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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Points4")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points4")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of movie... | {} | BigSalmon/Points4 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T01:57:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
It should also be able to do all that this can: URL
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# training logs
- https://wandb.ai/junyu/huggingface/runs/1jg2jlgt
# install
- https://github.com/JunnYu/FLASHQuad_pytorch
# usage
```python
import torch
from flash import FLASHForMaskedLM
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_small_wwm_clu... | {"license": "mit", "inference": false} | junnyu/flash_small_wwm_cluecorpussmall | null | [
"transformers",
"pytorch",
"flash",
"fill-mask",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-02T01:59:48+00:00 | [] | [] | TAGS
#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us
|
# training logs
- URL
# install
- URL
# usage
| [
"# training logs\r\n- URL",
"# install\r\n- URL",
"# usage"
] | [
"TAGS\n#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us \n",
"# training logs\r\n- URL",
"# install\r\n- URL",
"# usage"
] |
null | null | model and notebook for the Fatima Fellowship 2022 coding Challenge
| {} | TheJarmanitor/fatima-fellowship-model | null | [
"region:us"
] | null | 2022-04-02T02:01:06+00:00 | [] | [] | TAGS
#region-us
| model and notebook for the Fatima Fellowship 2022 coding Challenge
| [] | [
"TAGS\n#region-us \n"
] |
null | null | **Google Colab Notebook link:**
https://colab.research.google.com/drive/1iA8nvb93VLcrDfIt17AOIHnkVdLSNcW_?usp=sharing
This repo contains files for defining and creating a simple convolutional network for
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 deg... | {} | satoshiz01/Flipped_CIFAR10_vision | null | [
"region:us"
] | null | 2022-04-02T02:30:55+00:00 | [] | [] | TAGS
#region-us
| Google Colab Notebook link:
URL
This repo contains files for defining and creating a simple convolutional network for
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees).
The following files are in this repo:
Coding_Challenge_for_Fatima_Fellowship.... | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
# biomedtra-small for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explor... | {"language": "es", "datasets": ["squad_es", "hackathon-pln-es/biomed_squad_es_v2"], "metrics": ["f1"]} | hackathon-pln-es/biomedtra-small-es-squad2-es | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T02:31:31+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #electra #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us
| biomedtra-small for QA
======================
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 Hackathon organized by SOMOS NLP.
Motivation
----------
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores t... | [] | [
"TAGS\n#transformers #pytorch #electra #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us \n"
] |
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-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | nikhil6041/wav2vec2-large-xls-r-300m-hindi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T02:35:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
question-answering | transformers | # roberta-base es for QA
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the [squad_es(v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [deepset/... | {"language": "es", "datasets": ["squad_es"]} | hackathon-pln-es/roberta-base-bne-squad2-es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"es",
"dataset:squad_es",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T02:38:30+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #es #dataset-squad_es #endpoints_compatible #has_space #region-us
| # roberta-base es for QA
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the squad_es(v2) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in deepset/roberta-base-squad2, an english-based model trained for similar purposes
## Performance
Evaluated ... | [
"# roberta-base es for QA \nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the squad_es(v2) training dataset.",
"## Hyperparameters\n\nThe hyperparameters were chosen based on those used in deepset/roberta-base-squad2, an english-based model trained for similar purposes",
"## Performanc... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #es #dataset-squad_es #endpoints_compatible #has_space #region-us \n",
"# roberta-base es for QA \nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the squad_es(v2) training dataset.",
"## Hyperparameters\n\nThe hyper... |
question-answering | transformers |
# roberta-base-biomedical-clinical-es for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus.... | {"language": "es", "datasets": ["squad_es", "hackathon-pln-es/biomed_squad_es_v2"], "metrics": ["f1"]} | hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T02:47:54+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us
| roberta-base-biomedical-clinical-es for QA
==========================================
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 Hackathon organized by SOMOS NLP.
Motivation
----------
Recent research has made available Spanish Language Models trained on Bi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us \n"
] |
text-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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | jingwei001/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T03:36:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6432
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# paper_feedback_intent
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown da... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "paper_feedback_intent", "results": []}]} | mp6kv/paper_feedback_intent | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T03:37:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| paper\_feedback\_intent
=======================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3621
* Accuracy: 0.9302
* Precision: 0.9307
* Recall: 0.9302
* F1: 0.9297
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/780200431859269633/kXZwD... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/percybotshelley | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T04:27:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Romantic Poetry Bot
@percybotshelley
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# finetuned-vit-base-patch16-224-upside-down-detector
This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following ... | {"license": "apache-2.0", "tags": ["accelerator"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg", "example_title": "original"}, {"src": "https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-d... | jaygala24/finetuned-vit-base-patch16-224-upside-down-detector | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"accelerator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T07:42:45+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #accelerator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-vit-base-patch16-224-upside-down-detector
===================================================
This model is a fine-tuned version of vit-base-patch16-224-in21k on the custom image orientation dataset adapted from the beans dataset. It achieves the following results on the evaluation set:
* Accuracy: 0.8947... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-04\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linea... | [
"TAGS\n#transformers #pytorch #vit #image-classification #accelerator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-04\n* train\\_batch\\_size: 32\n* eval\\_batc... |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-dtm-domain-v1-distill
此模型是之前[开源对话匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-dtm-domain-v1)的蒸馏版本（仅4层 BERT），适用于**开放领域的对话匹配**场景（偏口语化），比如：
- 哪有好玩的 VS. 这附近有什么好玩的地方
- 定时25分钟 VS. 计时半个小时
- 我要听王琦的歌 VS. 放一首王琦的歌
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-dtm-domain-v1-distill | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T08:32:32+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-dtm-domain-v1-distill
=============================================
此模型是之前开源对话匹配模型的蒸馏版本（仅4层 BERT），适用于开放领域的对话匹配场景（偏口语化），比如：
* 哪有好玩的 VS. 这附近有什么好玩的地方
* 定时25分钟 VS. 计时半个小时
* 我要听王琦的歌 VS. 放一首王琦的歌
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-general-v1-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-general-v1-distill | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T08:39:32+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-general-v1-distill
==========================================
此模型是之前开源通用语义匹配模型的蒸馏版本（仅4层 BERT），适用于通用语义匹配场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 ... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# T5-define
(This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)
This model is trained to generate word definitions based on the word and a context,
using a subset of wordnet for all words that have an example and definition.
The model uses task prompts on the for... | {"language": "en", "datasets": ["marksverdhei/wordnet-definitions-en-2021"], "widget": [{"text": "define \"toecoin\": toecoin rose by 200% after Elon Musk mentioned it in his tweet"}]} | marksverdhei/t5-base-define | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:marksverdhei/wordnet-definitions-en-2021",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T08:50:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-marksverdhei/wordnet-definitions-en-2021 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5-define
(This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)
This model is trained to generate word definitions based on the word and a context,
using a subset of wordnet for all words that have an example and definition.
The model uses task prompts on the for... | [
"# T5-define \n\n(This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)\n\nThis model is trained to generate word definitions based on the word and a context,\nusing a subset of wordnet for all words that have an example and definition.\nThe model uses task prompts ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-marksverdhei/wordnet-definitions-en-2021 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-define \n\n(This model is still a work in progress. If you use it for fine tuning, mak... |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好且编码速度更快**。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throu... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-general-v2-distill | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T08:58:18+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-general-v2-distill
==========================================
此模型是之前开源通用语义匹配模型的蒸馏版本（仅4层 BERT），适用于通用语义匹配场景，从效果来看该模型在各种任务上泛化能力更好且编码速度更快。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 6% 左右（具体结果详见下... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-qmc-domain-v1
此模型是基于之前开源[问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1)的蒸馏轻量化版本（仅含4层 BERT），适用于**开放领域的问题匹配**场景，比如：
- 洗澡用什么香皂好？vs. 洗澡用什么香皂好
- 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
- 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-qmc-domain-v1-distill | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T09:02:53+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-qmc-domain-v1
=====================================
此模型是基于之前开源问题匹配模型的蒸馏轻量化版本（仅含4层 BERT），适用于开放领域的问题匹配场景，比如：
* 洗澡用什么香皂好？vs. 洗澡用什么香皂好
* 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
* 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-qmc-finance-v1-distill
此模型是之前[开源金融问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-finance-v1)的蒸馏轻量化版本（仅4层 BERT），适用于**金融领域的问题匹配**场景，比如：
- 8千日利息400元? VS 10000元日利息多少钱
- 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
- 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-qmc-finance-v1-distill | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T09:07:48+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-qmc-finance-v1-distill
==============================================
此模型是之前开源金融问题匹配模型的蒸馏轻量化版本（仅4层 BERT），适用于金融领域的问题匹配场景，比如：
* 8千日利息400元? VS 10000元日利息多少钱
* 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
* 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 ... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
null | null | **Upside down detector**: Train a model to detect if images are upside down
* Trained on Google Street View.
* Synthetically turn some of images upside down. Create a training and test set.
* Build a neural network using TensorFlow.
* Train it to classify image orientation until a reasonable accuracy is reached.
* Loo... | {} | OmarAlasqa/RotNet_FatimaFellowship | null | [
"tensorboard",
"region:us"
] | null | 2022-04-02T09:31:43+00:00 | [] | [] | TAGS
#tensorboard #region-us
| Upside down detector: Train a model to detect if images are upside down
* Trained on Google Street View.
* Synthetically turn some of images upside down. Create a training and test set.
* Build a neural network using TensorFlow.
* Train it to classify image orientation until a reasonable accuracy is reached.
* Look at... | [] | [
"TAGS\n#tensorboard #region-us \n"
] |
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. -->
# wav2vec_asr_swbd_10_epochs
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_asr_swbd_10_epochs", "results": []}]} | itaihay/wav2vec_asr_swbd_10_epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T09:53:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_asr\_swbd\_10\_epochs
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-robust-ft-swbd-300h on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Wer: 0.9627
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
text-classification | null |
# fakeBert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a [news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) from Kaggle.
## Model description
Fine-tuning Bert for text classification.
## Training and evalu... | {"license": "mit", "tags": ["text-classification", "PyTorch", "Transformers"]} | asimokby/fakeBert | null | [
"text-classification",
"PyTorch",
"Transformers",
"license:mit",
"region:us"
] | null | 2022-04-02T09:56:45+00:00 | [] | [] | TAGS
#text-classification #PyTorch #Transformers #license-mit #region-us
|
# fakeBert
This model is a fine-tuned version of bert-base-uncased on a news dataset from Kaggle.
## Model description
Fine-tuning Bert for text classification.
## Training and evaluation data
Training & Validation: Fake and real news dataset
Testing: Fake News Detection Challenge KDD 2020
### Tr... | [
"# fakeBert\r\n\r\nThis model is a fine-tuned version of bert-base-uncased on a news dataset from Kaggle.",
"## Model description\r\n\r\nFine-tuning Bert for text classification.",
"## Training and evaluation data\r\n\r\nTraining & Validation: Fake and real news dataset\r\nTesting: Fake News Detection Challenge... | [
"TAGS\n#text-classification #PyTorch #Transformers #license-mit #region-us \n",
"# fakeBert\r\n\r\nThis model is a fine-tuned version of bert-base-uncased on a news dataset from Kaggle.",
"## Model description\r\n\r\nFine-tuning Bert for text classification.",
"## Training and evaluation data\r\n\r\nTraining ... |
text-generation | transformers |
# Doctor Who model | {"tags": ["conversational"]} | mczolly/DialoGPT-small-the-doctor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T10:05:59+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Doctor Who model | [
"# Doctor Who model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Doctor Who model"
] |
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. -->
# JustAdvanceTechonology/medical_notes_mulitilingual
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/goo... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "JustAdvanceTechonology/medical_notes_mulitilingual", "results": []}]} | JustAdvanceTechonology/medical_notes_mulitilingual | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T10:06:15+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| JustAdvanceTechonology/medical\_notes\_mulitilingual
====================================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 8.7536
* Validation Loss: 6.1397
* Epoch: 7
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 1209, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
token-classification | transformers |
# Electra Base Discriminator conll03 English
# Results:
```
***** predict metrics *****
predict_accuracy = 0.9813
predict_f1 = 0.9137
predict_loss = 0.1251
predict_precision = 0.9098
predict_recall = 0.9177
predict_runtime ... | {"language": ["en"], "license": "apache-2.0", "tags": ["token-classification", "pytorch"], "datasets": ["conll2003"], "metrics": ["Accuracy, F1 Score, Precision, Recall"], "model-index": [{"name": "bhadresh-savani/electra-base-discriminator-finetuned-conll03-english", "results": [{"task": {"type": "token-classification... | bhadresh-savani/electra-base-discriminator-finetuned-conll03-english | null | [
"transformers",
"pytorch",
"tf",
"jax",
"electra",
"token-classification",
"en",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T10:22:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #electra #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Electra Base Discriminator conll03 English
# Results:
| [
"# Electra Base Discriminator conll03 English",
"# Results:"
] | [
"TAGS\n#transformers #pytorch #tf #jax #electra #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Electra Base Discriminator conll03 English",
"# Results:"
] |
null | null | # fake_news | {} | ankitkupadhyay/fake_news | null | [
"region:us"
] | null | 2022-04-02T10:33:01+00:00 | [] | [] | TAGS
#region-us
| # fake_news | [
"# fake_news"
] | [
"TAGS\n#region-us \n",
"# fake_news"
] |
null | null |
## Dataset
[NEWS2018 DATASET_04, Task ID: M-EnHi](http://workshop.colips.org/news2018/dataset.html)
## Notebooks
- `xmltodict.ipynb` contains the code to convert the `xml` files to `json` for training
- `training_script.ipynb` contains the code for training and inference. It is a modified version of https://git... | {"license": "apache-2.0"} | anuragshas/en-hi-transliteration | null | [
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-02T10:50:28+00:00 | [] | [] | TAGS
#license-apache-2.0 #has_space #region-us
| Dataset
-------
NEWS2018 DATASET\_04, Task ID: M-EnHi
Notebooks
---------
* 'URL' contains the code to convert the 'xml' files to 'json' for training
* 'training\_script.ipynb' contains the code for training and inference. It is a modified version of URL
Predictions
-----------
'pred\_test.json' contains top-... | [] | [
"TAGS\n#license-apache-2.0 #has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484080880222351360/FtDB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/sanjabh/1648901691950/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/sanjabh | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T11:13:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Lucid Dreams
@sanjabh
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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... | {"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... | Sam4669/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T12:00:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2317
* Accuracy: 0.923
* F1: 0.9232
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
audio-to-audio | asteroid | ## Asteroid model `Awais/Audio_Source_Separation`
Imported from [Zenodo](https://zenodo.org/record/3873572#.X9M69cLjJH4)
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the Libri2Mix d... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri2Mix", "sep_clean"]} | Awais/Audio_Source_Separation | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri2Mix",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-04-02T12:01:03+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us
| ## Asteroid model 'Awais/Audio_Source_Separation'
Imported from Zenodo
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_clean' task of the Libri2Mix dataset.
Training config:
Results :
On Libri2Mix min test set :
License notice:
This wor... | [
"## Asteroid model 'Awais/Audio_Source_Separation'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLic... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'Awais/Audio_Source_Separation'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Ast... |
fill-mask | transformers | # Duck and Cover - Genre Autoencoder
This model is part of the [duck_and_cover](https://github.com/mcschmitz/duck_and_cover) repository. Scope of this repository is to generate album covers based on several conditions like release year, artist & album name, and genre(s) using different types of GANs. The possible list... | {} | mnne/duck-and-cover-genre-encoder | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T12:12:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Duck and Cover - Genre Autoencoder
This model is part of the duck_and_cover repository. Scope of this repository is to generate album covers based on several conditions like release year, artist & album name, and genre(s) using different types of GANs. The possible list of genres that this encoder covers can be foun... | [
"# Duck and Cover - Genre Autoencoder\n\nThis model is part of the duck_and_cover repository. Scope of this repository is to generate album covers based on several conditions like release year, artist & album name, and genre(s) using different types of GANs. The possible list of genres that this encoder covers can ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Duck and Cover - Genre Autoencoder\n\nThis model is part of the duck_and_cover repository. Scope of this repository is to generate album covers based on several conditions like release year, artist & albu... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145
## Validation Metrics
- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.83... | {"language": "en", "tags": "autotrain", "datasets": ["unjustify/autotrain-data-commonsense_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.355285184457145} | unjustify/autotrain-commonsense_1-696121179 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:unjustify/autotrain-data-commonsense_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T12:45:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsense_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145
## Validation Metrics
- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.83... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 696121179\n- CO2 Emissions (in grams): 4.355285184457145",
"## Validation Metrics\n\n- Loss: 0.34467628598213196\n- Accuracy: 0.8544333807491702\n- Precision: 0.9014251781472684\n- Recall: 0.7721261444557477\n- AUC: 0.942276696... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsense_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 696121179\n- CO2 Emissions... |
text-classification | transformers |
## **Sentiment Inferencing model for stock related commments**
#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*
<br />
### Description
This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labeled tags 'Bullish' or 'Beari... | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finance"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | zhayunduo/roberta-base-stocktwits-finetuned | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"finance",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T12:48:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #finance #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Sentiment Inferencing model for stock related commments
-------------------------------------------------------
#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*
### Description
This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, ... | [
"#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*",
"### Description\n\n\nThis model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labeled tags 'Bullish' or 'Bearish'\n\n\ntry something that the individual investors may say ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #finance #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*",
"### Description\n\n\nThis model is fine tuned wi... |
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... | {"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... | Denzil/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T13:14:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2169
* Accuracy: 0.924
* F1: 0.9239
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
image-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. -->
# vit-base-patch16-224-in21k-bantai_vitv1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-in21k-bantai_vitv1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "ima... | AykeeSalazar/vit-base-patch16-224-in21k-bantai_vitv1 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T13:17:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-bantai\_vitv1
========================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3961
* Accuracy: 0.8636
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | shwetha/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T13:51:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.5925
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text2text-generation | transformers |
# Finetuned T5 on the french part of Lang-8 to automatically correct sentences.
Since the Lang-8 dataset contains really short sentences, the model does not generalize well with sentences larger than 10 words.
I'll upload soon the cleaned dataset that I've used for training. | {"language": ["fr"], "tags": ["text2text generation"], "widget": [{"text": "improve grammar: Elle ne peux jamais aller au cin\u00e9ma avec son amis", "example_title": "Grammar correction"}]} | PoloHuggingface/French_grammar_error_corrector | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text generation",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T14:45:49+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2text generation #fr #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Finetuned T5 on the french part of Lang-8 to automatically correct sentences.
Since the Lang-8 dataset contains really short sentences, the model does not generalize well with sentences larger than 10 words.
I'll upload soon the cleaned dataset that I've used for training. | [
"# Finetuned T5 on the french part of Lang-8 to automatically correct sentences. \nSince the Lang-8 dataset contains really short sentences, the model does not generalize well with sentences larger than 10 words.\nI'll upload soon the cleaned dataset that I've used for training."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2text generation #fr #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Finetuned T5 on the french part of Lang-8 to automatically correct sentences. \nSince the Lang-8 dataset contains really short sentences, the ... |
feature-extraction | transformers |
# Data2Vec-Audio-Large
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**:... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-large | null | [
"transformers",
"pytorch",
"data2vec-audio",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T14:59:46+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Data2Vec-Audio-Large
Facebook's Data2Vec
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a token... | [
"# Data2Vec-Audio-Large\n\nFacebook's Data2Vec\n\nThe large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognitio... | [
"TAGS\n#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Audio-Large\n\nFacebook's Data2Vec\n\nThe large model pretrained on 16kHz sampled speech audio. When using the m... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Large-10m
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your sp... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-large-10m | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T15:00:11+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Large-10m
Facebook's Data2Vec
The large model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michae... | [
"# Data2Vec-Audio-Large-10m\n\nFacebook's Data2Vec\n\nThe large model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiat... | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Large-10m\n\nFacebook's Data2Vec\n\nThe large model pretrained and fine-tuned on 10 minutes of Librispeech ... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Large-100h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your sp... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-large-100h | null | [
"transformers",
"pytorch",
"safetensors",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T15:00:42+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Large-100h
Facebook's Data2Vec
The large model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michae... | [
"# Data2Vec-Audio-Large-100h\n\nFacebook's Data2Vec\n\nThe large model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiat... | [
"TAGS\n#transformers #pytorch #safetensors #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Large-100h\n\nFacebook's Data2Vec\n\nThe large model pretrained and fine-tuned on 100 hours of... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Large-960h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your sp... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingf... | facebook/data2vec-audio-large-960h | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T15:01:11+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Data2Vec-Audio-Large-960h
=========================
Facebook's Data2Vec
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, A... | [] | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
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... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | Prinernian/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T16:49:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2208
* Accuracy: 0.924
* F1: 0.9240
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers |
> A more recent version can be found [here](https://huggingface.co/pszemraj/grammar-synthesis-large). Training smaller and/or comparably sized models is a WIP.
# t5-v1_1-base-ft-jflAUG
**GOAL:** a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should h... | {"license": "cc-by-nc-sa-4.0", "tags": ["grammar", "spelling", "punctuation", "error-correction"], "datasets": ["jfleg"], "widget": [{"text": "i can has cheezburger", "example_title": "cheezburger"}, {"text": "There car broke down so their hitching a ride to they're class.", "example_title": "compound-1"}, {"text": "so... | pszemraj/t5-v1_1-base-ft-jflAUG | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"grammar",
"spelling",
"punctuation",
"error-correction",
"dataset:jfleg",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T17:05:54+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #spelling #punctuation #error-correction #dataset-jfleg #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
> A more recent version can be found here. Training smaller and/or comparably sized models is a WIP.
# t5-v1_1-base-ft-jflAUG
GOAL: a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e... | [
"# t5-v1_1-base-ft-jflAUG\n\nGOAL: a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e. it does not change things that do not need to be changed).\n\n- this model _(at least from pre... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #spelling #punctuation #error-correction #dataset-jfleg #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# t5-v1_1-base-ft-jflAUG\n\nGOAL: a more robust and gene... |
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. -->
---
# t5-russian-summarization
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио https:/... | {"tags": ["generated_from_trainer"], "datasets": "UrukHan/wav2vec2-russian", "widget": [{"text": "\u0417\u0430\u043f\u0430\u0434 \u043f\u043e\u0441\u043b\u0435 \u043d\u0430\u0447\u0430\u043b\u0430 \u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u043e\u0439 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u... | UrukHan/t5-russian-summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:UrukHan/wav2vec2-russian",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T17:09:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-UrukHan/wav2vec2-russian #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
---
# t5-russian-summarization
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио URL и его результаты можно закидывать в эту модель. тестил на видео случайном с ютюба
<table border="0">
<tr>
<td><b style="font-size:30px">Input</b></td>
<td><b style="font-size:30px... | [
"# t5-russian-summarization\n---\nмодель для исправление текста из распознаного аудио. моя модлеь для распознования аудио URL и его результаты можно закидывать в эту модель. тестил на видео случайном с ютюба\n\n<table border=\"0\">\n <tr>\n <td><b style=\"font-size:30px\">Input</b></td>\n <td><b style=\"fon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-UrukHan/wav2vec2-russian #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# t5-russian-summarization\n---\nмодель для исправление текста из распознан... |
question-answering | transformers |
# roberta-base-biomedical-es for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This pro... | {"language": "es", "datasets": ["squad_es", "hackathon-pln-es/biomed_squad_es_v2"], "metrics": ["f1"]} | hackathon-pln-es/roberta-base-biomedical-es-squad2-es | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-02T17:25:38+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us
| roberta-base-biomedical-es for QA
=================================
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 Hackathon organized by SOMOS NLP.
Motivation
----------
Recent research has made available Spanish Language Models trained on Biomedical corpus. T... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #es #dataset-squad_es #dataset-hackathon-pln-es/biomed_squad_es_v2 #endpoints_compatible #has_space #region-us \n"
] |
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/di... | {"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", "ar... | vicl/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 | 2022-04-02T17:29:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8697
* Matthews Correlation: 0.5599
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers | This model provides a GPT-2 language model trained with SimCTG on the ROCStories benchmark [(Mostafazadeh et al., 2016)](https://aclanthology.org/N16-1098.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply Sim... | {} | cambridgeltl/simctg_rocstories | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T18:09:14+00:00 | [
"2202.06417"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model provides a GPT-2 language model trained with SimCTG on the ROCStories benchmark (Mostafazadeh et al., 2016) based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustra... | [
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a s... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n... |
fill-mask | transformers | distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| {} | vocab-transformers/distilbert-mlm-250k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T20:07:10+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | distilbert-base-uncased trained for 500K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| {} | vocab-transformers/distilbert-mlm-500k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T20:12:40+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased trained for 500K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | distilbert-base-uncased trained for 750K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| {} | vocab-transformers/distilbert-mlm-750k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T20:15:23+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased trained for 750K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | distilbert-base-uncased trained for 1000K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| {} | vocab-transformers/distilbert-mlm-1000k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T20:16:53+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased trained for 1000K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | distilbert-base-uncased trained for 680K steps (lowest loss on dev dataset) with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| {} | vocab-transformers/distilbert-mlm-best | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T20:18:48+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased trained for 680K steps (lowest loss on dev dataset) with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | vicl/distilbert-base-uncased-finetuned-mrpc | 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 | 2022-04-02T20:45:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mrpc
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4044
* Accuracy: 0.8480
* F1: 0.8942
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-stsb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "distilbert-base-uncased-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "stsb"... | vicl/distilbert-base-uncased-finetuned-stsb | 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 | 2022-04-02T21:08:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-stsb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5644
* Pearson: 0.8666
* Spearmanr: 0.8636
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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. -->
# canine-s-finetuned-cola
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "canine-s-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "... | vicl/canine-s-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T21:29:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-s-finetuned-cola
=======================
This model is a fine-tuned version of google/canine-s on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6653
* Matthews Correlation: 0.0594
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-generation | transformers |
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<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/clortown-elonmusk-stephencurry30/1648940589601/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/clortown-elonmusk-stephencurry30 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T22:02:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & yeosang elf agenda & Stephen Curry
@clortown-elonmusk-stephencurry30
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was de... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
# Spanish to Quechua translator
This model is a finetuned version of the [t5-small](https://huggingface.co/t5-small).
## Model description
t5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the... | {"language": ["es", "qu"], "license": "apache-2.0", "tags": ["quechua", "translation", "spanish"], "metrics": ["bleu", "sacrebleu"], "widget": [{"text": "Dios ama a los hombres"}, {"text": "A pesar de todo, soy feliz"}, {"text": "\u00bfQu\u00e9 har\u00e1n all\u00ed?"}, {"text": "Debes aprender a respetar"}]} | hackathon-pln-es/t5-small-finetuned-spanish-to-quechua | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"quechua",
"translation",
"spanish",
"es",
"qu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T23:10:11+00:00 | [] | [
"es",
"qu"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #quechua #translation #spanish #es #qu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Spanish to Quechua translator
This model is a finetuned version of the t5-small.
## Model description
t5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the test.
## Intended uses & limitati... | [
"# Spanish to Quechua translator\n\nThis model is a finetuned version of the t5-small.",
"## Model description\n\nt5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the test.",
"## Intended... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #quechua #translation #spanish #es #qu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Spanish to Quechua translator\n\nThis model is a finetuned version of the t5-small.",... |
null | null | # -*- coding: utf-8 -*-
'''
Original file is located at
https://colab.research.google.com/drive/1HrNm5UMZr2Zjmze_HKW799p6LAHM8BTa
'''
from google.colab import files
files.upload()
!pip install kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download 'shaunthes... | {"license": "apache-2.0"} | Asayaya/Upside_down_detector | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-04-02T23:55:24+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| # -*- coding: utf-8 -*-
'''
Original file is located at
URL
'''
from URL import files
URL()
!pip install kaggle
!cp URL ~/.kaggle/
!chmod 600 ~/.kaggle/URL
!kaggle datasets download 'shaunthesheep/microsoft-catsvsdogs-dataset'
!unzip microsoft-catsvsdogs-dataset
import tensorflow as tf
fr... | [
"# -*- coding: utf-8 -*-\r\n'''\r\nOriginal file is located at\r\nURL\r\n'''\r\n\r\n\r\n\r\nfrom URL import files\r\nURL()\r\n\r\n!pip install kaggle\r\n\r\n!cp URL ~/.kaggle/\r\n\r\n!chmod 600 ~/.kaggle/URL\r\n\r\n!kaggle datasets download 'shaunthesheep/microsoft-catsvsdogs-dataset'\r\n\r\n!unzip microsoft-catsvs... | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# -*- coding: utf-8 -*-\r\n'''\r\nOriginal file is located at\r\nURL\r\n'''\r\n\r\n\r\n\r\nfrom URL import files\r\nURL()\r\n\r\n!pip install kaggle\r\n\r\n!cp URL ~/.kaggle/\r\n\r\n!chmod 600 ~/.kaggle/URL\r\n\r\n!kaggle datasets download 'shaunthesheep/microsoft-catsv... |
image-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. -->
# violation-classification-bantai_vit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "model-index": [{"name": "violation-classification-bantai_vit", "results": []}]} | AykeeSalazar/violation-classification-bantai_vit | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T02:01:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# violation-classification-bantai_vit
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2362
- eval_accuracy: 0.9478
- eval_runtime: 43.2567
- eval_samples_per_second: 85.42
- eval_steps_per_se... | [
"# violation-classification-bantai_vit\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2362\n- eval_accuracy: 0.9478\n- eval_runtime: 43.2567\n- eval_samples_per_second: 85.42\n- eval_st... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# violation-classification-bantai_vit\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on t... |
text-classification | transformers | Student project that fine-tuned the roberta-base-openai-detector model on the Twibot-20 dataset. | {} | tdrenis/finetuned-bot-detector | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T02:38:27+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Student project that fine-tuned the roberta-base-openai-detector model on the Twibot-20 dataset. | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# Goal
This model can be used to add emoji to an input text.
To accomplish this, we framed the problem as a token-classification problem, predicting the emoji that should follow a certain word/token as an entity.
The accompanying demo, which includes all the pre- and postprocessing needed can be found [here](https:/... | {"language": "nl", "tags": ["token-classification", "sequence-tagger-model"]} | ml6team/xlm-roberta-base-nl-emoji-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"sequence-tagger-model",
"nl",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-03T05:50:02+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #sequence-tagger-model #nl #autotrain_compatible #endpoints_compatible #has_space #region-us
| Goal
====
This model can be used to add emoji to an input text.
To accomplish this, we framed the problem as a token-classification problem, predicting the emoji that should follow a certain word/token as an entity.
The accompanying demo, which includes all the pre- and postprocessing needed can be found here.
... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #sequence-tagger-model #nl #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-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. -->
# distilgpt2-finetuned-restaurant-reviews-clean
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgp... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-restaurant-reviews-clean", "results": []}]} | Zohar/distilgpt2-finetuned-restaurant-reviews-clean | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-03T06:25:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-restaurant-reviews-clean
=============================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5371
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | silencesys/paraphrase-xlm-r-multilingual-v1-fine-tuned-for-latin | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T07:30:56+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clusterin... |
text-classification | transformers | # ALBERT for Math AR
This model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a releva... | {} | AnReu/albert-for-math-ar-base-ft | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T08:32:31+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #albert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # ALBERT for Math AR
This model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a releva... | [
"# ALBERT for Math AR\n\nThis model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a... | [
"TAGS\n#transformers #pytorch #safetensors #albert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALBERT for Math AR\n\nThis model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addit... |
text-classification | transformers | This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news articles. Base on the Kaggle dataset(https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset).
| {} | AnnaBabaie/ms-marco-MiniLM-L-12-v2-news | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T11:55:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news articles. Base on the Kaggle dataset(URL
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | jsunster/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T12:02:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1476
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-generation | transformers | # JonSnow GPT model
| {"tags": ["conversational"]} | crazypegasus/GPT-JonSnow | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-03T12:09:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # JonSnow GPT model
| [
"# JonSnow GPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JonSnow GPT model"
] |
image-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. -->
# violation-classification-bantai-vit-v100ep
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "violation-classification-bantai-vit-v100ep", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "... | AykeeSalazar/violation-classification-bantai-vit-v100ep | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T13:05:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| violation-classification-bantai-vit-v100ep
==========================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2557
* Accuracy: 0.9157
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
null | null | This model uses images of cats to detect if an image of a cat is upside down or not.
<br>
I have used fastai library for this.
<br>
I have collected data on my google drive through colab by using duckduckgo search API
<br>
I used transfer learning by implementing resnet-18 architecture to solve this particular task. | {} | Suhail/Upside_down_detector | null | [
"region:us"
] | null | 2022-04-03T13:23:47+00:00 | [] | [] | TAGS
#region-us
| This model uses images of cats to detect if an image of a cat is upside down or not.
<br>
I have used fastai library for this.
<br>
I have collected data on my google drive through colab by using duckduckgo search API
<br>
I used transfer learning by implementing resnet-18 architecture to solve this particular task. | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# DistilBERT base cased model for Fake News Classification
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
wi... | {"language": "en", "license": "gpl-3.0", "datasets": ["Fake and real news dataset"], "library": "transformers", "other": "distilbert"} | Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-03T13:52:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBERT base cased model for Fake News Classification
========================================================
Model description
-----------------
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a... | [
"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions. It also inherits some of\nthe bias of its teacher model.\n\n\nThis bias will also affect a... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be charact... |
null | null | A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only dark images | {} | johnowhitaker/orbgan_dark | null | [
"pytorch",
"has_space",
"region:us"
] | null | 2022-04-03T13:54:33+00:00 | [] | [] | TAGS
#pytorch #has_space #region-us
| A version of URL trained on only dark images | [] | [
"TAGS\n#pytorch #has_space #region-us \n"
] |
null | null | A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only light images | {} | johnowhitaker/orbgan_light | null | [
"pytorch",
"has_space",
"region:us"
] | null | 2022-04-03T13:58:51+00:00 | [] | [] | TAGS
#pytorch #has_space #region-us
| A version of URL trained on only light images | [] | [
"TAGS\n#pytorch #has_space #region-us \n"
] |
text2text-generation | transformers |
# T5-small-nl24 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The H... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-small-nl24-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"finnish",
"t5x",
"seq2seq",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-04-03T15:37:27+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| T5-small-nl24 for Finnish
=========================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fin... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\n... |
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-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]} | morahil/wav2vec2-large-xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T15:45:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice da... |
image-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. -->
# violation-classification-bantai-vit-v80ep
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "violation-classification-bantai-vit-v80ep", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "i... | AykeeSalazar/violation-classification-bantai-vit-v80ep | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T15:46:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| violation-classification-bantai-vit-v80ep
=========================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1974
* Accuracy: 0.9560
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
image-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. -->
# deit_flyswot
This model was trained from scratch on the image_folder dataset.
It achieves the following results on the evaluatio... | {"tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["f1"], "model-index": [{"name": "deit_flyswot", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": "default"}, "metrics": [{"type": "f1",... | davanstrien/deit_flyswot | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T16:09:20+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us
| deit\_flyswot
=============
This model was trained from scratch on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0755
* F1: 0.9908
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 666\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* ... |
fill-mask | transformers |
# deberta-base-nepali
This model is pre-trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset consisting of over 13 million Nepali text sequences using a masked language modeling (MLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenizatio... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": "Sakonii/nepalitext-language-model-dataset", "mask_token": "<mask>", "widget": [{"text": "\u092e\u093e\u0928\u0935\u093f\u092f \u0917\u0924\u093f\u0935\u093f\u0927\u093f\u0932\u0947 \u092a\u094d\u0930\u093e\u0924\u0943\u0924\u093f\u0915 \u092a\u0930\u09... | Sakonii/deberta-base-nepali | null | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"fill-mask",
"generated_from_trainer",
"dataset:Sakonii/nepalitext-language-model-dataset",
"arxiv:1911.02116",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T16:11:58+00:00 | [
"1911.02116",
"2006.03654"
] | [] | TAGS
#transformers #pytorch #safetensors #deberta #fill-mask #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-base-nepali
===================
This model is pre-trained on nepalitext dataset consisting of over 13 million Nepali text sequences using a masked language modeling (MLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization similar to XLM-ROBERTa and trains DeBERTa for language mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #safetensors #deberta #fill-mask #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters we... |
null | null | # Projected GAN
- https://arxiv.org/abs/2111.01007
- https://github.com/autonomousvision/projected_gan
- weights
- https://s3.eu-central-1.amazonaws.com/avg-projects/projected_gan/models/art_painting.pkl
- https://s3.eu-central-1.amazonaws.com/avg-projects/projected_gan/models/church.pkl
- https://s3.eu-c... | {} | public-data/projected_gan | null | [
"arxiv:2111.01007",
"has_space",
"region:us"
] | null | 2022-04-03T16:22:17+00:00 | [
"2111.01007"
] | [] | TAGS
#arxiv-2111.01007 #has_space #region-us
| # Projected GAN
- URL
- URL
- weights
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
| [
"# Projected GAN\n\n- URL\n- URL\n\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2111.01007 #has_space #region-us \n",
"# Projected GAN\n\n- URL\n- URL\n\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] |
feature-extraction | transformers |
# ERNIE-Gram-zh
## Introduction
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
More detail: https://arxiv.org/abs/2010.12148
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-gram-zh| Chinese |Layer:12, Hidden:768, ... | {"language": "zh"} | nghuyong/ernie-gram-zh | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"zh",
"arxiv:2010.12148",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T16:34:43+00:00 | [
"2010.12148"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #feature-extraction #zh #arxiv-2010.12148 #endpoints_compatible #region-us
| ERNIE-Gram-zh
=============
Introduction
------------
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
More detail: URL
Released Model Info
-------------------
This released Pytorch model is converted from the officially released PaddlePaddle ERNIE m... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #zh #arxiv-2010.12148 #endpoints_compatible #region-us \n"
] |
token-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. -->
# bert-base-german-cased-finetuned-subj_v2_v1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v2_v1", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v2_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T16:49:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v2\_v1
=============================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1587
* Precision: 0.2222
* Recall: 0.0107
* F1: 0.0204
* Accuracy: 0.9511... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers | Model based trained on 30% of the kaggle public data on fake and reals news article. The model achieved an `auc` of 1.0, precision, recall and f1score all at score of 1.0.
* Task;- The predictor classifies news articles into either fake or real news.
* It is a transformer model trained using the `ktrain` library on 3... | {} | ikekobby/fake-real-news-classifier | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T16:57:15+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Model based trained on 30% of the kaggle public data on fake and reals news article. The model achieved an 'auc' of 1.0, precision, recall and f1score all at score of 1.0.
* Task;- The predictor classifies news articles into either fake or real news.
* It is a transformer model trained using the 'ktrain' library on 3... | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Readability ES Paragraphs for three classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "bertin"], "pipeline_tag": "text-classification", "widget": [{"text": "Las L\u00edneas de Nazca son una serie de marcas trazadas en el suelo, cuya anchura oscila entre los 40 y los 110 cent\u00edmetros."}, {"text": "Hace mucho tiempo, en el gran ... | hackathon-pln-es/readability-es-3class-paragraphs | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"spanish",
"bertin",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-03T19:08:20+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Readability ES Paragraphs for three classes
Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classif... | [
"# Readability ES Paragraphs for three classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.",
"## Description and performance\n\nThis version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model perf... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Readability ES Paragraphs for three classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability asse... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln34")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy... | {} | BigSalmon/InformalToFormalLincoln34 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-03T19:17:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 | # Multiagent RL Model for Tic-Tac-Toe
| {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | AlekseyKorshuk/tic-tac-toe | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-04-03T19:18:06+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # Multiagent RL Model for Tic-Tac-Toe
| [
"# Multiagent RL Model for Tic-Tac-Toe"
] | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# Multiagent RL Model for Tic-Tac-Toe"
] |
fill-mask | transformers |
# roberta-base-wechsel-ukrainian
[`roberta-base`](https://huggingface.co/roberta-base) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://aclanthology.org/2022.naacl-main.293/).
... | {"language": "uk", "license": "mit"} | benjamin/roberta-base-wechsel-ukrainian | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T19:39:08+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #roberta #fill-mask #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wechsel-ukrainian
==============================
'roberta-base' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
Evaluation
==========
Evaluation was done on lang-uk's ... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# roberta-large-wechsel-ukrainian
[`roberta-base`](https://huggingface.co/roberta-base) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://aclanthology.org/2022.naacl-main.293/).... | {"language": "uk", "license": "mit"} | benjamin/roberta-large-wechsel-ukrainian | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-03T20:04:31+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-wechsel-ukrainian
===============================
'roberta-base' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
Evaluation
==========
Evaluation was done on lang-uk'... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO
This model is created from the finetuning of the pre-model
for Spanish [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), using PyTorch framework,
and trained with a set of theses of the National Autonomous University of Mexi... | {"license": "apache-2.0", "tags": ["transformers", "text-classification"], "datasets": "unam_tesis", "metrics": "accuracy", "annotations_creators": ["inoid", "MajorIsaiah", "Ximyer", "clavel"], "languages": "es", "widget": [{"text": "Introducci\u00f3n al an\u00e1lisis de riesgos competitivos bajo el enfoque de la funci... | hackathon-pln-es/unam_tesis_BETO_finnetuning | null | [
"transformers",
"pytorch",
"text-classification",
"dataset:unam_tesis",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-03T20:26:04+00:00 | [] | [] | TAGS
#transformers #pytorch #text-classification #dataset-unam_tesis #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Unam\_tesis\_beto\_finnetuning: Unam's thesis classification with BETO
======================================================================
This model is created from the finetuning of the pre-model
for Spanish BETO, using PyTorch framework,
and trained with a set of theses of the National Autonomous University of ... | [] | [
"TAGS\n#transformers #pytorch #text-classification #dataset-unam_tesis #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | keras | # UPSIDE DOWN classifer | {"language": ["Python"], "datasets": ["https://huggingface.co/datasets/cats_vs_dogs"], "metrics": ["Accuracy", "F1-Score", "Precision"]} | nnitiwe/upside_down_clf | null | [
"keras",
"region:us"
] | null | 2022-04-03T22:15:20+00:00 | [] | [
"Python"
] | TAGS
#keras #region-us
| # UPSIDE DOWN classifer | [
"# UPSIDE DOWN classifer"
] | [
"TAGS\n#keras #region-us \n",
"# UPSIDE DOWN classifer"
] |
null | null |
# Fake news classifier
The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below:
### Train a text classification model to detect fake news articles!
* Download the dataset here: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
* Develop an NL... | {"language": ["python3"], "tags": ["NLP, classification"]} | scdong/Fake-news-classifier | null | [
"region:us"
] | null | 2022-04-03T22:22:14+00:00 | [] | [
"python3"
] | TAGS
#region-us
|
# Fake news classifier
The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below:
### Train a text classification model to detect fake news articles!
* Download the dataset here: URL
* Develop an NLP model for classification that uses a pretrained language mod... | [
"# Fake news classifier\n\nThe project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below:",
"### Train a text classification model to detect fake news articles!\n* Download the dataset here: URL\n* Develop an NLP model for classification that uses a pretrained l... | [
"TAGS\n#region-us \n",
"# Fake news classifier\n\nThe project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below:",
"### Train a text classification model to detect fake news articles!\n* Download the dataset here: URL\n* Develop an NLP model for classification... |
text-classification | transformers |
# Readability ES Paragraphs for two classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using p... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "bertin"], "pipeline_tag": "text-classification", "widget": [{"text": "La cueva de Zaratustra en el Pretil de los Consejos. Rimeros de libros hacen escombro y cubren las paredes. Empapelan los cuatro vidrios de una puerta cuatro cromos espeluznan... | hackathon-pln-es/readability-es-paragraphs | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"spanish",
"bertin",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-03T23:18:35+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Readability ES Paragraphs for two classes
Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using paragraph-level granularity when possible. The model performs binary c... | [
"# Readability ES Paragraphs for two classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.",
"## Description and performance\n\nThis version of the model was trained on a mix of datasets, using paragraph-level granularity when possible. The model perfo... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Readability ES Paragraphs for two classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assess... |
null | null |
# Unam_tesis_ROBERTA_GOB_finnetuning: Unam's thesis classification with PlanTL-GOB-ES/roberta-large-bne
This model is created from the finetuning of the pre-model
for RoBERTa large trained with data from the National Library of Spain (BNE) [
PlanTL-GOB-ES] (https://huggingface.co/PlanTL-GOB-ES/roberta-large-bn... | {"license": "apache-2.0"} | hackathon-pln-es/unam_tesis_ROBERTA_GOB_finnetuning | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-04-04T00:57:47+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Unam\_tesis\_ROBERTA\_GOB\_finnetuning: Unam's thesis classification with PlanTL-GOB-ES/roberta-large-bne
=========================================================================================================
This model is created from the finetuning of the pre-model
for RoBERTa large trained with data from the Na... | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
question-answering | transformers | # Generación de respuestas a preguntas AMA para profesiones
El modelo presentando a continuación se ha generado a partir del [dataset de preguntas AMA desde Reddit (ITAMA-DataSet)](https://huggingface.co/datasets/hackathon-pln-es/ITAMA-DataSet). En especial, se pueden realizar preguntas sobre las siguientes profesiones... | {"language": ["es"], "library_name": "transformers", "datasets": ["hackathon-pln-es/ITAMA-DataSet"], "pipeline_tag": "question-answering"} | hackathon-pln-es/itama | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-answering",
"es",
"dataset:hackathon-pln-es/ITAMA-DataSet",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T01:08:27+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-answering #es #dataset-hackathon-pln-es/ITAMA-DataSet #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Generación de respuestas a preguntas AMA para profesiones
=========================================================
El modelo presentando a continuación se ha generado a partir del dataset de preguntas AMA desde Reddit (ITAMA-DataSet). En especial, se pueden realizar preguntas sobre las siguientes profesiones: 'medic... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-answering #es #dataset-hackathon-pln-es/ITAMA-DataSet #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln35")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln35 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T01:08:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | ## Coding Challenge - Deep Learning for NLP (Foong)
### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.
Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv
### Write-up:
The acc... | {} | foongminwong/dl-nlp | null | [
"region:us"
] | null | 2022-04-04T01:54:33+00:00 | [] | [] | TAGS
#region-us
| ## Coding Challenge - Deep Learning for NLP (Foong)
### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.
Dataset: URL
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv
### Write-up:
The accuracy of the model is 0.995.
There are a couple of misclassi... | [
"## Coding Challenge - Deep Learning for NLP (Foong)",
"### Description:\nThis repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.\n\nDataset: URL\nLibraries used: Scikit-learn, NLTK, pandas, numpy, csv",
"### Write-up:\nThe accuracy of the model is 0.995. \n\nThere are a... | [
"TAGS\n#region-us \n",
"## Coding Challenge - Deep Learning for NLP (Foong)",
"### Description:\nThis repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.\n\nDataset: URL\nLibraries used: Scikit-learn, NLTK, pandas, numpy, csv",
"### Write-up:\nThe accuracy of the model ... |
text-classification | transformers | Label mappings
{'LABEL_0':'Biology','LABEL_1':'Physics','LABEL_2':'Chemistry','LABEL_3':'Maths','LABEL_4':'Social Science','LABEL_5':'English'}
Training data distribution
Physics - 7000
Maths - 7000
Biology - 7000
Chemistry - 7000
English - 5254
Social Science - 7000 | {} | Jackett/subject_classifier_extended | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T02:05:43+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Label mappings
{'LABEL_0':'Biology','LABEL_1':'Physics','LABEL_2':'Chemistry','LABEL_3':'Maths','LABEL_4':'Social Science','LABEL_5':'English'}
Training data distribution
Physics - 7000
Maths - 7000
Biology - 7000
Chemistry - 7000
English - 5254
Social Science - 7000 | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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-large-xls-r-300m-en-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-en-colab", "results": []}]} | jaeyeon/wav2vec2-large-xls-r-300m-en-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T03:02:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-en-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1169
* Wer: 0.0597
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_bat... |
fill-mask | transformers |
[`bioformer-8L`](https://huggingface.co/bioformers/bioformer-8L) pretrained on 164,179 COVID-19 abstracts (from [LitCovid website](https://www.ncbi.nlm.nih.gov/research/coronavirus/)) for 100 epochs.
In our evaluation, this pretraining process leads to improved performance on the multi-label COVID-19 topic classific... | {"language": ["en"], "license": "apache-2.0"} | bioformers/bioformer-litcovid | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T03:06:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bioformer-8L' pretrained on 164,179 COVID-19 abstracts (from LitCovid website) for 100 epochs.
In our evaluation, this pretraining process leads to improved performance on the multi-label COVID-19 topic classification task (BioCreative VII track 5). | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | emon1521/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 | 2022-04-04T03:21:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
text-generation | transformers | It works worse than the GPT-2 Large & Medium models I have been training, because I don't have the compute needed to train the entire dataset I have. I had to resort to using bits.
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BPointsLin... | {} | BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-04T04:04:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| It works worse than the GPT-2 Large & Medium models I have been training, because I don't have the compute needed to train the entire dataset I have. I had to resort to using bits.
Points and keywords. Informal to formal. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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... | {"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... | aprilzoo/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T04:24:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2202
* Accuracy: 0.923
* F1: 0.9232
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# codet5-base-buggy-error-description
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "codet5-base-buggy-error-description", "results": []}]} | alexjercan/codet5-base-buggy-error-description | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T05:03:44+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codet5-base-buggy-error-description
This model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# codet5-base-buggy-error-description\n\nThis model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codet5-base-buggy-error-description\n\nThis model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.",... |
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