Search is not available for this dataset
pipeline_tag
stringclasses 48
values | library_name
stringclasses 205
values | text
stringlengths 0
18.3M
| metadata
stringlengths 2
1.07B
| id
stringlengths 5
122
| last_modified
null | tags
listlengths 1
1.84k
| sha
null | created_at
stringlengths 25
25
|
|---|---|---|---|---|---|---|---|---|
feature-extraction | transformers |
# IndoConvBERT Base Model
IndoConvBERT is a ConvBERT model pretrained on Indo4B.
## Pretraining details
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.
The current version of the model is trained on Indo4B and small Twitter dump.
## Acknowledgement
Big thanks to TFRC (TensorFlow Research Cloud) for providing free TPU.
| {"language": "id", "inference": false} | Wikidepia/IndoConvBERT-base | null | [
"transformers",
"pytorch",
"tf",
"convbert",
"feature-extraction",
"id",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | # Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
sentence = "Anak anak melakukan piket kelas agar kebersihan kelas terjaga"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer(text, padding='longest', return_tensors="pt")
outputs = model.generate(
input_ids=encoding["input_ids"], attention_mask=encoding["attention_mask"],
max_length=512,
do_sample=True,
top_k=200,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
```
## Limitations
Sometimes paraphrase contain date which doesnt exists in the original text :/
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s. | {"language": ["id"]} | Wikidepia/IndoT5-base-paraphrase | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | # Indonesian T5 Base
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following [`google/t5-v1_1-base`](https://huggingface.co/google/t5-v1_1-base).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
| {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers |
**NOTE** : This model might be broken :/
# Indonesian T5 Large
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 500K steps following [`google/t5-v1_1-large`](https://huggingface.co/google/t5-v1_1-large).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
| {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | # Indonesian T5 Small
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following [`google/t5-v1_1-small`](https://huggingface.co/google/t5-v1_1-small).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s. | {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | flair |
# SponsorBlock Auto Segment | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"]} | Wikidepia/SB-AutoSegment | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers |
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/albert-bahasa-uncased-squad'
)
nlp = pipeline('question-answering', model="Wikidepia/albert-bahasa-uncased-squad", tokenizer=tokenizer)
QA_input = {
'question': 'Kapan orang Normandia berada di Normandia?',
'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) adalah orang-orang yang pada abad ke-10 dan ke-11 memberikan nama mereka ke Normandia, sebuah wilayah di Prancis. Mereka adalah keturunan dari Norse (\ "Norman \" berasal dari \ "Norseman \") perampok dan perompak dari Denmark, Islandia dan Norwegia yang, di bawah pemimpin mereka Rollo, setuju untuk bersumpah setia kepada Raja Charles III dari Francia Barat. Melalui generasi asimilasi dan pencampuran dengan penduduk asli Franka dan Romawi-Gaul, keturunan mereka secara bertahap akan bergabung dengan budaya Francia Barat yang berbasis di Karoling. Identitas budaya dan etnis orang Normandia yang berbeda awalnya muncul pada paruh pertama abad ke-10, dan terus berkembang selama abad-abad berikutnya.'
}
res = nlp(QA_input)
print(res)
```
| {"language": "id", "inference": false} | Wikidepia/albert-bahasa-uncased-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers |
# IndoBERT-Lite base fine-tuned on Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answering/SQuAD) for **Q&A** downstream task.
## Model in action
Fast usage with **pipelines**:
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/indobert-lite-squad'
)
qa_pipeline = pipeline(
"question-answering",
model="Wikidepia/indobert-lite-squad",
tokenizer=tokenizer
)
qa_pipeline({
'context': "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgenössische Technische Hochschule, ETH) di Zürich pada tahun 1900.",
'question': "Kapan Einstein melepas kewarganegaraan Jerman?"
})
```
# Output:
```json
{
"score":0.9799205660820007,
"start":147,
"end":151,
"answer":"1896"
}
```
README copied from [mrm8488's repository](https://huggingface.co/mrm8488/bert-tiny-finetuned-squadv2)
| {"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgen\u00f6ssische Technische Hochschule, ETH) di Z\u00fcrich pada tahun 1900."}]} | Wikidepia/indobert-lite-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers |
# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answering/squad) for **Q&A** downstream task.
## Model in action
Fast usage with **pipelines**:
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/indobert-lite-squad'
)
qa_pipeline = pipeline(
"question-answering",
model="Wikidepia/indobert-lite-squad",
tokenizer=tokenizer
)
qa_pipeline({
'context': "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgenössische Technische Hochschule, ETH) di Zürich pada tahun 1900.",
'question': "Kapan Einstein melepas kewarganegaraan Jerman?"
})
```
# Output:
```json
{
"score": 0.9169162511825562,
"start": 147,
"end": 151,
"answer": "1896"
}
```
README copied from [mrm8488's repository](https://huggingface.co/mrm8488/bert-tiny-finetuned-squadv2) | {"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgen\u00f6ssische Technische Hochschule, ETH) di Z\u00fcrich pada tahun 1900."}]} | Wikidepia/indobert-lite-squadx | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers | {} | Wikidepia/indonesian-punctuation | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | # NMT Model for English-Indonesian
| {} | Wikidepia/marian-nmt-enid | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | Wikidepia/quartznet-indonesian | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
automatic-speech-recognition | transformers | {} | Wikidepia/w2v2-id-tmp | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
automatic-speech-recognition | transformers |
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of `facebook/wav2vec2-xls-r-300m` on the `mozilla-foundation/common_voice_8_0` and [MagicHub Indonesian Conversational Speech Corpus](https://magichub.com/datasets/indonesian-conversational-speech-corpus/).
| {"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "id", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R-300M - Indonesian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "id"}, "metrics": [{"type": "wer", "value": 5.046, "name": "Test WER"}, {"type": "cer", "value": 1.699, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "id"}, "metrics": [{"type": "wer", "value": 41.31, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "id"}, "metrics": [{"type": "wer", "value": 52.23, "name": "Test WER"}]}]}]} | Wikidepia/wav2vec2-xls-r-300m-indonesian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"id",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | {} | WikinewsSum/bart-large-cnn-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bart-large-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bart-large-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bart-large-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bart-large-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bert2bert-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bert2bert-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/bert2bert-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-with-title-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-with-title-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | WikinewsSum/t5-base-with-title-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Williwaw/prac | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | Wilson2021/bert_cn_finetuning_model01 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | Wilson2021/mymodel1007 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
image-classification | transformers |
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model requires torch 2.0 environment. | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | WinKawaks/vit-small-patch16-224 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
image-classification | transformers |
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model requires torch 2.0 environment. | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | WinKawaks/vit-tiny-patch16-224 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | WindJ/hello_world_model | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Wins/gpt_law | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | {} | Wintermute/Wintermute | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | {} | Wintermute/Wintermute_extended | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers |
# JC DialogGPT Model | {"tags": ["conversational"]} | Wise/DialogGPT-small-JC | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | WithYou/model_test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Wolferella/gpt-neo-2.7B | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | Wonjun/KPTBert | null | [
"transformers",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Woonn/distilbert-base-uncased-finetuned-cola | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Woonn/kcbert-base-finetuned-nsmc | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Woonn/kcbert-base-finetuned-sst2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Woonn/kogpt2-base-v2-finetuned-sst2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers |
<!-- 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: 0.2162
- Accuracy: 0.9225
- F1: 0.9227
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8437 | 1.0 | 250 | 0.3153 | 0.903 | 0.9005 |
| 0.2467 | 2.0 | 500 | 0.2162 | 0.9225 | 0.9227 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cpu
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9225, "name": "Accuracy"}, {"type": "f1", "value": 0.9227046184638882, "name": "F1"}]}]}]} | Worldman/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | WoutN2001/DialoGPT-small-joshua | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WoutN2001/james | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WoutN2001/james2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | # waaaa | {"tags": ["conversational"]} | WoutN2001/james3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | WoutN2001/james6 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Wtfray/Rayssa | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Wuhu0/output1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WurmWillem/DialoGPT-medium-Rick | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WurmWillem/DialoGPT-medium-RickandMorty | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WurmWillem/DialoGPT-medium-RickandMorty2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | null | {"tags": ["conversational"]} | WurmWillem/DialoGPT-medium-RickandMorty3 | null | [
"conversational",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | WurmWillem/Rick | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Wusgnob/t5-small-finetuned-xsum | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Ww0042/testface | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | Wzf/bert_fintuuing | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers |
<!-- 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. -->
# albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
I use title and label to train the classifier
label_0 : Fake news
label_1 : Real news
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0910
- Accuracy: 0.9758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0452 | 1.0 | 1768 | 0.0910 | 0.9758 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-fakenews-discriminator", "results": []}]} | XSY/albert-base-v2-fakenews-discriminator | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers |
<!-- 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. -->
# albert-base-v2-imdb-calssification
label_0: negative
label_1: positive
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1983
- Accuracy: 0.9361
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.26 | 1.0 | 1563 | 0.1983 | 0.9361 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-imdb-calssification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.93612, "name": "Accuracy"}]}]}]} | XSY/albert-base-v2-imdb-calssification | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers |
<!-- 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. -->
# albert-base-v2-scarcasm-discriminator
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2379
- Accuracy: 0.8996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2111 | 1.0 | 2179 | 0.2379 | 0.8996 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-scarcasm-discriminator", "results": []}]} | XSY/albert-base-v2-scarcasm-discriminator | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-scarcasm-discriminator
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1844
- Accuracy: 0.9698
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.144 | 1.0 | 2179 | 0.2522 | 0.9215 |
| 0.116 | 2.0 | 4358 | 0.2105 | 0.9530 |
| 0.0689 | 3.0 | 6537 | 0.2015 | 0.9610 |
| 0.028 | 4.0 | 8716 | 0.1844 | 0.9698 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-scarcasm-discriminator", "results": []}]} | XSY/roberta-scarcasm-discriminator | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | 这个模型是根据这个一步一步完成的，如果想自己微调，请参考https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.6901
---
<!-- 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-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4500
- Rouge1: 28.6901
- Rouge2: 8.0102
- Rougel: 22.6087
- Rougelsum: 22.6105
- Gen Len: 18.824
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6799 | 1.0 | 25506 | 2.4500 | 28.6901 | 8.0102 | 22.6087 | 22.6105 | 18.824 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {} | XSY/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | XYG/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.9549699799866577
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/XYHY/autonlp-123-478412765
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "unk", "tags": "autonlp", "datasets": ["XYHY/autonlp-data-123"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 69.86520391863117} | XYHY/autonlp-123-478412765 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:XYHY/autonlp-data-123",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | Xan/Hhhehe | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Xanlete/DialoGPT-small-harrypotter | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | Xenova/sponsorblock-base-v1.1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | Xenova/sponsorblock-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Xenova/sponsorblock-classifier | null | [
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | Xenova/sponsorblock-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | # Ultron Small | {"tags": ["conversational"]} | Xeouz/Ultron-Small | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | Xia/albert | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | XiangPan/roberta_squad1_2epoch | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Xianshu/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | {} | XiaoqiJiao/2nd_General_TinyBERT_6L_768D | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | {} | XiaoqiJiao/TinyBERT_General_4L_312D | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | {} | XiaoqiJiao/TinyBERT_General_6L_768D | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | A VQGAN-compatible model trained on screenshots of cityscapes from 90s anime. To use, direct vqgan to the model as you would vqgan_imagenet_f16_1024, faceshq, etc. | {} | Xibanya/AestheticCities | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-to-image | null | # Sunset Cities
This is the [Malevich](https://huggingface.co/sberbank-ai/rudalle-Malevich) ruDALL-E model finetuned on anime screenshots of big cities at sunset.
<img style="text-align:center; display:block;" src="https://huggingface.co/Xibanya/sunset_city/resolve/main/citysunset.png" width="256">
### installation
```
pip install rudalle
```
### How to use
Basic implementation to get a list of image data objects.
```python
from translate import Translator
from rudalle import get_rudalle_model, get_tokenizer, get_vae
from rudalle.pipelines import generate_images
model = get_rudalle_model('Malevich', pretrained=True, fp16=True, device='cuda')
model.load_state_dict(torch.load(CHECKPOINT_PATH))
vae = get_vae().to('cuda')
tokenizer = get_tokenizer()
input_text = Translator(to_lang='ru').translate('city at sunset')
images, _ = generate_images(
text=input_text,
tokenizer=tokenizer, dalle=model, vae=vae,
images_num=1,
top_k=2048,
top_p=0.95,
temperature=1.0
)
```
the Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
``` | {"language": ["ru", "en"], "license": "cc-by-sa-4.0", "tags": ["PyTorch", "Transformers"], "pipeline_tag": "text-to-image"} | Xibanya/sunset_city | null | [
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | Xillolxlbln/alkx | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Xillolxlbln/wav2vec2-large-xls-r-300m-nl-colab_khaled | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Xonlly/test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers |
# Harry | {"tags": ["conversational"]} | XuguangAi/DialoGPT-small-Harry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers |
# Leslie | {"tags": ["conversational"]} | XuguangAi/DialoGPT-small-Leslie | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers |
# Rick | {"tags": ["conversational"]} | XuguangAi/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers |
# Toxic language detection
## Model description
A toxic language detection model trained on tweets. The base model is Roberta-large. For more information,
including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='Xuhui/ToxDect-roberta-large', return_all_scores=True)
prediction = classifier("You are f**king stupid!", )
print(prediction)
"""
Output:
[[{'label': 'LABEL_0', 'score': 0.002632011892274022}, {'label': 'LABEL_1', 'score': 0.9973680377006531}]]
"""
```
## Training procedure
The random seed for this model is 22. For other details, please refer to the Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
### BibTeX entry and citation info
```bibtex
@inproceedings{zhou-etal-2020-debiasing,
title = {Challenges in Automated Debiasing for Toxic Language Detection},
author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah A.},
booktitle = {EACL},
abbr = {EACL},
html = {https://www.aclweb.org/anthology/2021.eacl-main.274.pdf},
code = {https://github.com/XuhuiZhou/Toxic_Debias},
year = {2021},
bibtex_show = {true},
selected = {true}
}
``` | {"language": [], "tags": [], "datasets": [], "metrics": []} | Xuhui/ToxDect-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2102.00086",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | XxProKillerxX/Meh | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | YESO/yeso | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | {} | YNJI/ELECTRA | null | [
"transformers",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | YSKartal/berturk-social-5m | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | YYF/bert-finetuning-test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | YYF/test_hello | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | # 经典昆曲欣赏 期末作业
## KunquChat
Author: 1900012921 俞跃江
| {} | YYJ/KunquChat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | YacShin/LocationAddressV1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | Yagahu/Bahi | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.