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text2text-generation | transformers | {} | DeepPavlov/marianmt-tatoeba-enru | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeepPavlov/marianmt-tatoeba-ruen | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | # RoBERTa Large model fine-tuned on Winogrande
This model was fine-tuned on Winogrande dataset (XL size) in sequence classification task format, meaning that original pairs of sentences
with corresponding options filled in were separated, shuffled and classified independently of each other.
## Model description
## Intended use & limitations
### How to use
## Training data
[WinoGrande-XL](https://huggingface.co/datasets/winogrande) reformatted the following way:
1. Each sentence was split on "`_`" placeholder symbol.
2. Each option was concatenated with the second part of the split, thus transforming each example into two text segment pairs.
3. Text segment pairs corresponding to correct and incorrect options were marked with `True` and `False` labels accordingly.
4. Text segment pairs were shuffled thereafter.
For example,
```json
{
"answer": "2",
"option1": "plant",
"option2": "urn",
"sentence": "The plant took up too much room in the urn, because the _ was small."
}
```
becomes
```json
{
"sentence1": "The plant took up too much room in the urn, because the ",
"sentence2": "plant was small.",
"label": false
}
```
and
```json
{
"sentence1": "The plant took up too much room in the urn, because the ",
"sentence2": "urn was small.",
"label": true
}
```
These sentence pairs are then treated as independent examples.
### BibTeX entry and citation info
```bibtex
@article{sakaguchi2019winogrande,
title={WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
author={Sakaguchi, Keisuke and Bras, Ronan Le and Bhagavatula, Chandra and Choi, Yejin},
journal={arXiv preprint arXiv:1907.10641},
year={2019}
}
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | {"language": ["en"], "datasets": ["winogrande"], "widget": [{"text": "The roof of Rachel's home is old and falling apart, while Betty's is new. The home value of </s> Rachel is lower."}, {"text": "The wooden doors at my friends work are worse than the wooden desks at my work, because the </s> desks material is cheaper."}, {"text": "Postal Service were to reduce delivery frequency. </s> The postal service could deliver less frequently."}, {"text": "I put the cake away in the refrigerator. It has a lot of butter in it. </s> The cake has a lot of butter in it."}]} | DeepPavlov/roberta-large-winogrande | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
feature-extraction | transformers |
# rubert-base-cased-conversational
Conversational RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with [RuBERT](../rubert-base-cased).
08.11.2021: upload model with MLM and NSP heads
\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
| {"language": ["ru"]} | DeepPavlov/rubert-base-cased-conversational | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
feature-extraction | transformers |
# rubert-base-cased-sentence
Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\].
\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)
\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
| {"language": ["ru"]} | DeepPavlov/rubert-base-cased-sentence | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
feature-extraction | transformers |
# rubert-base-cased
RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\].
08.11.2021: upload model with MLM and NSP heads
\[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).
| {"language": ["ru"]} | DeepPavlov/rubert-base-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers |
# XLM-RoBERTa-Large-En-Ru-MNLI
xlm-roberta-large-en-ru finetuned on mnli. | {"language": ["en", "ru"], "tags": ["xlm-roberta", "xlm-roberta-large", "xlm-roberta-large-en-ru", "xlm-roberta-large-en-ru-mnli"], "datasets": ["glue", "mnli"], "model_index": [{"name": "mnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}}]}], "widget": [{"text": "\u041b\u044e\u0431\u043b\u044e \u0442\u0435\u0431\u044f. \u041d\u0435\u043d\u0430\u0432\u0438\u0436\u0443 \u0442\u0435\u0431\u044f"}, {"text": "I love you. I hate you"}]} | DeepPavlov/xlm-roberta-large-en-ru-mnli | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
feature-extraction | transformers |
# XLM-RoBERTa-Large-En-Ru
## Model description
This model is a version XLM-RoBERTa with embeddings and vocabulary reduced to most frequent tokens in English and Russian.
| {"language": ["en", "ru"]} | DeepPavlov/xlm-roberta-large-en-ru | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Lithuanian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "lt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\\twith torch.no_grad():
\\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 56.55 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
| {"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Lithuanina by Deividas Mataciunas", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lt", "type": "common_voice", "args": "lt"}, "metrics": [{"type": "wer", "value": 56.55, "name": "Test WER"}]}]}]} | DeividasM/wav2vec2-large-xlsr-53-lithuanian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | transformers | {} | DeltaHub/adapter_t5-3b_cola | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | DeltaHub/adapter_t5-3b_mrpc | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | DeltaHub/adapter_t5-3b_qnli | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | Need to work with OpenDelta
```
from transformers import AutoModelForSeq2SeqLM
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
from opendelta import AutoDeltaModel
delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5)
delta.log()
```
| {} | DeltaHub/lora_t5-base_mrpc | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers |
# Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive)
### Comment l'utiliser ?
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased')
loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert")
nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer)
print(nlp("Je suis plutôt confiant."))
```
```
[{'label': 'OBJECTIVE', 'score': 0.3320835530757904}]
```
## Résultats de l'évaluation du modèle
| Epoch | Validation Loss | Samples Per Second |
|:------:|:--------------:|:------------------:|
| 1 | 2.219246 | 49.476000 |
| 2 | 1.883753 | 47.259000 |
| 3 | 1.747969 | 44.957000 |
| 4 | 1.695606 | 43.872000 |
| 5 | 1.641470 | 45.726000 |
## Citation
Pour toute utilisation de ce modèle, merci d'utiliser cette citation :
> Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>
| {"language": "fr", "tags": ["sentiments", "text-classification", "flaubert", "french", "flaubert-large"]} | DemangeJeremy/4-sentiments-with-flaubert | null | [
"transformers",
"pytorch",
"flaubert",
"text-classification",
"sentiments",
"french",
"flaubert-large",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Denilson/gbert-base-germaner | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | Deniskin/emailer_medium_300 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Deniskin/essays_small_2000 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Deniskin/essays_small_2000i | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | Deniskin/gpt3_medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Asuna Yuuki DialoGPT Model | {"tags": ["conversational"]} | Denny29/DialoGPT-medium-asunayuuki | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Denver/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-fil | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-hi | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-id | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-ja | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-ms | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-my | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-th | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMixFT_en-vi | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMix_en-ja-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMix_en-zh-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | title: ArcaneGAN
emoji: 🚀
colorFrom: blue
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false | {} | Despin89/test | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers |
# Token classification for FOODs.
Detects foods in sentences.
Currently, only supports spanish. Multiple words foods are detected as one entity.
## To-do
- English support.
- Negation support.
- Quantity tags.
- Psychosocial tags. | {"widget": [{"text": "El paciente se alimenta de pan, sopa de calabaza y coca-cola"}]} | Dev-DGT/food-dbert-multiling | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Miku DialogGPT Model | {"tags": ["conversational"]} | Devid/DialoGPT-small-Miku | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | {} | Devmapall/paraphrase-quora | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | The default Prism model available at https://github.com/thompsonb/prism. See the [README.md](https://github.com/thompsonb/prism/blob/master/README.md) file for more information.
**LICENCE NOTICE**
```
MIT License
Copyright (c) Brian Thompson
Portions of this software are copied from fairseq (https://github.com/pytorch/fairseq),
which is released under the MIT License and Copyright (c) Facebook, Inc. and its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
``` | {"license": "mit"} | Devrim/prism-default | null | [
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | DevsIA/Devs_IA | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | Hello | {} | DevsIA/imagenes | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | null |
# Wav2Vec2-Large-XLSR-Welsh
This model has moved to https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy
| {"language": "cy", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-welsh (by Dewi Bryn Jones, fine tuning week - March 2021)", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice cy", "type": "common_voice", "args": "cy"}, "metrics": [{"type": "wer", "value": "25.59%", "name": "Test WER"}]}]}]} | DewiBrynJones/wav2vec2-large-xlsr-welsh | null | [
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"cy",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | DheerajPranav/Dialo-GPT-Rick-bot | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dhito/am | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dhritam/Zova-bot | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dhruva/Interstellar | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dibyaranjan/nl_image_search | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | DicoTiar/wisdomfiy | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2915
- Bleu: 27.9273
- Gen Len: 34.0935
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.7448 | 1.0 | 38145 | 1.2915 | 27.9273 | 34.0935 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metrics": [{"type": "bleu", "value": 27.9273, "name": "Bleu"}]}]}]} | DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | DiegoBalam12/institute_classification | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Digakive/Hsgshs | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Dilmk2/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | {} | DimaOrekhov/cubert-method-name | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | DimaOrekhov/transformer-method-name | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# V DialoGPT Model | {"tags": ["conversational"]} | Dimedrolza/DialoGPT-small-cyberpunk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# HomerBot: A conversational chatbot imitating Homer Simpson
This model is a fine-tuned [DialoGPT](https://huggingface.co/microsoft/DialoGPT-medium) (medium version) on Simpsons [scripts](https://www.kaggle.com/datasets/pierremegret/dialogue-lines-of-the-simpsons).
More specifically, we fine-tune DialoGPT-medium for 3 epochs on 10K **(character utterance, Homer's response)** pairs
For more details, check out our git [repo](https://github.com/jesseDingley/HomerBot) containing all the code.
### How to use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("DingleyMaillotUrgell/homer-bot")
model = AutoModelForCausalLM.from_pretrained("DingleyMaillotUrgell/homer-bot")
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User: ") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature = 0.8
)
# print last outpput tokens from bot
print("Homer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
| {"language": ["en"], "tags": ["conversational"]} | jesseD/homer-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | DivyanshuSheth/T5-Seq2Seq-Final | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dizoid/Lll | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dkwkk/Da | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dkwkk/W | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dmitriiserg/Pxd | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Dmitry12/sber | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Harry Potter DialoGPT Medium Model | {"tags": ["conversational"]} | Doiman/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | DongHai/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+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-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: 0.7335
- Matthews Correlation: 0.5356
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5309 | 1.0 | 535 | 0.5070 | 0.4239 |
| 0.3568 | 2.0 | 1070 | 0.5132 | 0.4913 |
| 0.24 | 3.0 | 1605 | 0.6081 | 0.4990 |
| 0.1781 | 4.0 | 2140 | 0.7335 | 0.5356 |
| 0.1243 | 5.0 | 2675 | 0.8705 | 0.5242 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.535587402888147, "name": "Matthews Correlation"}]}]}]} | DongHyoungLee/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Donghyun/L2_BERT | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers | The Reader model is for Korean Question Answering
The backbone model is deepset/xlm-roberta-large-squad2.
It is a finetuned model with KorQuAD-v1 dataset.
As a result of verification using KorQuAD evaluation dataset, it showed approximately 87% and 92% respectively for the EM score and F1 score.
Thank you | {} | Dongjae/mrc2reader | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | {} | Dongmin/testmodel | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | 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. -->
# Wayne_NLP_mT5
This model was trained only english datasets.
if you want trained korean + english model
go to wayne_mulang_mT5.
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0a0+3fd9dcf
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "Wayne_NLP_mT5", "results": []}]} | Waynehillsdev/Wayne_NLP_mT5 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | 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. -->
# Waynehills-STT-doogie-server
This model is a fine-tuned version of [Doogie/Waynehills-STT-doogie-server](https://huggingface.co/Doogie/Waynehills-STT-doogie-server) 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | Waynehillsdev/Waynehills-STT-doogie-server | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# Waynehills_summary_tensorflow
This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Waynehills_summary_tensorflow", "results": []}]} | Waynehillsdev/Waynehills_summary_tensorflow | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | 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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4180
- Wer: 0.3392
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.656 | 4.0 | 500 | 1.8973 | 1.0130 |
| 0.8647 | 8.0 | 1000 | 0.4667 | 0.4705 |
| 0.2968 | 12.0 | 1500 | 0.4211 | 0.4035 |
| 0.1719 | 16.0 | 2000 | 0.4725 | 0.3739 |
| 0.1272 | 20.0 | 2500 | 0.4586 | 0.3543 |
| 0.1079 | 24.0 | 3000 | 0.4356 | 0.3484 |
| 0.0808 | 28.0 | 3500 | 0.4180 | 0.3392 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Waynehillsdev/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers | {} | Waynehillsdev/waynehills_sentimental_kor | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | Doohae/p_encoder | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | Doohae/q_encoder | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers | Model for Extraction-based MRC
original model : klue/roberta-large
Designed for ODQA Competition | {} | Doohae/roberta | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
#Rick DialoGPT model | {"tags": ["conversational"]} | Doquey/DialoGPT-small-Luisbot1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
#Michael | {"tags": "conversational"} | Doquey/DialoGPT-small-Michaelbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Celestia Ludenburg DiabloGPT Model | {"tags": ["conversational"]} | Doxophobia/DialoGPT-medium-celeste | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers | {} | DoyyingFace/bert-COVID-HATE-finetuned-test | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asonam-clean | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-asonam-unclean | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-concat-clean | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-concat-unclean-discriminate | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-concat-unclean-with-clean-valid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-concat-unclean | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-self-clean-small-discriminate | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-freeze4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-warmup-50 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
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