit
---
### Neon Pastel on Stable Diffusion
This is the `<neon-pastel>` style taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here are some of the outputs from this model:
Prompt: the taj mahal in `<neon-pastel>` style
Prompt: portrait of barack obama in `<neon-pastel>` style
Prompt: a beautiful beach landscape in `<neon-pastel>` style
|
theunnecessarythings/ddpm-ema-flowers-64
|
theunnecessarythings
| 2022-09-14T15:54:23Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/flowers-102-categories",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-14T14:15:13Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/flowers-102-categories
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-flowers-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/flowers-102-categories` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: cosine
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/sreerajr000/ddpm-ema-flowers-64/tensorboard?#scalars)
|
sd-concepts-library/tb303
|
sd-concepts-library
| 2022-09-14T15:26:31Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T15:26:17Z |
---
license: mit
---
### TB303 on Stable Diffusion
This is the `<"tb303>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
MayaGalvez/bert-base-multilingual-cased-finetuned-multilingual-nli
|
MayaGalvez
| 2022-09-14T15:24:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-14T13:25:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-multilingual-cased-finetuned-multilingual-nli_newdata_oneepoch
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. -->
# bert-base-multilingual-cased-finetuned-multilingual-nli_newdata_oneepoch
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: 0.7647
- Accuracy: 0.6853
- Precision: 0.6932
- Recall: 0.6853
- F1: 0.6847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9394 | 0.04 | 500 | 0.9044 | 0.592 | 0.5985 | 0.592 | 0.5917 |
| 0.8603 | 0.08 | 1000 | 0.9159 | 0.579 | 0.6210 | 0.579 | 0.5739 |
| 0.8293 | 0.11 | 1500 | 0.8520 | 0.6214 | 0.6278 | 0.6214 | 0.6215 |
| 0.8042 | 0.15 | 2000 | 0.8085 | 0.6418 | 0.6439 | 0.6418 | 0.6414 |
| 0.7945 | 0.19 | 2500 | 0.8251 | 0.6319 | 0.6575 | 0.6319 | 0.6262 |
| 0.7768 | 0.23 | 3000 | 0.8298 | 0.6383 | 0.6556 | 0.6383 | 0.6365 |
| 0.753 | 0.27 | 3500 | 0.8225 | 0.6464 | 0.6684 | 0.6464 | 0.6436 |
| 0.754 | 0.3 | 4000 | 0.7979 | 0.6529 | 0.6750 | 0.6529 | 0.6523 |
| 0.7466 | 0.34 | 4500 | 0.7644 | 0.6718 | 0.6727 | 0.6718 | 0.6713 |
| 0.7331 | 0.38 | 5000 | 0.7861 | 0.6591 | 0.6757 | 0.6591 | 0.6581 |
| 0.72 | 0.42 | 5500 | 0.7972 | 0.6595 | 0.6815 | 0.6595 | 0.6582 |
| 0.7103 | 0.46 | 6000 | 0.7652 | 0.6702 | 0.6728 | 0.6702 | 0.6688 |
| 0.7103 | 0.49 | 6500 | 0.7732 | 0.6684 | 0.6796 | 0.6684 | 0.6670 |
| 0.7023 | 0.53 | 7000 | 0.7921 | 0.6657 | 0.6834 | 0.6657 | 0.6663 |
| 0.6827 | 0.57 | 7500 | 0.7672 | 0.6733 | 0.6824 | 0.6733 | 0.6726 |
| 0.6826 | 0.61 | 8000 | 0.7665 | 0.6755 | 0.6789 | 0.6755 | 0.6747 |
| 0.6705 | 0.65 | 8500 | 0.7659 | 0.6755 | 0.6815 | 0.6755 | 0.6748 |
| 0.662 | 0.68 | 9000 | 0.7738 | 0.6767 | 0.6833 | 0.6767 | 0.6757 |
| 0.6556 | 0.72 | 9500 | 0.7623 | 0.6805 | 0.6906 | 0.6805 | 0.6799 |
| 0.6462 | 0.76 | 10000 | 0.7863 | 0.6719 | 0.6849 | 0.6719 | 0.6701 |
| 0.6405 | 0.8 | 10500 | 0.7523 | 0.681 | 0.6845 | 0.681 | 0.6805 |
| 0.6407 | 0.84 | 11000 | 0.7661 | 0.6807 | 0.6856 | 0.6807 | 0.6801 |
| 0.6341 | 0.87 | 11500 | 0.7672 | 0.6787 | 0.6904 | 0.6787 | 0.6770 |
| 0.6292 | 0.91 | 12000 | 0.7742 | 0.682 | 0.6922 | 0.682 | 0.6803 |
| 0.6238 | 0.95 | 12500 | 0.7584 | 0.6855 | 0.6926 | 0.6855 | 0.6850 |
| 0.6201 | 0.99 | 13000 | 0.7647 | 0.6853 | 0.6932 | 0.6853 | 0.6847 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
JAS100/bert-finetuned-ner
|
JAS100
| 2022-09-14T14:28:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-14T14:10:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9330024813895782
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.9410194377242012
- name: Accuracy
type: accuracy
value: 0.9865926885265203
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
- Precision: 0.9330
- Recall: 0.9492
- F1: 0.9410
- Accuracy: 0.9866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0852 | 1.0 | 1756 | 0.0722 | 0.9149 | 0.9295 | 0.9221 | 0.9814 |
| 0.0353 | 2.0 | 3512 | 0.0593 | 0.9223 | 0.9492 | 0.9356 | 0.9863 |
| 0.018 | 3.0 | 5268 | 0.0620 | 0.9330 | 0.9492 | 0.9410 | 0.9866 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Abdulmateen/bert-finetuned-ner
|
Abdulmateen
| 2022-09-14T14:26:48Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-13T05:49:10Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Abdulmateen/bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Abdulmateen/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1865
- Validation Loss: 0.1351
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4679 | 0.2717 | 0 |
| 0.2578 | 0.1703 | 1 |
| 0.1865 | 0.1351 | 2 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BlinkDL/rwkv-3-pile-1b5
|
BlinkDL
| 2022-09-14T13:54:48Z | 0 | 7 | null |
[
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2022-06-23T11:44:36Z |
---
language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- The Pile
---
# RWKV-3 1.5B
## Model Description
RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
RWKV-4 1.5B is out: https://huggingface.co/BlinkDL/rwkv-4-pile-1b5
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 896
n_layer = 24
n_embd = 2048
Preview checkpoint: RWKV-3-Pile-20220723-3542.pth : Trained on the Pile for 127B tokens.
* Pile loss 2.102
* LAMBADA ppl 7.52, acc 54.71%
* PIQA acc 71.11%
* SC2016 acc 67.24%
* Hellaswag acc_norm 50.45%
Preview checkpoint: 20220708-1905.pth : Trained on the Pile for 68B tokens.
* Pile loss 2.148
* LAMBADA ppl 8.41, acc 53.17%
* PIQA acc 69.64%
* SC2016 acc 67.08%
* Hellaswag acc_norm 48.20%
(I am still training it)
|
BlinkDL/rwkv-2-pile-430m
|
BlinkDL
| 2022-09-14T13:54:23Z | 0 | 4 | null |
[
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2022-06-23T03:09:51Z |
---
language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- The Pile
---
# RWKV-2 430M
## Model Description
RWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 768 n_layer = 24 n_embd = 1024
Final checkpoint: 20220615-10803.pth : Trained on the Pile for 331B tokens.
* Pile loss 2.349
* LAMBADA ppl 15.34, acc 42.42%
* PIQA acc 67.03%
* SC2016 acc 62.05%
* Hellaswag acc_norm 38.47%
|
NoaG/try
|
NoaG
| 2022-09-14T12:26:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-14T12:09:04Z |
# Dummy model
This is a Dummy model.
|
burakyldrm/wav2vec2-burak-new-300-v2
|
burakyldrm
| 2022-09-14T12:10:15Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-14T03:30:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-burak-new-300-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-burak-new-300-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7018
- Wer: 0.3641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 500
- num_epochs: 141
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.2967 | 8.62 | 500 | 1.0561 | 0.8351 |
| 0.5199 | 17.24 | 1000 | 0.6019 | 0.5054 |
| 0.2249 | 25.86 | 1500 | 0.6036 | 0.4576 |
| 0.1573 | 34.48 | 2000 | 0.6680 | 0.4532 |
| 0.1301 | 43.1 | 2500 | 0.6823 | 0.4396 |
| 0.1108 | 51.72 | 3000 | 0.6630 | 0.4263 |
| 0.0941 | 60.34 | 3500 | 0.6574 | 0.4226 |
| 0.0797 | 68.97 | 4000 | 0.6796 | 0.4174 |
| 0.0689 | 77.59 | 4500 | 0.6426 | 0.4088 |
| 0.0612 | 86.21 | 5000 | 0.6615 | 0.3949 |
| 0.0528 | 94.83 | 5500 | 0.6769 | 0.3906 |
| 0.0515 | 103.45 | 6000 | 0.6073 | 0.3827 |
| 0.0394 | 112.07 | 6500 | 0.7099 | 0.3767 |
| 0.0352 | 120.69 | 7000 | 0.7082 | 0.3688 |
| 0.0324 | 129.31 | 7500 | 0.6939 | 0.3699 |
| 0.028 | 137.93 | 8000 | 0.7018 | 0.3641 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/junji-ito-artstyle
|
sd-concepts-library
| 2022-09-14T11:45:57Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T11:45:43Z |
---
license: mit
---
### Junji Ito ArtStyle on Stable Diffusion
This is the `<junji-ito-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/breakcore
|
sd-concepts-library
| 2022-09-14T11:39:49Z | 0 | 5 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T11:39:45Z |
---
license: mit
---
### breakcore on Stable Diffusion
This is the `breakcore` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
theojolliffe/bart-model2-1409
|
theojolliffe
| 2022-09-14T11:35:12Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-14T09:28:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-model2-1409
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. -->
# bart-model2-1409
This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 434 | 0.0866 | 65.9374 | 57.1515 | 64.1273 | 64.1253 | 19.787 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0
- Datasets 1.18.0
- Tokenizers 0.10.3
|
michael20at/ppo-LunarLander-v2
|
michael20at
| 2022-09-14T11:23:41Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-12T05:17:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 261.95 +/- 15.45
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
1ucky40nc3/wav2vec2-common_voice-tr-demo
|
1ucky40nc3
| 2022-09-14T11:12:34Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-14T08:47:52Z |
---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-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. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3798
- Wer: 0.3448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.92 | 100 | 3.5932 | 1.0 |
| No log | 1.83 | 200 | 3.0185 | 0.9999 |
| No log | 2.75 | 300 | 0.9357 | 0.8007 |
| No log | 3.67 | 400 | 0.5945 | 0.6318 |
| 3.1829 | 4.59 | 500 | 0.4931 | 0.5265 |
| 3.1829 | 5.5 | 600 | 0.4757 | 0.4784 |
| 3.1829 | 6.42 | 700 | 0.4282 | 0.4540 |
| 3.1829 | 7.34 | 800 | 0.3995 | 0.4252 |
| 3.1829 | 8.26 | 900 | 0.4046 | 0.4149 |
| 0.2215 | 9.17 | 1000 | 0.4048 | 0.3951 |
| 0.2215 | 10.09 | 1100 | 0.3944 | 0.3865 |
| 0.2215 | 11.01 | 1200 | 0.3853 | 0.3643 |
| 0.2215 | 11.93 | 1300 | 0.3950 | 0.3632 |
| 0.2215 | 12.84 | 1400 | 0.3836 | 0.3506 |
| 0.1009 | 13.76 | 1500 | 0.3808 | 0.3510 |
| 0.1009 | 14.68 | 1600 | 0.3807 | 0.3456 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/reksio-dog
|
sd-concepts-library
| 2022-09-14T10:47:52Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T10:47:46Z |
---
license: mit
---
### reksio dog on Stable Diffusion
This is the `<reksio-dog>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sguskin/dynamic-minilmv2-L6-H384-squad1.1
|
sguskin
| 2022-09-14T10:15:13Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"dataset:SQuAD1.1",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-28T07:29:30Z |
---
license: "mit"
datasets:
- SQuAD1.1
---
|
IIIT-L/xlm-roberta-large-finetuned-TRAC-DS
|
IIIT-L
| 2022-09-14T09:45:42Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-14T07:48:15Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-large-finetuned-TRAC-DS
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. -->
# xlm-roberta-large-finetuned-TRAC-DS
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0992
- Accuracy: 0.3342
- Precision: 0.1114
- Recall: 0.3333
- F1: 0.1670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.1187640010910775e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1358 | 0.25 | 612 | 1.1003 | 0.4436 | 0.1479 | 0.3333 | 0.2049 |
| 1.1199 | 0.5 | 1224 | 1.1130 | 0.4436 | 0.1479 | 0.3333 | 0.2049 |
| 1.1221 | 0.75 | 1836 | 1.0992 | 0.3342 | 0.1114 | 0.3333 | 0.1670 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sd-concepts-library/chillpill
|
sd-concepts-library
| 2022-09-14T09:44:54Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T20:30:22Z |
---
license: mit
---
### Chillpill on Stable Diffusion
This is the `<Chillpill>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
MiguelCosta/distlBert-base-uncased-20Newsgroups
|
MiguelCosta
| 2022-09-14T09:34:23Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-14T09:34:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distlBert-base-uncased-20Newsgroups
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distlBert-base-uncased-20Newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3180, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
NBF/distilbert-base-uncased-finetuned-cola
|
NBF
| 2022-09-14T08:56:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-13T10:25:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5512772054945002
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8147
- Matthews Correlation: 0.5513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.532 | 1.0 | 535 | 0.5646 | 0.3988 |
| 0.3638 | 2.0 | 1070 | 0.5168 | 0.4911 |
| 0.2456 | 3.0 | 1605 | 0.5489 | 0.5414 |
| 0.183 | 4.0 | 2140 | 0.7633 | 0.5437 |
| 0.1375 | 5.0 | 2675 | 0.8147 | 0.5513 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.10.3
|
sd-concepts-library/naval-portrait
|
sd-concepts-library
| 2022-09-14T08:44:51Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T08:44:36Z |
---
license: mit
---
### naval-portrait on Stable Diffusion
This is the `<naval-portrait>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
thkkvui/distilbert-base-uncased-finetuned-emotion
|
thkkvui
| 2022-09-14T08:31:47Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-14T08:08:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249199121289653
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2240
- Accuracy: 0.925
- F1: 0.9249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8487 | 1.0 | 250 | 0.3310 | 0.9045 | 0.9011 |
| 0.2606 | 2.0 | 500 | 0.2240 | 0.925 | 0.9249 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.13.0.dev20220711
- Datasets 2.4.0
- Tokenizers 0.12.1
|
hubtype/distilbert-base-uncased-nonsense
|
hubtype
| 2022-09-14T08:09:05Z | 3,576 | 3 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-13T15:43:03Z |
## Definition
This text classification model detects whenever a text has no sense.
## Usage Recommendations
- **max\_length**: 128
- **padding**: "max_length"
- **truncation**: True
## Performance
- **Accuracy**: 99\%
|
mishig/temp-model
|
mishig
| 2022-09-14T08:07:18Z | 0 | 1 | null |
[
"layoutlm",
"pdf",
"document-question-answering",
"en",
"license:mit",
"region:us"
] |
document-question-answering
| 2022-05-25T09:59:02Z |
---
pipeline_tag: document-question-answering
language: en
license: mit
tags:
- layoutlm
- pdf
---
# LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets.
## Getting started with the model
To run these examples, you must have [PIL](https://pillow.readthedocs.io/en/stable/installation.html), [pytesseract](https://pypi.org/project/pytesseract/), and [PyTorch](https://pytorch.org/get-started/locally/) installed in addition to [transformers](https://huggingface.co/docs/transformers/index).
```python
from transformers import pipeline
nlp = pipeline(
"document-question-answering",
model="impira/layoutlm-document-qa",
)
nlp(
"https://templates.invoicehome.com/invoice-template-us-neat-750px.png",
"What is the invoice number?"
)
# {'score': 0.9943977, 'answer': 'us-001', 'start': 15, 'end': 15}
nlp(
"https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg",
"What is the purchase amount?"
)
# {'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97}
nlp(
"https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png",
"What are the 2020 net sales?"
)
# {'score': 0.59147286, 'answer': '$ 3,750', 'start': 19, 'end': 20}
```
**NOTE**: This model and pipeline was recently landed in transformers via [PR #18407](https://github.com/huggingface/transformers/pull/18407) and [PR #18414](https://github.com/huggingface/transformers/pull/18414), so you'll need to use a recent version of transformers, for example:
```bash
pip install git+https://github.com/huggingface/transformers.git@2ef774211733f0acf8d3415f9284c49ef219e991
```
## About us
This model was created by the team at [Impira](https://www.impira.com/).
|
rajpurkar/results
|
rajpurkar
| 2022-09-14T08:01:27Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-14T07:55:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 13 | 3.1802 |
| No log | 2.0 | 26 | 3.1813 |
| No log | 3.0 | 39 | 3.1822 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.13.0.dev20220912
- Datasets 2.4.0
- Tokenizers 0.11.0
|
Vasanth/image_captioner_vit_gpt2
|
Vasanth
| 2022-09-14T07:47:02Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-09-13T23:49:03Z |
---
tags:
- generated_from_trainer
model-index:
- name: image_captioner_vit_gpt2
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. -->
# image_captioner_vit_gpt2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 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
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
sujathass/bert-base-uncased-finetuned-cola
|
sujathass
| 2022-09-14T06:37:34Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-14T03:35:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5910926879747481
---
<!-- 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-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4822
- Matthews Correlation: 0.5911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4768 | 0.4943 |
| 0.4116 | 2.0 | 536 | 0.4275 | 0.5803 |
| 0.4116 | 3.0 | 804 | 0.4822 | 0.5911 |
| 0.1884 | 4.0 | 1072 | 0.5984 | 0.5880 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
paust/pko-t5-base
|
paust
| 2022-09-14T04:44:45Z | 884 | 18 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-16T11:59:13Z |
---
language: ko
license: cc-by-4.0
---
# pko-t5-base
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다.
pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다.
## Usage
transformers 의 API 를 사용하여 접근 가능합니다. tokenizer 를 사용할때는 `T5Tokenizer` 가 아니라 `T5TokenizerFast` 를 사용해주십시오. model 은 T5ForConditionalGeneration 를 그대로 활용하시면 됩니다.
### Example
```python
from transformers import T5TokenizerFast, T5ForConditionalGeneration
tokenizer = T5TokenizerFast.from_pretrained('paust/pko-t5-base')
model = T5ForConditionalGeneration.from_pretrained('paust/pko-t5-base')
input_ids = tokenizer(["qa question: 당신의 이름은 무엇인가요?"]).input_ids
labels = tokenizer(["T5 입니다."]).input_ids
outputs = model(input_ids=input_ids, labels=labels)
print(f"loss={outputs.loss} logits={outputs.logits}")
```
## Klue 평가 (dev)
| | Model | ynat (macro F1) | sts (pearsonr/F1) | nli (acc) | ner (entity-level F1) | re (micro F1) | dp (LAS) | mrc (EM/F1) |
|-----|------------------------------------------------------------------|-----------------|-------------------|-----------|-----------------------|---------------|-----------|-------------|
| | Baseline | **87.30** | **93.20/86.13** | **89.50** | 86.06 | 71.06 | 87.93 | **75.26/-** |
| FT | [pko-t5-small](https://huggingface.co/paust/pko-t5-small) (77M) | 86.21 | 77.99/77.01 | 69.20 | 82.60 | 66.46 | 93.15 | 43.81/46.58 |
| FT | [pko-t5-base](https://huggingface.co/paust/pko-t5-base) (250M) | 87.29 | 90.25/83.43 | 79.73 | 87.80 | 67.23 | 97.28 | 61.53/64.74 |
| FT | [pko-t5-large](https://huggingface.co/paust/pko-t5-large) (800M) | 87.12 | 92.05/85.24 | 84.96 | **88.18** | **75.17** | **97.60** | 68.01/71.44 |
| MT | pko-t5-small | 84.54 | 68.50/72/02 | 51.16 | 74.69 | 66.11 | 80.40 | 43.60/46.28 |
| MT | pko-t5-base | 86.89 | 83.96/80.30 | 72.03 | 85.27 | 66.59 | 95.05 | 61.11/63.94 |
| MT | pko-t5-large | 87.57 | 91.93/86.29 | 83.63 | 87.41 | 71.34 | 96.99 | 70.70/73.72 |
- FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝
- [Baseline](https://arxiv.org/abs/2105.09680): KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수
## License
[PAUST](https://paust.io)에서 만든 pko-t5는 [MIT license](https://github.com/paust-team/pko-t5/blob/main/LICENSE) 하에 공개되어 있습니다.
|
paust/pko-t5-small
|
paust
| 2022-09-14T04:41:53Z | 386 | 5 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-16T10:26:56Z |
---
language: ko
license: cc-by-4.0
---
# pko-t5-small
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다.
pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다.
## Usage
transformers 의 API 를 사용하여 접근 가능합니다. tokenizer 를 사용할때는 `T5Tokenizer` 가 아니라 `T5TokenizerFast` 를 사용해주십시오. model 은 T5ForConditionalGeneration 를 그대로 활용하시면 됩니다.
### Example
```python
from transformers import T5TokenizerFast, T5ForConditionalGeneration
tokenizer = T5TokenizerFast.from_pretrained('paust/pko-t5-small')
model = T5ForConditionalGeneration.from_pretrained('paust/pko-t5-small')
input_ids = tokenizer(["qa question: 당신의 이름은 무엇인가요?"]).input_ids
labels = tokenizer(["T5 입니다."]).input_ids
outputs = model(input_ids, labels)
print(f"loss={outputs.loss} logits={outputs.logits}")
```
## Klue 평가 (dev)
| | Model | ynat (macro F1) | sts (pearsonr/F1) | nli (acc) | ner (entity-level F1) | re (micro F1) | dp (LAS) | mrc (EM/F1) |
|-----|------------------------------------------------------------------|-----------------|-------------------|-----------|-----------------------|---------------|-----------|-------------|
| | Baseline | **87.30** | **93.20/86.13** | **89.50** | 86.06 | 71.06 | 87.93 | **75.26/-** |
| FT | [pko-t5-small](https://huggingface.co/paust/pko-t5-small) (77M) | 86.21 | 77.99/77.01 | 69.20 | 82.60 | 66.46 | 93.15 | 43.81/46.58 |
| FT | [pko-t5-base](https://huggingface.co/paust/pko-t5-base) (250M) | 87.29 | 90.25/83.43 | 79.73 | 87.80 | 67.23 | 97.28 | 61.53/64.74 |
| FT | [pko-t5-large](https://huggingface.co/paust/pko-t5-large) (800M) | 87.12 | 92.05/85.24 | 84.96 | **88.18** | **75.17** | **97.60** | 68.01/71.44 |
| MT | pko-t5-small | 84.54 | 68.50/72/02 | 51.16 | 74.69 | 66.11 | 80.40 | 43.60/46.28 |
| MT | pko-t5-base | 86.89 | 83.96/80.30 | 72.03 | 85.27 | 66.59 | 95.05 | 61.11/63.94 |
| MT | pko-t5-large | 87.57 | 91.93/86.29 | 83.63 | 87.41 | 71.34 | 96.99 | 70.70/73.72 |
- FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝
- [Baseline](https://arxiv.org/abs/2105.09680): KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수
## License
[PAUST](https://paust.io)에서 만든 pko-t5는 [MIT license](https://github.com/paust-team/pko-t5/blob/main/LICENSE) 하에 공개되어 있습니다.
|
momo/distilbert-base-uncased-finetuned-ner
|
momo
| 2022-09-14T04:35:14Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
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
metrics:
- name: Precision
type: precision
value: 0.9262123053131559
- name: Recall
type: recall
value: 0.9380243875153821
- name: F1
type: f1
value: 0.9320809248554913
- name: Accuracy
type: accuracy
value: 0.9839547555880344
---
<!-- 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: 0.0617
- Precision: 0.9262
- Recall: 0.9380
- F1: 0.9321
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2465 | 1.0 | 878 | 0.0727 | 0.9175 | 0.9199 | 0.9187 | 0.9808 |
| 0.0527 | 2.0 | 1756 | 0.0610 | 0.9245 | 0.9361 | 0.9303 | 0.9834 |
| 0.0313 | 3.0 | 2634 | 0.0617 | 0.9262 | 0.9380 | 0.9321 | 0.9840 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dennislibre/westworld
|
dennislibre
| 2022-09-14T03:55:30Z | 0 | 0 |
fastai
|
[
"fastai",
"image-classification",
"region:us"
] |
image-classification
| 2022-09-14T03:38:10Z |
---
tags:
- fastai
- image-classification
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
sd-concepts-library/uma-style-classic
|
sd-concepts-library
| 2022-09-14T02:53:06Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T02:53:02Z |
---
license: mit
---
### uma-style-classic on Stable Diffusion
This is the `<uma>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Padomin/t5-base-TEDxJP-3front-1body-3rear
|
Padomin
| 2022-09-14T01:46:01Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-13T15:58:46Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-3front-1body-3rear
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. -->
# t5-base-TEDxJP-3front-1body-3rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4427
- Wer: 0.1709
- Mer: 0.1651
- Wil: 0.2519
- Wip: 0.7481
- Hits: 55802
- Substitutions: 6391
- Deletions: 2394
- Insertions: 2252
- Cer: 0.1337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.628 | 1.0 | 1457 | 0.4785 | 0.2008 | 0.1912 | 0.2803 | 0.7197 | 54855 | 6650 | 3082 | 3234 | 0.1735 |
| 0.5271 | 2.0 | 2914 | 0.4292 | 0.1779 | 0.1718 | 0.2602 | 0.7398 | 55387 | 6527 | 2673 | 2293 | 0.1469 |
| 0.4724 | 3.0 | 4371 | 0.4222 | 0.1719 | 0.1664 | 0.2530 | 0.7470 | 55610 | 6365 | 2612 | 2123 | 0.1353 |
| 0.4125 | 4.0 | 5828 | 0.4174 | 0.1707 | 0.1653 | 0.2512 | 0.7488 | 55694 | 6304 | 2589 | 2135 | 0.1342 |
| 0.3646 | 5.0 | 7285 | 0.4218 | 0.1712 | 0.1655 | 0.2521 | 0.7479 | 55756 | 6373 | 2458 | 2224 | 0.1339 |
| 0.3232 | 6.0 | 8742 | 0.4253 | 0.1695 | 0.1642 | 0.2505 | 0.7495 | 55726 | 6340 | 2521 | 2087 | 0.1333 |
| 0.3583 | 7.0 | 10199 | 0.4303 | 0.1699 | 0.1645 | 0.2514 | 0.7486 | 55733 | 6393 | 2461 | 2120 | 0.1338 |
| 0.2894 | 8.0 | 11656 | 0.4355 | 0.1699 | 0.1643 | 0.2508 | 0.7492 | 55827 | 6371 | 2389 | 2215 | 0.1325 |
| 0.2825 | 9.0 | 13113 | 0.4399 | 0.1705 | 0.1648 | 0.2518 | 0.7482 | 55785 | 6409 | 2393 | 2207 | 0.1334 |
| 0.2901 | 10.0 | 14570 | 0.4427 | 0.1709 | 0.1651 | 0.2519 | 0.7481 | 55802 | 6391 | 2394 | 2252 | 0.1337 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/colossus
|
sd-concepts-library
| 2022-09-14T01:13:25Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T01:13:14Z |
---
license: mit
---
### colossus on Stable Diffusion
This is the `<colossus>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/ie-gravestone
|
sd-concepts-library
| 2022-09-14T00:47:38Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T00:47:31Z |
---
license: mit
---
### ie gravestone on Stable Diffusion
This is the `<internet-explorer-gravestone>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
BigSalmon/Infill04
|
BigSalmon
| 2022-09-14T00:46:06Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-14T00:38:55Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill04")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill04")
```
```
Try it out here:
https://huggingface.co/spaces/BigSalmon/TestAnyGPTModel
```
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
Infill / Infilling / Masking / Phrase Masking
```
His contention [blank] by the evidence [sep] was refuted [answer]
***
Few sights are as [blank] New York City as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
When rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
The library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
```
```
original: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the [MASK] star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently [MASK] the big screen in Garden State, which he also directed. Farrell is pencilled in to [MASK] of Crockett in a film version of 1980s police [MASK] Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
infill: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the show. The film star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently been seen on the big screen in Garden State, which he also directed. Farrell is pencilled in to play the role of Crockett in a film version of 1980s police drama Miami Vice. Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
```
|
Padomin/t5-base-TEDxJP-1front-1body-1rear
|
Padomin
| 2022-09-14T00:26:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-13T16:16:53Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-1front-1body-1rear
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. -->
# t5-base-TEDxJP-1front-1body-1rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4600
- Wer: 0.1742
- Mer: 0.1683
- Wil: 0.2562
- Wip: 0.7438
- Hits: 55625
- Substitutions: 6495
- Deletions: 2467
- Insertions: 2291
- Cer: 0.1364
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6478 | 1.0 | 1457 | 0.4880 | 0.2256 | 0.2100 | 0.2999 | 0.7001 | 54825 | 6842 | 2920 | 4808 | 0.2019 |
| 0.542 | 2.0 | 2914 | 0.4461 | 0.1886 | 0.1807 | 0.2697 | 0.7303 | 55225 | 6615 | 2747 | 2817 | 0.1577 |
| 0.4873 | 3.0 | 4371 | 0.4390 | 0.1764 | 0.1702 | 0.2584 | 0.7416 | 55541 | 6519 | 2527 | 2344 | 0.1392 |
| 0.4271 | 4.0 | 5828 | 0.4361 | 0.1750 | 0.1691 | 0.2567 | 0.7433 | 55512 | 6453 | 2622 | 2226 | 0.1381 |
| 0.3705 | 5.0 | 7285 | 0.4366 | 0.1741 | 0.1684 | 0.2558 | 0.7442 | 55508 | 6427 | 2652 | 2164 | 0.1358 |
| 0.3557 | 6.0 | 8742 | 0.4424 | 0.1738 | 0.1679 | 0.2555 | 0.7445 | 55600 | 6453 | 2534 | 2235 | 0.1369 |
| 0.3838 | 7.0 | 10199 | 0.4471 | 0.1741 | 0.1684 | 0.2562 | 0.7438 | 55550 | 6473 | 2564 | 2210 | 0.1362 |
| 0.3095 | 8.0 | 11656 | 0.4517 | 0.1746 | 0.1685 | 0.2566 | 0.7434 | 55618 | 6499 | 2470 | 2305 | 0.1367 |
| 0.306 | 9.0 | 13113 | 0.4573 | 0.1748 | 0.1688 | 0.2570 | 0.7430 | 55601 | 6517 | 2469 | 2304 | 0.1369 |
| 0.3073 | 10.0 | 14570 | 0.4600 | 0.1742 | 0.1683 | 0.2562 | 0.7438 | 55625 | 6495 | 2467 | 2291 | 0.1364 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/uma
|
sd-concepts-library
| 2022-09-14T00:08:35Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-14T00:08:27Z |
---
license: mit
---
### uma on Stable Diffusion
This is the `<uma>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
mfreihaut/distilbert-base-uncased-finetuned-cola
|
mfreihaut
| 2022-09-13T23:58:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T18:13:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5616581968995631
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7956
- Matthews Correlation: 0.5617
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5235 | 1.0 | 535 | 0.5394 | 0.4192 |
| 0.3498 | 2.0 | 1070 | 0.4989 | 0.5065 |
| 0.2343 | 3.0 | 1605 | 0.5506 | 0.5518 |
| 0.1744 | 4.0 | 2140 | 0.7471 | 0.5354 |
| 0.1243 | 5.0 | 2675 | 0.7956 | 0.5617 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.12.1
|
sd-concepts-library/tubby-cats
|
sd-concepts-library
| 2022-09-13T23:26:36Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T23:26:30Z |
---
license: mit
---
### Tubby Cats on Stable Diffusion
This is the `<tubby>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
huggingtweets/taylorswift13
|
huggingtweets
| 2022-09-13T23:18:45Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/taylorswift13/1663111120837/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1564101520043479043/eJpWqka2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Taylor Swift</div>
<div style="text-align: center; font-size: 14px;">@taylorswift13</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Taylor Swift.
| Data | Taylor Swift |
| --- | --- |
| Tweets downloaded | 721 |
| Retweets | 89 |
| Short tweets | 88 |
| Tweets kept | 544 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/155f8g1q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @taylorswift13's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mywgndz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mywgndz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/taylorswift13')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/tylerthecreator
|
huggingtweets
| 2022-09-13T23:17:27Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tylerthecreator/1663110868565/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1405605347755139074/kKJuRJS0_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">T</div>
<div style="text-align: center; font-size: 14px;">@tylerthecreator</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from T.
| Data | T |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 612 |
| Short tweets | 635 |
| Tweets kept | 1984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gsjklloe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tylerthecreator's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zwt7m6t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zwt7m6t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/tylerthecreator')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/metallica
|
huggingtweets
| 2022-09-13T23:15:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:15:06Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1407334956716769288/HFgpsbmW_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Metallica</div>
<div style="text-align: center; font-size: 14px;">@metallica</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Metallica.
| Data | Metallica |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 390 |
| Short tweets | 185 |
| Tweets kept | 2675 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n6wz64s/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @metallica's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ea9ctpp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ea9ctpp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/metallica')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/mariahcarey
|
huggingtweets
| 2022-09-13T23:15:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:13:30Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mariahcarey/1663110896270/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1486066100248981508/AwBY6X2x_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mariah Carey</div>
<div style="text-align: center; font-size: 14px;">@mariahcarey</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Mariah Carey.
| Data | Mariah Carey |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 697 |
| Short tweets | 388 |
| Tweets kept | 2140 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1euvplmf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mariahcarey's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lc0u7bu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lc0u7bu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mariahcarey')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/1gunnagunna-iamcardib-pnbrock
|
huggingtweets
| 2022-09-13T23:12:13Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:12:04Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1478425372438011912/GQujYoYi_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1565550091334828032/flg5WPOb_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1541590121102905345/jxbNo0z0_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">WUNNA & PnBRock & Cardi B</div>
<div style="text-align: center; font-size: 14px;">@1gunnagunna-iamcardib-pnbrock</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from WUNNA & PnBRock & Cardi B.
| Data | WUNNA | PnBRock | Cardi B |
| --- | --- | --- | --- |
| Tweets downloaded | 2827 | 3104 | 3073 |
| Retweets | 2216 | 1190 | 1500 |
| Short tweets | 125 | 310 | 348 |
| Tweets kept | 486 | 1604 | 1225 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cayvnkn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @1gunnagunna-iamcardib-pnbrock's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/od188nqh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/od188nqh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/1gunnagunna-iamcardib-pnbrock')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/piercetheveil
|
huggingtweets
| 2022-09-13T23:09:47Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:08:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/piercetheveil/1663110582992/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1565188375157493760/pzWyT_Xh_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Pierce The Veil</div>
<div style="text-align: center; font-size: 14px;">@piercetheveil</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Pierce The Veil.
| Data | Pierce The Veil |
| --- | --- |
| Tweets downloaded | 3095 |
| Retweets | 456 |
| Short tweets | 206 |
| Tweets kept | 2433 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rbpf3rpq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @piercetheveil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rksak7da) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rksak7da/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/piercetheveil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/zodiac_mf
|
huggingtweets
| 2022-09-13T23:09:43Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:08:10Z |
---
language: en
thumbnail: http://www.huggingtweets.com/zodiac_mf/1663110577337/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1135964801992200192/QZ4fMALY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ZODIAC MOTHERFUCKER</div>
<div style="text-align: center; font-size: 14px;">@zodiac_mf</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ZODIAC MOTHERFUCKER.
| Data | ZODIAC MOTHERFUCKER |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 374 |
| Short tweets | 387 |
| Tweets kept | 2485 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1czfq5eo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zodiac_mf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n0oxy8f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n0oxy8f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zodiac_mf')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/c9mang0
|
huggingtweets
| 2022-09-13T23:07:19Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:05:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/c9mang0/1663110434885/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1360714045599358978/oh2wRcYm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🐐 🐐 🐐 🐐 🐐 🐐 🐐</div>
<div style="text-align: center; font-size: 14px;">@c9mang0</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🐐 🐐 🐐 🐐 🐐 🐐 🐐.
| Data | 🐐 🐐 🐐 🐐 🐐 🐐 🐐 |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 248 |
| Short tweets | 547 |
| Tweets kept | 2428 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pk7njzw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @c9mang0's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9ttvld6j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9ttvld6j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/c9mang0')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/lingua_ignota_
|
huggingtweets
| 2022-09-13T23:06:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-13T23:05:25Z |
---
language: en
thumbnail: http://www.huggingtweets.com/lingua_ignota_/1663110365756/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1425094858558017550/-xYab3Fe_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">LINGUA IGNOTA</div>
<div style="text-align: center; font-size: 14px;">@lingua_ignota_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from LINGUA IGNOTA.
| Data | LINGUA IGNOTA |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 554 |
| Short tweets | 640 |
| Tweets kept | 2042 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bcjlrml/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lingua_ignota_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pjyx8m2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pjyx8m2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lingua_ignota_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/gretathunberg
|
huggingtweets
| 2022-09-13T23:01:27Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-31T20:58:14Z |
---
language: en
thumbnail: http://www.huggingtweets.com/gretathunberg/1663110082774/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1459213153301053442/rL5hhpAI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Greta Thunberg</div>
<div style="text-align: center; font-size: 14px;">@gretathunberg</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Greta Thunberg.
| Data | Greta Thunberg |
| --- | --- |
| Tweets downloaded | 3300 |
| Retweets | 2457 |
| Short tweets | 28 |
| Tweets kept | 815 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g6d8tpo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gretathunberg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iv3jq06) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iv3jq06/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gretathunberg')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
espnet/fsc_challenge_slu_2pass_transformer_gt
|
espnet
| 2022-09-13T21:48:07Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc_challenge",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-09-13T21:40:42Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc_challenge
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/fsc_challenge_slu_2pass_transformer_gt`
This model was trained by Siddhant using fsc_challenge recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 3b54bfe52a294cdfce668c20d777bfa65f413745
pip install -e .
cd egs2/fsc_challenge/slu1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/fsc_challenge_slu_2pass_transformer_gt
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Mar 13 20:59:06 EDT 2022`
- python version: `3.8.11 (default, Aug 3 2021, 15:09:35) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a3`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `97b9dad4dbca71702cb7928a126ec45d96414a3f`
- Commit date: `Mon Sep 13 22:55:04 2021 +0900`
## asr_train_asr_hubert_transformer_adam_specaug_deliberation_transformer_3_raw_en_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/spk_test|3349|17937|99.9|0.1|0.0|0.0|0.1|0.6|
|inference_asr_model_valid.acc.ave_5best/utt_test|4204|22540|89.8|6.6|3.6|0.0|10.2|27.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/spk_test|3349|152191|100.0|0.0|0.0|0.0|0.1|0.6|
|inference_asr_model_valid.acc.ave_5best/utt_test|4204|191435|94.5|2.8|2.7|0.5|6.0|27.6|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_hubert_transformer_adam_specaug_deliberation_transformer_3.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_hubert_transformer_adam_specaug_deliberation_transformer_3_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 25
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- ../../fsc_challenge/asr1/exp/asr_train_asr_hubert_transformer_adam_specaug_old_raw_en_word/valid.acc.ave_5best.pth:encoder:encoder
ignore_init_mismatch: false
freeze_param:
- encoder
- postdecoder.model
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
- exp/asr_stats_raw_en_word/train/transcript_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
- exp/asr_stats_raw_en_word/valid/transcript_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
- - dump/raw/train/transcript
- transcript
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
- - dump/raw/valid/transcript
- transcript
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- the
- turn
- lights
- in
- up
- 'on'
- down
- temperature
- heat
- switch
- kitchen
- volume
- 'off'
- increase_volume_none
- bedroom
- washroom
- decrease_volume_none
- language
- bathroom
- decrease
- my
- to
- increase
- decrease_heat_washroom
- increase_heat_washroom
- music
- heating
- bring
- increase_heat_none
- too
- decrease_heat_none
- me
- change_language_none_none
- activate_lights_washroom
- set
- activate_lights_kitchen
- activate_music_none
- lamp
- deactivate_music_none
- increase_heat_bedroom
- i
- increase_heat_kitchen
- sound
- get
- decrease_heat_kitchen
- loud
- activate_lights_bedroom
- deactivate_lights_bedroom
- decrease_heat_bedroom
- need
- deactivate_lights_kitchen
- bring_newspaper_none
- newspaper
- bring_shoes_none
- shoes
- bring_socks_none
- socks
- activate_lights_none
- deactivate_lights_none
- louder
- go
- deactivate_lights_washroom
- change_language_Chinese_none
- chinese
- could
- you
- bring_juice_none
- juice
- deactivate_lamp_none
- make
- activate_lamp_none
- it
- stop
- play
- change
- quiet
- change_language_Korean_none
- korean
- some
- practice
- change_language_German_none
- german
- ok
- now
- main
- change_language_English_none
- english
- its
- hear
- pause
- this
- thats
- lower
- far
- audio
- please
- fetch
- phones
- a
- different
- start
- resume
- softer
- couldnt
- anything
- quieter
- put
- video
- is
- low
- max
- phone
- mute
- reduce
- use
- languages
- allow
- device
- system
- <sos/eos>
transcript_token_list:
- <blank>
- <unk>
- the
- turn
- lights
- in
- up
- 'on'
- down
- temperature
- heat
- switch
- kitchen
- volume
- 'off'
- bedroom
- washroom
- language
- bathroom
- decrease
- my
- to
- increase
- music
- heating
- bring
- too
- me
- set
- lamp
- i
- sound
- get
- loud
- need
- newspaper
- shoes
- socks
- louder
- go
- chinese
- could
- you
- juice
- make
- it
- stop
- play
- change
- quiet
- korean
- some
- practice
- german
- ok
- now
- main
- english
- its
- hear
- pause
- this
- thats
- lower
- far
- audio
- please
- fetch
- phones
- a
- different
- start
- resume
- softer
- couldnt
- anything
- quieter
- put
- video
- is
- low
- max
- phone
- mute
- reduce
- use
- languages
- allow
- device
- system
- <sos/eos>
two_pass: false
pre_postencoder_norm: false
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
transcript_token_list:
- <blank>
- <unk>
- the
- turn
- lights
- in
- up
- 'on'
- down
- temperature
- heat
- switch
- kitchen
- volume
- 'off'
- bedroom
- washroom
- language
- bathroom
- decrease
- my
- to
- increase
- music
- heating
- bring
- too
- me
- set
- lamp
- i
- sound
- get
- loud
- need
- newspaper
- shoes
- socks
- louder
- go
- chinese
- could
- you
- juice
- make
- it
- stop
- play
- change
- quiet
- korean
- some
- practice
- german
- ok
- now
- main
- english
- its
- hear
- pause
- this
- thats
- lower
- far
- audio
- please
- fetch
- phones
- a
- different
- start
- resume
- softer
- couldnt
- anything
- quieter
- put
- video
- is
- low
- max
- phone
- mute
- reduce
- use
- languages
- allow
- device
- system
- <sos/eos>
ctc_weight: 0.5
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
two_pass: false
pre_postencoder_norm: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
postencoder: null
postencoder_conf: {}
deliberationencoder: transformer
deliberationencoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 4
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: linear
normalize_before: true
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
decoder2: rnn
decoder2_conf: {}
postdecoder: hugging_face_transformers
postdecoder_conf:
model_name_or_path: bert-base-cased
output_size: 256
required:
- output_dir
- token_list
version: 0.10.3a3
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/fsc_challenge_slu_2pass_conformer
|
espnet
| 2022-09-13T21:36:30Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc_challenge",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-09-13T21:35:39Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc_challenge
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/fsc_challenge_slu_2pass_conformer`
This model was trained by Siddhant using fsc_challenge recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 3b54bfe52a294cdfce668c20d777bfa65f413745
pip install -e .
cd egs2/fsc_challenge/slu1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/fsc_challenge_slu_2pass_conformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Jan 19 10:05:40 EST 2022`
- python version: `3.8.11 (default, Aug 3 2021, 15:09:35) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a3`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `97b9dad4dbca71702cb7928a126ec45d96414a3f`
- Commit date: `Mon Sep 13 22:55:04 2021 +0900`
## asr_train_asr_hubert_transformer_adam_specaug_deliberation_raw_en_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/spk_test|3349|17937|97.1|1.2|1.7|0.4|3.3|7.1|
|inference_asr_model_valid.acc.ave_5best/utt_test|4204|22540|80.5|12.4|7.0|3.0|22.5|55.2|
|inference_asr_model_valid.acc.ave_5best/valid|2597|13782|97.5|1.0|1.6|0.2|2.7|5.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/spk_test|3349|152191|98.4|0.5|1.1|0.2|1.8|7.1|
|inference_asr_model_valid.acc.ave_5best/utt_test|4204|191435|89.3|5.4|5.3|2.7|13.4|55.2|
|inference_asr_model_valid.acc.ave_5best/valid|2597|117282|98.6|0.4|1.0|0.2|1.6|5.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_hubert_transformer_adam_specaug_deliberation.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_hubert_transformer_adam_specaug_deliberation_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 80
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- ../../fsc_challenge/asr1/exp/asr_train_asr_hubert_transformer_adam_specaug_raw_en_word/valid.acc.ave_5best.pth:encoder:encoder
ignore_init_mismatch: false
freeze_param:
- encoder
- postdecoder.model
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
- exp/asr_stats_raw_en_word/train/transcript_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
- exp/asr_stats_raw_en_word/valid/transcript_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
- - dump/raw/train/transcript
- transcript
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
- - dump/raw/valid/transcript
- transcript
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- the
- turn
- lights
- in
- up
- 'on'
- down
- temperature
- heat
- switch
- kitchen
- volume
- 'off'
- increase_volume_none
- bedroom
- washroom
- decrease_volume_none
- language
- bathroom
- decrease
- my
- to
- increase
- decrease_heat_washroom
- increase_heat_washroom
- music
- heating
- bring
- increase_heat_none
- too
- decrease_heat_none
- me
- change_language_none_none
- activate_lights_washroom
- set
- activate_lights_kitchen
- activate_music_none
- lamp
- deactivate_music_none
- increase_heat_bedroom
- i
- increase_heat_kitchen
- sound
- get
- decrease_heat_kitchen
- loud
- activate_lights_bedroom
- deactivate_lights_bedroom
- decrease_heat_bedroom
- need
- deactivate_lights_kitchen
- bring_newspaper_none
- newspaper
- bring_shoes_none
- shoes
- bring_socks_none
- socks
- activate_lights_none
- deactivate_lights_none
- louder
- go
- deactivate_lights_washroom
- change_language_Chinese_none
- chinese
- could
- you
- bring_juice_none
- juice
- deactivate_lamp_none
- make
- activate_lamp_none
- it
- stop
- play
- change
- quiet
- change_language_Korean_none
- korean
- some
- practice
- change_language_German_none
- german
- ok
- now
- main
- change_language_English_none
- english
- its
- hear
- pause
- this
- thats
- lower
- far
- audio
- please
- fetch
- phones
- a
- different
- start
- resume
- softer
- couldnt
- anything
- quieter
- put
- video
- is
- low
- max
- phone
- mute
- reduce
- use
- languages
- allow
- device
- system
- <sos/eos>
transcript_token_list:
- <blank>
- <unk>
- the
- turn
- in
- lights
- up
- down
- 'on'
- temperature
- heat
- kitchen
- switch
- 'off'
- volume
- bedroom
- language
- to
- washroom
- bathroom
- my
- increase
- decrease
- music
- heating
- bring
- me
- too
- i
- set
- sound
- get
- and
- lamp
- loud
- a
- need
- you
- shoes
- socks
- newspaper
- it
- light
- louder
- chinese
- could
- go
- make
- juice
- some
- main
- stop
- change
- quiet
- practice
- english
- german
- now
- play
- korean
- okay
- it's
- room
- that's
- of
- hear
- is
- different
- wash
- this
- lower
- fetch
- audio
- please
- far
- pause
- start
- use
- two
- phone
- low
- softer
- put
- bed
- video
- couldn't
- languages
- max
- quieter
- allow
- system
- mute
- phone's
- reduce
- resume
- anything
- device
- washer
- ten
- that
- which
- paper
- phones
- they
- so
- him
- news
- um
- from
- he
- out
- crease
- your
- ah
- better
- but
- late
- value
- k
- washing
- turned
- don't
- then
- o
- whites
- hating
- any
- said
- buy
- lies
- by
- watch
- give
- one
- done
- them
- hit
- place
- jews
- mites
- send
- for
- thing
- night
- not
- languish
- after
- was
- decreased
- lots
- pass
- dream
- bad
- life
- do
- search
- case
- increased
- corian
- we
- swish
- valium
- lamb
- or
- right
- row
- germans
- their
- 'no'
- soft
- time
- like
- own
- swiss
- can
- song
- nights
- lines
- eating
- bottom
- us
- are
- churn
- hate
- load
- top
- em
- take
- what's
- ok
- chain
- lates
- here
- with
- quite
- lice
- at
- lap
- am
- heap
- lampon
- temper
- tough
- hitting
- zone
- john
- three
- may
- vine
- bath
- keep
- into
- burn
- light's
- map
- meat
- eat
- sat
- new
- day
- white
- r
- what
- sheet
- line
- town
- through
- watchroom
- sign
- brain
- tan
- south
- kitten
- sit
- such
- should
- back
- rights
- shows
- knees
- choose
- meet
- likes
- sarks
- say
- frame
- boss
- land
- bar
- bites
- plays
- might
- our
- washerm
- bit
- she
- kit
- his
- tip
- she's
- fire
- free
- find
- bringing
- mics
- priest
- they're
- heats
- run
- shears
- fish
- stone
- tone
- there
- picture
- sent
- t
- bitum
- sweet
- sun
- ring
- love
- i'll
- pin
- vi
- try
- have
- bather
- be
- temp
- zoom
- all
- earn
- bethroom
- sucks
- fit
- as
- lives
- break
- sick
- arm
- thee
- miss
- basroom
- viam
- turns
- found
- dumb
- ladder
- trend
- vime
- viome
- if
- bathroomites
- tom
- during
- bat
- they'll
- wish
- way
- four
- about
- cheese
- mean
- nice
- ocean
- page
- turning
- still
- souths
- thy
- stand
- ding
- while
- who
- white's
- step
- switchy
- made
- mason
- see
- ashroom
- vice
- switched
- la
- ton
- washering
- favor
- via
- i'm
- betum
- create
- you're
- played
- green
- blank
- bassroom
- rim
- nap
- oh
- lay
- grease
- ice
- ball
- kitchens
- x
- nine
- her
- bridge
- pies
- pepper
- attempture
- rain
- wastrom
- peace
- b
- push
- bound
- lawyer
- these
- let
- fast
- star
- drone
- sauce
- practise
- veteran
- paul's
- fall
- temporary
- chop
- key
- bow
- daylights
- loader
- water
- twitch
- chains
- lion
- getting
- pars
- code
- fly
- makes
- offs
- lie
- jim
- tear
- sacks
- fine
- print
- five
- tap
- ye
- mind
- foot
- kiss
- fat
- writes
- will
- switcher
- de
- baton
- man
- yeah
- piece
- vol
- saw
- come
- m
- when
- talk
- round
- niece
- musing
- open
- throw
- bathing
- viim
- know
- head
- teacher
- 'true'
- term
- lord
- orshroom
- trace
- feature
- sock
- seas
- sweets
- layup
- slang
- stream
- drum
- d
- plan
- chun
- sell
- left
- leg
- settling
- men
- bathter
- 'false'
- stay
- chapter
- jane
- son
- rates
- q
- christie
- awesome
- tongue
- chaff
- there's
- outer
- clouder
- past
- red
- chair
- lab
- shares
- enough
- fund
- license
- semi-phones
- bones
- sown
- ups
- needs
- can't
- kitchenites
- drop
- let's
- french
- tempter
- source
- beth
- trying
- volumes
- slides
- atherm
- app
- battle
- drawing
- 'yes'
- trees
- twenty
- pleas
- large
- worship
- oz
- deep
- bathrooms
- loudest
- trim
- playing
- saudi
- volley
- its
- bite
- wall
- heater
- bond
- sand
- catch
- loss
- tender
- long
- dam
- heen
- might's
- fits
- sue
- eight
- pray
- sail
- sounds
- ain't
- lang
- spatroom
- part
- pitch
- matron
- jump
- wrong
- kills
- why
- band
- name
- ply
- piper
- thrown
- roll
- would
- self
- same
- js
- wash-rame
- bed-room
- lot's
- swits
- favour
- meter
- assume
- math
- heed
- stitch
- dawn
- gorian
- radio
- thumb
- pressure
- store
- dot
- meal
- seat
- welsh
- chin
- swift
- bing
- waste
- vassum
- making
- bend
- boil
- plain
- decrees
- square
- tomb
- finger
- temperatures
- speak
- pep
- bait
- stepper
- corey
- allium
- cloud
- state
- dan
- stole
- christy
- fight
- marshroom
- plate
- shown
- big
- bathrobe
- torn
- debate
- chande
- heaving
- switz
- batter
- wash-room
- trap
- ram
- jarman
- friend's
- tipture
- lampos
- flouter
- brand
- bass
- brit
- dome
- decree
- foam
- link
- sax
- seeking
- gain
- values
- beat
- watching
- dark
- creates
- midroom
- months
- camp
- forium
- compared
- race
- shines
- tempre
- pair
- weight
- read
- decays
- lump
- worse
- bus
- truth
- mice
- door
- sub
- draw
- cruci
- tar
- tend
- batting
- ms
- means
- buying
- late's
- legs
- therm
- mike
- linguish
- blaze
- mine
- peren
- walden
- arc
- cruise
- walling
- lakes
- passer
- russian
- rock
- funds
- curry
- patch
- per
- wait
- how
- days
- mamp
- paz
- just
- bringches
- must
- nor
- sing
- u
- blower
- clay
- heated
- resound
- fights
- wamp
- hearing
- basserm
- brewing
- produce
- sorry
- wound
- had
- food
- other
- quick
- an
- face
- flights
- fun
- downs
- wine
- vion
- wampoff
- lake
- lane
- last
- soften
- i'd
- check
- lowered
- heam
- ink
- twist
- think
- die
- crack
- degree
- washington
- friend
- doors
- number
- gave
- button
- tears
- lash
- many
- raised
- show
- action
- age
- engage
- says
- join
- under
- till
- save
- follow
- brings
- shot
- worst
- trainees
- night's
- bear
- rome
- l
- s
- roam
- da
- pasta
- spring
- languished
- tier
- bone
- pete
- next
- leads
- dick
- rate
- dig
- good
- six
- fought
- mesic
- beto
- fitch
- chimpashire
- carrying
- depritus
- laws
- youth
- ethroom
- baffer
- rule
- wing
- langus
- loudy
- re
- jeers
- pull
- lessons
- lighter
- took
- jew
- layout
- fats
- wit
- nace
- d's
- temple
- drawn
- person
- wastrong
- codio
- crank
- chunny
- inglace
- increases
- raleum
- z
- drones
- slide
- wet
- saucer
- embracer
- c
- bracher
- bathrum
- template
- musics
- radios
- glyta
- inglease
- sad
- shawn
- deeps
- nerve
- sobs
- blame
- best
- wandstrom
- rostrum
- sogs
- sayings
- itchen
- churnies
- ocana
- fom's
- witch
- clamp
- liked
- manguish
- lusher
- saying
- folium
- foium
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- <blank>
- <unk>
- the
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- thrown
- roll
- would
- self
- same
- js
- wash-rame
- bed-room
- lot's
- swits
- favour
- meter
- assume
- math
- heed
- stitch
- dawn
- gorian
- radio
- thumb
- pressure
- store
- dot
- meal
- seat
- welsh
- chin
- swift
- bing
- waste
- vassum
- making
- bend
- boil
- plain
- decrees
- square
- tomb
- finger
- temperatures
- speak
- pep
- bait
- stepper
- corey
- allium
- cloud
- state
- dan
- stole
- christy
- fight
- marshroom
- plate
- shown
- big
- bathrobe
- torn
- debate
- chande
- heaving
- switz
- batter
- wash-room
- trap
- ram
- jarman
- friend's
- tipture
- lampos
- flouter
- brand
- bass
- brit
- dome
- decree
- foam
- link
- sax
- seeking
- gain
- values
- beat
- watching
- dark
- creates
- midroom
- months
- camp
- forium
- compared
- race
- shines
- tempre
- pair
- weight
- read
- decays
- lump
- worse
- bus
- truth
- mice
- door
- sub
- draw
- cruci
- tar
- tend
- batting
- ms
- means
- buying
- late's
- legs
- therm
- mike
- linguish
- blaze
- mine
- peren
- walden
- arc
- cruise
- walling
- lakes
- passer
- russian
- rock
- funds
- curry
- patch
- per
- wait
- how
- days
- mamp
- paz
- just
- bringches
- must
- nor
- sing
- u
- blower
- clay
- heated
- resound
- fights
- wamp
- hearing
- basserm
- brewing
- produce
- sorry
- wound
- had
- food
- other
- quick
- an
- face
- flights
- fun
- downs
- wine
- vion
- wampoff
- lake
- lane
- last
- soften
- i'd
- check
- lowered
- heam
- ink
- twist
- think
- die
- crack
- degree
- washington
- friend
- doors
- number
- gave
- button
- tears
- lash
- many
- raised
- show
- action
- age
- engage
- says
- join
- under
- till
- save
- follow
- brings
- shot
- worst
- trainees
- night's
- bear
- rome
- l
- s
- roam
- da
- pasta
- spring
- languished
- tier
- bone
- pete
- next
- leads
- dick
- rate
- dig
- good
- six
- fought
- mesic
- beto
- fitch
- chimpashire
- carrying
- depritus
- laws
- youth
- ethroom
- baffer
- rule
- wing
- langus
- loudy
- re
- jeers
- pull
- lessons
- lighter
- took
- jew
- layout
- fats
- wit
- nace
- d's
- temple
- drawn
- person
- wastrong
- codio
- crank
- chunny
- inglace
- increases
- raleum
- z
- drones
- slide
- wet
- saucer
- embracer
- c
- bracher
- bathrum
- template
- musics
- radios
- glyta
- inglease
- sad
- shawn
- deeps
- nerve
- sobs
- blame
- best
- wandstrom
- rostrum
- sogs
- sayings
- itchen
- churnies
- ocana
- fom's
- witch
- clamp
- liked
- manguish
- lusher
- saying
- folium
- foium
- bae
- vendor
- edram
- bendroom
- bisium
- acainos
- walk
- slight
- pauses
- alm
- palima
- temperating
- loaded
- fifty
- banner
- thank
- hearts
- croom
- alima
- sats
- doing
- list
- masats
- mesus
- christa
- feet
- quote
- tandy
- bitroom
- chand
- fitjan
- west
- lampow
- lords
- press
- chili
- bedcom
- fin
- wasolates
- pastor
- breathe
- stout
- font's
- perian
- chich
- chitty
- bought
- indication
- fonts
- gen
- vash
- ashram
- india
- avashroom
- chrome
- home
- aria
- newspad
- steppad
- barthrome
- chant
- software
- chandy
- shui
- chandi
- keitelates
- belt
- trujandelaits
- indika
- chen
- shindy
- shoot
- district
- stum
- codeio
- debesh
- chandle
- digit
- prim
- size
- sodio
- roshroom
- vohima
- suppose
- shiny
- versus
- lowing
- doubt
- bake
- semithones
- waffroom
- crucial
- chippeture
- ray
- decreesadio
- kenchet
- decreesadi
- meta
- mang
- baum
- ate
- washram
- jacries
- premium
- sops
- tempure
- built
- zoo
- pring
- lowers
- opum
- dipture
- tetch
- basper
- peak
- bassorm
- pas
- switchofta
- lover
- flap
- incretes
- bastroom
- tikris
- didn't
- sweater
- bastom
- lets
- lampoff
- bedrim
- jumites
- bassum
- vaim
- badger
- lengths
- promise
- batroom
- dog
- bedrooms
- satio
- involve
- quiner
- samayum
- sulo
- fused
- butte
- decrysovalium
- subtuse
- silo
- tread
- fetchment
- lime
- stuff
- kin
- switzerland
- eaten
- sex
- sublinguisha
- seling
- singhush
- fitchmus
- ox
- seven
- mingle
- shakorean
- foams
- keeps
- setmingwich
- quorian
- swer
- smingh
- someites
- washroomites
- posse
- seeding
- haste
- rosh
- toff
- vein
- seating
- chemi
- sis
- debt
- lamish
- jinnies
- gennies
- linguishu
- tarn
- gym
- eats
- dough
- fedium
- isaac
- bender
- does
- healing
- speed
- adam
- noun
- aldio
- etch
- amp
- sangus
- southern
- vodroom
- sanch
- post
- beddo
- things
- sounded
- tick
- toverty
- view
- weshroom
- comparator
- fordom
- keys
- templar
- worker
- tiki
- odio
- geo
- fordium
- mode
- mindless
- fishermen
- lua
- pierto
- voting
- brad
- europe
- grade
- shrieks
- cures
- stub
- decase
- ufoium
- geophonia
- moved
- villium
- drews
- compare
- vodal
- dumpier
- sorts
- cases
- bosh
- modes
- souls
- rose
- pedro
- mash
- shanja
- druce
- benise
- draws
- minces
- laguish
- throughdome
- sweetzer
- compato
- science
- dumper
- breathing
- yong
- vanquish
- chorean
- bell
- word
- sentence
- strain
- washern
- lava
- basom
- smith
- less
- sons
- sights
- sides
- vying
- allowed
- cough
- drew
- knee
- bastin
- sot
- stopping
- blee
- kisa
- agenda
- va
- outset
- belights
- tingway
- lada
- sus
- germ
- bedroomed
- length
- phoned
- rezone
- lands
- knitting
- submit
- broom
- touch
- hook
- maths
- chaffelites
- kinch
- june
- limp
- namp
- basser
- gitchen
- dreamly
- bash
- upped
- boff
- mines
- zik
- chikurian
- ab
- ut
- hamperture
- pawn
- okena
- kain
- coose
- fon's
- truck
- battery
- washm
- likesov
- cherp
- scripture
- small
- welding
- samphon
- slaying
- baldium
- berumita
- slamp
- helium
- habiture
- saldom
- vatum
- blushham
- apedial
- sums
- flipper
- langworth
- id
- itch
- isherm
- washerms
- odds
- bedium
- heights
- kent
- bethrym
- atham
- hos
- induce
- bathtub
- lamped
- bellium
- iter
- usherm
- stu
- rathroom
- mees
- resumed
- barchy
- betterim
- trello
- flowing
- butcher
- hooker
- mashes
- mu
- site
- riz
- maty
- handing
- hand
- predium
- iton
- kate
- newspip
- lifeton
- printing
- shiz
- hidanu
- bother
- weights
- jigs
- glotter
- templates
- lamper
- heata
- boshroom
- mounter
- tipperger
- washrooms
- imager
- washman
- vayum
- methroom
- lossroom
- waft
- shrum
- meshes
- fringes
- amma
- keching
- redees
- crane
- barthroom
- yim
- lara
- speech
- barther
- tea
- fra
- chorium
- trines
- starks
- firm's
- wallian
- rushing
- lau
- scott
- fotherine
- volleying
- arthur
- hesing
- kna
- path
- meant
- mutes
- y
- young
- knows
- kitchened
- washherm
- washher
- kori
- tipperture
- ballium
- rizo
- though
- topperture
- tapature
- douce
- inblies
- query
- law
- semi
- turnouts
- turneth
- bassom
- flay
- damager
- vassorm
- cree
- vassar
- faster
- julo
- rub
- septa
- lost
- sal
- core
- piney
- desamla
- bat's
- third
- poshroom
- hunt
- kickin
- william
- mackt
- cheer
- wardium
- funny
- wat
- trough
- ecogen
- cannot
- forget
- lighted
- engine
- bind
- edwin
- mitoff
- plensic
- trump
- setling
- semi-divise
- again
- over
- chat
- endogen
- mainling
- increci
- blease
- fishing
- washerb
- downed
- bedder
- mython
- volium
- byton
- mayam
- basium
- bathium
- wilder
- cape
- viion
- fet
- dusk
- betterm
- file
- spatorm
- spedroom
- starts
- nightcloth
- decris
- borrow
- vian
- baimhap
- track
- church
- batherine
- washerroom
- dirt
- nam
- trip
- gin
- brian
- sup
- swisher
- parse
- chainius
- lat
- sly
- duna
- shum
- heal
- rice
- tiny
- only
- tied
- tinder
- board
- later
- gate
- violin
- tonight
- pager
- move
- latin
- warrior
- major
- magnum
- each
- bitter
- none
- tempreja
- michaws
- thirteen
- little
- cease
- bytron
- oita
- neot
- bank
- armage
- hang
- wandhi
- rantage
- went
- orsh
- aloud
- delete
- stroll
- side
- browser
- washa
- ramp
- hinting
- grieve
- marked
- terran
- embrace
- breeze
- lambas
- emasek
- got
- blindness
- stun
- jama
- bitumhida
- he's
- polite
- ned
- drumhim
- langest
- heem
- midenings
- papo
- inglies
- form
- papu
- larries
- clean
- lepicha
- beja
- very
- coria
- deal
- mittron
- damn
- baja
- timey
- aima
- going
- ainting
- don
- adore
- bullock
- pain
- audience
- dotion
- viving
- notion
- bytro
- worry
- talks
- byte
- dance
- vosham
- because
- tree
- ming
- matox
- modern
- invasia
- makeness
- hawks
- natom
- arms
- pay
- hinder
- latum
- bench
- hoofs
- king
- dampentering
- north
- boo
- petations
- donna
- kabbatum
- temprejo
- chairman
- mr
- names
- standard
- batu
- ridge
- using
- couldying
- never
- matter
- ninety
- powder
- interviews
- stir
- changes
- bap
- jomo
- courier
- crop
- void
- safe
- tamies
- were
- network
- dambraja
- once
- glight
- mightum
- royal
- gordian
- viewers
- cajanaton
- stood
- wind
- treat
- lean
- teach
- matam
- tarsh
- incase
- dembation
- clang
- beta
- lyphon
- sang
- dia
- richer
- bedoleitone
- dampeja
- pinosh
- mishik
- oft
- hi
- live
- trumpet
- butch
- blights
- seth
- kids
- herb
- bettern
- pathroom
- shroom
- ifridio
- san
- sroom
- sanna
- christ
- clap
- worshroom
- cruce
- crisadio
- adi
- iam
- quarter
- throne
- heatsy
- lace
- shallize
- pratt
- beast
- slash
- paulster
- brazil
- priested
- trust
- slice
- kind
- jules
- being
- tying
- gets
- jewels
- jills
- arcs
- jajarman
- fesh
- crate
- grown
- brained
- porian
- shills
- grew
- bare
- keechin'
- toss
- reduced
- var
- libraries
- slanguish
- catching
- rawroom
- passa
- laugh
- dip
- vima
- plight
- clider
- tank
- prat
- securion
- cause
- care
- bent
- thyum
- odd
- washed
- twitched
- mangling
- valiumt
- pop
- nicogen
- switching
- federm
- washerrm
- stolen
- sub-language
- heane
- vap
- babin
- karan
- mezek
- towen
- ek
- full
- saver
- kill
- plates
- vien
- eck
- fi
- myer
- muzak
- vampoline
- bytes
- fars
- pratise
- pat
- decreases
- ison
- flight
- blies
- waffron
- debates
- double
- taping
- earth
- greece
- capture
- corium
- plake
- blush
- tech
- than
- fuff
- alter
- strengths
- dial
- betch
- chanda
- bartminate
- fiz
- outalates
- bitumlets
- sister
- rich
- washamlets
- nigusa
- digly
- eighty
- grace
- approach
- cachen
- mats
- embrature
- impression
- donate
- nispepa
- debrature
- mistakes
- misshik
- printer
- damperture
- seam
- degre
- embreter
- stretch
- ready
- ting
- embedded
- indawashroom
- kitar
- greeted
- embrids
- matterm
- mythic
- buff
- fifth
- cachan
- lova
- house
- style
- netson
- wassum
- fister
- wasmite
- version
- fistum
- debator
- soon
- gender
- buttermate
- fisterlets
- bridger
- waitum
- crisset
- embritter
- content
- itself
- korian
- mailing
- dears
- chimp
- lambon
- sarts
- chippers
- chimpature
- lods
- chine
- koreen
- ease
- mat's
- lightsigned
- viin
- vaya
- kinship
- sorrow
- corin
- near
- chimpshire
- annie
- viium
- loier
- wise
- tifeture
- mizek
- fiber
- timbers
- suck
- juiced
- thou
- linguids
- aaron
- air
- apt
- art
- erin
- slowy
- bedger
- relights
- seveji
- edgerm
- wide
- broadroom
- bet
- betel
- bats
- bazo
- venro
- turnatives
- bid
- washerll
- washerable
- attempts
- masaks
- mix
- andy
- rep
- rut
- ani
- sec
- loan
- gurin
- advice
- simus
- encryst
- lagwit
- swisser
- journeys
- kid
- bedma
- temptary
- father's
- files
- liners
- fleets
- paid
- limo
- washerel
- signature
- basil
- baroom
- lip
- lampong
- coming
- mekita
- kitchelettes
- soul
- lizone
- we'll
- eyes
- drink
- detention
- debatero
- every
- timster
- klater
- rent
- dell
- plea
- blow
- riya
- bashroom
- stopped
- junes
- myton
- sart
- tart
- cloth
- reso
- musiclotter
- zet
- loads
- attempter
- washham
- carinian
- decrysty
- further
- turos
- suits
- lampost
- audiovian
- meshirs
- tim
- emmy
- dempature
- look
- cooking
- dating
- shis
- ether
- reason
- volio
- byzal
- decrous
- stating
- bether
- equal
- edron
- curse
- semiphones
- tell
- dom
- lad
- glassor
- enphusiatic
- keton
- flash
- wasn't
- sweeter
- dry
- sunlights
- portion
- rigging
- level
- auto
- whips
- lesson
- killers
- internalized
- substituting
- metro
- tumblena
- holy
- folio
- silence
- worm
- dvo
- immute
- doesn't
- direction
- tiquisidom
- village
- magic
- whether
- fatalouts
- hot
- thoughts
- swipe
- resem
- wife
- shoe
- wiped
- wipes
- kits
- battering
- sweats
- readase
- jesus
- switchs
- g's
- faium
- wits
- al
- scream
- taine
- wids
- sweat
- mease
- sees
- lieout
- salter
- taints
- founds
- traced
- sailed
- bathtering
- nail
- ing
- loose
- hates
- taint
- languids
- watering
- bleed
- vium
- sam
- liout
- professor
- paying
- cogate
- mushrooms
- interpreter
- library
- words
- unrate
- insist
- work
- western
- belenn
- gangs
- bodier
- training
- money
- veil
- patron
- jonatha
- mars
- mainland
- wasuporian
- who's
- starting
- peretry
- filtering
- transformative
- written
- suit
- defeat
- nana
- menasa
- edbraphone
- goes
- der
- reperator
- segmaphone
- nagostuperium
- rating
- competitive
- betty
- hasan
- ventro
- tantam
- davos
- suttape
- loudness
- random
- volimex
- posta
- repellent
- study
- taste
- sample
- boiling
- lysome
- boy
- states
- lars
- factor
- point
- nicer
- sounder
- boliam
- poly
- boom
- bowling
- body
- chakawaliman
- misploder
- borium
- thousand
- stoke
- islam
- table
- basic
- temporator
- quarters
- middle
- easter
- antos
- tools
- painters
- trans
- world
- sense
- dieting
- oaks
- raising
- been
- mashaws
- transfer
- temples
- <sos/eos>
ctc_weight: 0.5
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
two_pass: false
pre_postencoder_norm: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
postencoder: null
postencoder_conf: {}
deliberationencoder: conformer
deliberationencoder_conf:
output_size: 256
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: linear
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
decoder2: rnn
decoder2_conf: {}
postdecoder: hugging_face_transformers
postdecoder_conf:
model_name_or_path: bert-base-cased
output_size: 256
required:
- output_dir
- token_list
version: 0.10.3a3
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
sd-concepts-library/looney-anime
|
sd-concepts-library
| 2022-09-13T21:09:35Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T21:09:29Z |
---
license: mit
---
### looney anime on Stable Diffusion
This is the `<looney-anime>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/thunderdome-covers
|
sd-concepts-library
| 2022-09-13T20:26:37Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T20:26:24Z |
---
license: mit
---
### Thunderdome Covers on Stable Diffusion
This is the `<thunderdome>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/eddie
|
sd-concepts-library
| 2022-09-13T20:14:14Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T20:14:10Z |
---
license: mit
---
### Eddie on Stable Diffusion
This is the `Eddie` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Padomin/t5-base-TEDxJP-0front-1body-1rear
|
Padomin
| 2022-09-13T19:50:21Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-13T12:31:15Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-0front-1body-1rear
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. -->
# t5-base-TEDxJP-0front-1body-1rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Wer: 0.1801
- Mer: 0.1739
- Wil: 0.2635
- Wip: 0.7365
- Hits: 55253
- Substitutions: 6626
- Deletions: 2708
- Insertions: 2296
- Cer: 0.1411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6609 | 1.0 | 1457 | 0.5121 | 0.2181 | 0.2049 | 0.2958 | 0.7042 | 54651 | 6867 | 3069 | 4151 | 0.1880 |
| 0.5633 | 2.0 | 2914 | 0.4719 | 0.1891 | 0.1817 | 0.2714 | 0.7286 | 55015 | 6654 | 2918 | 2644 | 0.1558 |
| 0.5212 | 3.0 | 4371 | 0.4626 | 0.1838 | 0.1771 | 0.2666 | 0.7334 | 55168 | 6635 | 2784 | 2452 | 0.1462 |
| 0.4498 | 4.0 | 5828 | 0.4616 | 0.1807 | 0.1747 | 0.2643 | 0.7357 | 55148 | 6630 | 2809 | 2231 | 0.1420 |
| 0.4058 | 5.0 | 7285 | 0.4633 | 0.1799 | 0.1739 | 0.2631 | 0.7369 | 55200 | 6592 | 2795 | 2231 | 0.1419 |
| 0.3802 | 6.0 | 8742 | 0.4675 | 0.1796 | 0.1733 | 0.2630 | 0.7370 | 55311 | 6636 | 2640 | 2321 | 0.1412 |
| 0.4126 | 7.0 | 10199 | 0.4737 | 0.1781 | 0.1724 | 0.2617 | 0.7383 | 55245 | 6595 | 2747 | 2163 | 0.1394 |
| 0.3436 | 8.0 | 11656 | 0.4772 | 0.1788 | 0.1729 | 0.2624 | 0.7376 | 55247 | 6616 | 2724 | 2208 | 0.1401 |
| 0.3249 | 9.0 | 13113 | 0.4827 | 0.1796 | 0.1735 | 0.2632 | 0.7368 | 55265 | 6635 | 2687 | 2281 | 0.1407 |
| 0.3347 | 10.0 | 14570 | 0.4869 | 0.1801 | 0.1739 | 0.2635 | 0.7365 | 55253 | 6626 | 2708 | 2296 | 0.1411 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
colorfulpen/1
|
colorfulpen
| 2022-09-13T19:38:19Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-09-13T19:38:19Z |
---
license: cc-by-nc-sa-4.0
---
|
erikejw/swin-base-patch4-window7-224-in22k-finetuned-eurosat
|
erikejw
| 2022-09-13T19:22:40Z | 219 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-13T18:27:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9896296296296296
---
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0390
- Accuracy: 0.9896
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1691 | 1.0 | 190 | 0.0693 | 0.9789 |
| 0.1275 | 2.0 | 380 | 0.0419 | 0.9889 |
| 0.1165 | 3.0 | 570 | 0.0390 | 0.9896 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
evegarcianz/bert-finetuned-squad
|
evegarcianz
| 2022-09-13T19:22:38Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-13T07:56:09Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: evegarcianz/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# evegarcianz/bert-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4637
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 33276, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.6630 | 0 |
| 0.4637 | 1 |
### Framework versions
- Transformers 4.21.0
- TensorFlow 2.8.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/poutine-dish
|
sd-concepts-library
| 2022-09-13T18:24:28Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-13T18:24:16Z |
---
license: mit
---
### Poutine Dish on Stable Diffusion
This is the `<poutine-qc>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
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