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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
**General Information**
This is a `bert-base-cased`, binary classification model, fine-tuned to classify a given sentence as containing advertising content or not. It leverages previous-sentence context to make more accurate predictions.
The model is used in the paper 'Leveraging multimodal content for podcast summar... | {"language": "en", "tags": ["bert", "classification", "pytorch"], "datasets": ["spotify-podcast-dataset"], "pipeline": ["text-classification"], "widget": [{"text": "__START__ [SEP] This is the first podcast on natural language processing applied to spoken language."}, {"text": "This is the first podcast on natural lang... | morenolq/spotify-podcast-advertising-classification | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"classification",
"en",
"dataset:spotify-podcast-dataset",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-05T13:36:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #classification #en #dataset-spotify-podcast-dataset #autotrain_compatible #endpoints_compatible #has_space #region-us
|
General Information
This is a 'bert-base-cased', binary classification model, fine-tuned to classify a given sentence as containing advertising content or not. It leverages previous-sentence context to make more accurate predictions.
The model is used in the paper 'Leveraging multimodal content for podcast summarizat... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #classification #en #dataset-spotify-podcast-dataset #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | keras | **Dataset:** MNIST
**Accuracy:** 0.986%
**Model Structure:**
| {"license": "mit"} | hasnainnaeem/mnist_model | null | [
"keras",
"license:mit",
"region:us"
] | null | 2022-04-05T13:41:48+00:00 | [] | [] | TAGS
#keras #license-mit #region-us
| Dataset: MNIST
Accuracy: 0.986%
Model Structure:
!Model Summary
| [] | [
"TAGS\n#keras #license-mit #region-us \n"
] |
null | null | ### Dataset
* UpDown dataset is created using the CIFAR10 dataset
### Model Information
* Finetune the 'google/vit-base-patch16-224-in21k' pretrained model for 1 epoch
### Performance Measurement
* Binary Cross-Entropy loss is used to measure the training loss
* Accuracy is used to measure the overall model p... | {"license": "apache-2.0"} | mahendra/cifar-up-down-image-classification | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T14:01:23+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| ### Dataset
* UpDown dataset is created using the CIFAR10 dataset
### Model Information
* Finetune the 'google/vit-base-patch16-224-in21k' pretrained model for 1 epoch
### Performance Measurement
* Binary Cross-Entropy loss is used to measure the training loss
* Accuracy is used to measure the overall model p... | [
"### Dataset\r\n* UpDown dataset is created using the CIFAR10 dataset",
"### Model Information\r\n* Finetune the 'google/vit-base-patch16-224-in21k' pretrained model for 1 epoch",
"### Performance Measurement\r\n* Binary Cross-Entropy loss is used to measure the training loss\r\n* Accuracy is used to measure th... | [
"TAGS\n#license-apache-2.0 #region-us \n",
"### Dataset\r\n* UpDown dataset is created using the CIFAR10 dataset",
"### Model Information\r\n* Finetune the 'google/vit-base-patch16-224-in21k' pretrained model for 1 epoch",
"### Performance Measurement\r\n* Binary Cross-Entropy loss is used to measure the trai... |
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | Harsit/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T14:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy")
```
```
wordy: classical music is becoming less popular more and m... | {} | BigSalmon/InformalToFormalLincolnConciseWordy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T14:17:33+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"
] |
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. -->
# scibert_scivocab_uncased_epoch20-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "scibert_scivocab_uncased_epoch20-finetuned-ner", "results": []}]} | HenryHXR/scibert_scivocab_uncased_epoch20-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T14:44:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# scibert_scivocab_uncased_epoch20-finetuned-ner
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# scibert_scivocab_uncased_epoch20-finetuned-ner\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# scibert_scivocab_uncased_epoch20-finetuned-ner\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset.",
"## Model description\n... |
feature-extraction | transformers |
## RegNetY 10B
This gigantic model is a scale up [RegNetY](https://arxiv.org/abs/2003.13678) model trained on one billion uncurated Instagram images.
Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & ... | {"license": "apache-2.0", "tags": ["vision", "seer"]} | facebook/regnet-y-10b-seer | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"feature-extraction",
"vision",
"seer",
"arxiv:2003.13678",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T14:47:49+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #feature-extraction #vision #seer #arxiv-2003.13678 #license-apache-2.0 #endpoints_compatible #region-us
|
## RegNetY 10B
This gigantic model is a scale up RegNetY model trained on one billion uncurated Instagram images.
Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the raw mod... | [
"## RegNetY 10B\n\nThis gigantic model is a scale up RegNetY model trained on one billion uncurated Instagram images.\n\nDisclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Intended uses & limitations\n\nYou can u... | [
"TAGS\n#transformers #pytorch #tf #regnet #feature-extraction #vision #seer #arxiv-2003.13678 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## RegNetY 10B\n\nThis gigantic model is a scale up RegNetY model trained on one billion uncurated Instagram images.\n\nDisclaimer: The team releasing RegNetMode... |
null | pytorch |
# Anime Faces Generator (StyleGAN3 by NVIDIA)
<img width="679" alt="Generated Faces" src="https://user-images.githubusercontent.com/35907066/161809457-e6467724-5942-4a89-b379-85ddfd6ac86c.png">
This is a [StyleGAN3 PyTorch](https://github.com/NVlabs/stylegan3) model trained on this [Anime Face Dataset](https://githu... | {"license": "mit", "library_name": "pytorch", "tags": ["image-generation", "gan", "stylegan", "stylegan3", "nvidia"]} | akiyamasho/stylegan3-anime-faces-generator | null | [
"pytorch",
"image-generation",
"gan",
"stylegan",
"stylegan3",
"nvidia",
"license:mit",
"region:us"
] | null | 2022-04-05T15:19:08+00:00 | [] | [] | TAGS
#pytorch #image-generation #gan #stylegan #stylegan3 #nvidia #license-mit #region-us
|
# Anime Faces Generator (StyleGAN3 by NVIDIA)
<img width="679" alt="Generated Faces" src="URL
This is a StyleGAN3 PyTorch model trained on this Anime Face Dataset.
### Usage
Demo on Spaces is not yet implemented.
You can run the model pickle file locally using the instructions in this generator-script-only subset... | [
"# Anime Faces Generator (StyleGAN3 by NVIDIA)\n\n<img width=\"679\" alt=\"Generated Faces\" src=\"URL\n\nThis is a StyleGAN3 PyTorch model trained on this Anime Face Dataset.",
"### Usage\n\nDemo on Spaces is not yet implemented.\n\nYou can run the model pickle file locally using the instructions in this generat... | [
"TAGS\n#pytorch #image-generation #gan #stylegan #stylegan3 #nvidia #license-mit #region-us \n",
"# Anime Faces Generator (StyleGAN3 by NVIDIA)\n\n<img width=\"679\" alt=\"Generated Faces\" src=\"URL\n\nThis is a StyleGAN3 PyTorch model trained on this Anime Face Dataset.",
"### Usage\n\nDemo on Spaces is not y... |
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. -->
# ls-timit-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following results on... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ls-timit-100percent-supervised-aug", "results": []}]} | Kuray107/ls-timit-100percent-supervised-aug | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T15:33:16+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ls-timit-100percent-supervised-aug
==================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0519
* Wer: 0.0292
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 32\n* eval\\_batch\\_size: 8\n* se... |
image-classification | transformers |
## RegNetY 10B
This gigantic model is a scale up [RegNetY](https://arxiv.org/abs/2003.13678) model trained on one bilion random images ad later finetuned on imagenet.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Inte... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_... | facebook/regnet-y-10b-seer-in1k | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T15:49:38+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RegNetY 10B
This gigantic model is a scale up RegNetY model trained on one bilion random images ad later finetuned on imagenet.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use... | [
"## RegNetY 10B\n\nThis gigantic model is a scale up RegNetY model trained on one bilion random images ad later finetuned on imagenet.\n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RegNetY 10B\n\nThis gigantic model is a scale up RegNetY model trained on one bilion random images ad later finetuned on... |
text-generation | transformers |
# My Little Pony DialoGPT Model | {"tags": ["conversational"]} | trev/DialoGPT-small-MLP | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T15:59:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Little Pony DialoGPT Model | [
"# My Little Pony DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Little Pony DialoGPT Model"
] |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | novarac23/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T17:00:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1374
* F1: 0.8627
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | miesnerjacob/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T17:34:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8559\n- Bleu: 52.9456",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
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... | Shadman-Rohan/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 | 2022-04-05T18:15:34+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: 0.2083
* Accuracy: 0.9245
* F1: 0.9248
Model description
-----------------
Mo... | [
"### 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... |
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/1442847071829204995/C-gq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/benk14894427/1649186779847/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/benk14894427 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T18:25:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Benk
@benk14894427
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-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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | vladimir-lomonosov/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T19:05:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1153
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### 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 #gpt2 #text-generation #generated_from_trainer #license-mit #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\\_batc... |
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/1453748100594642948/BAAS... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vivchen_/1649189613639/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vivchen_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T19:12:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Vivian
@vivchen\_
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"
] |
feature-extraction | transformers | # Dense Encoder - Distilbert - Frozen Token Embeddings
This model is a distilbert-base-uncased model trained for 30 epochs (235k steps), 64 batch size with MarginMSE Loss on MS MARCO dataset.
The token embeddings were frozen.
| Dataset | Model with updated token embeddings | Model with frozen embeddings |
| --- | :-... | {} | vocab-transformers/dense_encoder-distilbert-frozen_emb | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T19:57:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us
| Dense Encoder - Distilbert - Frozen Token Embeddings
====================================================
This model is a distilbert-base-uncased model trained for 30 epochs (235k steps), 64 batch size with MarginMSE Loss on MS MARCO dataset.
The token embeddings were frozen.
| [] | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1LtVm-VHvDnfNy7SsbZAqhh49ikBwh1un?usp=sharing)
| {"tags": ["conversational"]} | RAJESHNEMANI/Chatbot_AI | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T19:57:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # RickBot built for Chai
Make your own here
| [
"# RickBot built for Chai\nMake your own here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RickBot built for Chai\nMake your own here"
] |
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/1125539522983399425/1iUP... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jorgegos/1649193376372/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jorgegos | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T20:11:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jorge Gosalvez
@jorgegos
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"
] |
null | null | This model was trained using the 'bert-base-uncased' from the transformer library and it was trained on the popular fake/real news dataset from Kaggle.
Pytorch is the framework used to train the model and it had an accuracy score of 93.5 % and here is what the classification report looks like.
precision... | {} | hemhemoh/FatimaFellowship_NLPtask | null | [
"region:us"
] | null | 2022-04-05T20:35:08+00:00 | [] | [] | TAGS
#region-us
| This model was trained using the 'bert-base-uncased' from the transformer library and it was trained on the popular fake/real news dataset from Kaggle.
Pytorch is the framework used to train the model and it had an accuracy score of 93.5 % and here is what the classification report looks like.
precision... | [] | [
"TAGS\n#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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | harish3110/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T21:16:34+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1354
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #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: 5e-05\n... |
null | transformers | # Orientation Classifier
---
language:
- en
tags:
- image-classification
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
- f1
---
| {} | imessam/OrientationClassifier | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T22:35:25+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| # Orientation Classifier
---
language:
- en
tags:
- image-classification
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
- f1
---
| [
"# Orientation Classifier\n---\n\nlanguage:\n- en\ntags:\n- image-classification\nlicense: apache-2.0\ndatasets:\n- cifar10\nmetrics:\n- accuracy\n- f1\n\n---"
] | [
"TAGS\n#transformers #endpoints_compatible #region-us \n",
"# Orientation Classifier\n---\n\nlanguage:\n- en\ntags:\n- image-classification\nlicense: apache-2.0\ndatasets:\n- cifar10\nmetrics:\n- accuracy\n- f1\n\n---"
] |
image-to-image | pytorch |
# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Faces
Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Paper: https://arxiv.org/abs/2110.02711
<img src="https://github.com/submission10095/DiffusionCLIP_temp/raw/master/imgs/main1.png" alt="Excerpt from DiffusionCLIP paper showcasing c... | {"library_name": "pytorch", "tags": ["diffusion", "image-to-image"]} | gwang-kim/DiffusionCLIP-CelebA_HQ | null | [
"pytorch",
"diffusion",
"image-to-image",
"arxiv:2110.02711",
"arxiv:1710.10196",
"region:us"
] | null | 2022-04-05T23:58:42+00:00 | [
"2110.02711",
"1710.10196"
] | [] | TAGS
#pytorch #diffusion #image-to-image #arxiv-2110.02711 #arxiv-1710.10196 #region-us
|
# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Faces
Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Paper: URL
<img src="URL alt="Excerpt from DiffusionCLIP paper showcasing comparison of DiffusionCLIP versus other methods for image reconstruction, manipulation, and style transfer... | [
"# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Faces\n\nCreators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye\nPaper: URL\n\n<img src=\"URL alt=\"Excerpt from DiffusionCLIP paper showcasing comparison of DiffusionCLIP versus other methods for image reconstruction, manipulation, and sty... | [
"TAGS\n#pytorch #diffusion #image-to-image #arxiv-2110.02711 #arxiv-1710.10196 #region-us \n",
"# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Faces\n\nCreators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye\nPaper: URL\n\n<img src=\"URL alt=\"Excerpt from DiffusionCLIP paper showcasing... |
null | null | Deep Q-Network applied to Classical Control
==============================
Here you will find a DQN model trained to solve the classical control "CartPole-v0" problem as part of "Coding Challenge for Fatima Fellowship". The agent was trained for 1000 episodes and the framework used to trained the model is Pytorch.
* ... | {} | DMarcelAM/DQN_classical_control | null | [
"region:us"
] | null | 2022-04-06T00:22:03+00:00 | [] | [] | TAGS
#region-us
| Deep Q-Network applied to Classical Control
==============================
Here you will find a DQN model trained to solve the classical control "CartPole-v0" problem as part of "Coding Challenge for Fatima Fellowship". The agent was trained for 1000 episodes and the framework used to trained the model is Pytorch.
* ... | [] | [
"TAGS\n#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... | suey2580/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 | 2022-04-06T00:29:16+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: 1.0766
* Matthews Correlation: 0.5238
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.403175733231667e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5"... | [
"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... |
image-to-image | pytorch |
# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms
Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Paper: https://arxiv.org/abs/2110.02711
<img src="https://github.com/submission10095/DiffusionCLIP_temp/raw/master/imgs/main1.png" alt="Excerpt from DiffusionCLIP paper showcasin... | {"library_name": "pytorch", "tags": ["diffusion", "image-to-image"]} | gwang-kim/DiffusionCLIP-LSUN_Bedroom | null | [
"pytorch",
"diffusion",
"image-to-image",
"arxiv:2110.02711",
"region:us"
] | null | 2022-04-06T01:09:14+00:00 | [
"2110.02711"
] | [] | TAGS
#pytorch #diffusion #image-to-image #arxiv-2110.02711 #region-us
|
# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms
Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Paper: URL
<img src="URL alt="Excerpt from DiffusionCLIP paper showcasing comparison of DiffusionCLIP versus other methods for image reconstruction, manipulation, and style trans... | [
"# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms\n\nCreators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye\nPaper: URL\n\n<img src=\"URL alt=\"Excerpt from DiffusionCLIP paper showcasing comparison of DiffusionCLIP versus other methods for image reconstruction, manipulation, and ... | [
"TAGS\n#pytorch #diffusion #image-to-image #arxiv-2110.02711 #region-us \n",
"# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms\n\nCreators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye\nPaper: URL\n\n<img src=\"URL alt=\"Excerpt from DiffusionCLIP paper showcasing comparison of ... |
null | null | Clone this repo. In the /CIFAR100+CIFAR10_weights/CIFAR100+10_model/ directory, there are three weights for the three models trained on CIFAR100 + CIFAR10 dataset. The names of the weights can be found in my notebook respectively:
https://colab.research.google.com/drive/1zInKDML24y8eZTtElMrdxGZjaK4F-vTu?usp=sharing
... | {"license": "afl-3.0"} | nakkhatra/upside_down_detector | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-06T01:29:13+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| Clone this repo. In the /CIFAR100+CIFAR10_weights/CIFAR100+10_model/ directory, there are three weights for the three models trained on CIFAR100 + CIFAR10 dataset. The names of the weights can be found in my notebook respectively:
URL
The weights of the 2 models trained on CIFAR100+Svhn is in the root directory.
L... | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
text-generation | transformers |
# InCoder 6B
A 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or ... | {"license": "cc-by-nc-4.0", "tags": ["code", "python", "javascript"]} | facebook/incoder-6B | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"code",
"python",
"javascript",
"arxiv:2204.05999",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-06T02:17:49+00:00 | [
"2204.05999"
] | [] | TAGS
#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# InCoder 6B
A 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or ... | [
"# InCoder 6B\n\nA 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.\n\nThe model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, B... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# InCoder 6B\n\nA 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows... |
null | null | This is a Upside down detector on MNIST dateset using tensorflow/keras. | {} | SeifMosaad/mnist-upsidedowndetector | null | [
"region:us"
] | null | 2022-04-06T02:26:40+00:00 | [] | [] | TAGS
#region-us
| This is a Upside down detector on MNIST dateset using tensorflow/keras. | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Upside Down Classifier
The model was trained for the task of orientation classification. The model was trained on `CIFAR-100` dataset which contains 60000 images covering 600 classes of 32x32 RGB images.
# Data
Data was split to `50000` train samples and `10000` test samples.
# Results
The training of the model on ... | {} | ABEMark45/upside-down-classifier | null | [
"region:us"
] | null | 2022-04-06T02:28:18+00:00 | [] | [] | TAGS
#region-us
| # Upside Down Classifier
The model was trained for the task of orientation classification. The model was trained on 'CIFAR-100' dataset which contains 60000 images covering 600 classes of 32x32 RGB images.
# Data
Data was split to '50000' train samples and '10000' test samples.
# Results
The training of the model on ... | [
"# Upside Down Classifier\nThe model was trained for the task of orientation classification. The model was trained on 'CIFAR-100' dataset which contains 60000 images covering 600 classes of 32x32 RGB images.",
"# Data\nData was split to '50000' train samples and '10000' test samples.",
"# Results\nThe training ... | [
"TAGS\n#region-us \n",
"# Upside Down Classifier\nThe model was trained for the task of orientation classification. The model was trained on 'CIFAR-100' dataset which contains 60000 images covering 600 classes of 32x32 RGB images.",
"# Data\nData was split to '50000' train samples and '10000' test samples.",
... |
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
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base", "results": []}]} | emon1521/wav2vec2-base | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T02:40:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base
=============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0808
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\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:... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4... |
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. -->
# ls-timit-wsj0-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following resul... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ls-timit-wsj0-100percent-supervised-aug", "results": []}]} | Kuray107/ls-timit-wsj0-100percent-supervised-aug | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T03:04:41+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ls-timit-wsj0-100percent-supervised-aug
=======================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0489
* Wer: 0.0275
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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... | [
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null | null | **Model Name:** EfficientNet B3
**Classification Type:** Binary classification of normal vs upside-down images
**Created For:** Coding Challenge for Fatima Fellowship | {} | rkoushikroy2/upside_down_efficientnet | null | [
"region:us"
] | null | 2022-04-06T03:24:50+00:00 | [] | [] | TAGS
#region-us
| Model Name: EfficientNet B3
Classification Type: Binary classification of normal vs upside-down images
Created For: Coding Challenge for Fatima Fellowship | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 708521506
- CO2 Emissions (in grams): 7.419693550936528
## Validation Metrics
- Loss: 1.4744563102722168
- Rouge1: 30.0761
- Rouge2: 10.142
- RougeL: 27.2745
- RougeLsum: 27.2831
- Gen Len: 13.8746
## Usage
You can use cURL to access this m... | {"language": "en", "tags": "autotrain", "datasets": ["unjustify/autotrain-data-Create_Question_Model"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.419693550936528} | unjustify/autotrain-Create_Question_Model-708521506 | null | [
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"t5",
"text2text-generation",
"autotrain",
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"dataset:unjustify/autotrain-data-Create_Question_Model",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T03:45:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-unjustify/autotrain-data-Create_Question_Model #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 708521506
- CO2 Emissions (in grams): 7.419693550936528
## Validation Metrics
- Loss: 1.4744563102722168
- Rouge1: 30.0761
- Rouge2: 10.142
- RougeL: 27.2745
- RougeLsum: 27.2831
- Gen Len: 13.8746
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 708521506\n- CO2 Emissions (in grams): 7.419693550936528",
"## Validation Metrics\n\n- Loss: 1.4744563102722168\n- Rouge1: 30.0761\n- Rouge2: 10.142\n- RougeL: 27.2745\n- RougeLsum: 27.2831\n- Gen Len: 13.8746",
"## Usage\n\nYou can ... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 708... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | husnu/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T04:02:53+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-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_6.1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4380
* Wer: 0.3508
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* t... |
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. -->
# beirt-irish-translation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the eval... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "beirt-irish-translation", "results": []}]} | pbdevpros/beirt-irish-translation | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T04:05:49+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# beirt-irish-translation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0227
- Bleu: 78.9918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# beirt-irish-translation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0227\n- Bleu: 78.9918",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# beirt-irish-translation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.022... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | Siddique/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T04:08:38+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-turkish-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 pro... | [
"# wav2vec2-large-xls-r-300m-turkish-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 n... | [
"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-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
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. -->
# bert-base-uncased-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", ... | Intel/bert-base-uncased-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T06:30:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-mrpc
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6978
- Accuracy: 0.8603
- F1: 0.9042
- Combined Score: 0.8822
### Training hyperparameters
The following hyperparameters were used during tr... | [
"# bert-base-uncased-mrpc\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6978\n- Accuracy: 0.8603\n- F1: 0.9042\n- Combined Score: 0.8822",
"### Training hyperparameters\n\nThe following hyperparameters were... | [
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"# bert-base-uncased-mrpc\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieve... |
automatic-speech-recognition | transformers |
This repository contains a number of experiments for the [PSST Challenge](https://psst.study/).
As the test set is unavailable, all numbers are based on the validation set.
The experiments in the tables below were finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wa... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["jimregan/psst", "timit_asr"]} | jimregan/psst-partial-timit | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:jimregan/psst",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T07:30:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #en #dataset-jimregan/psst #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| This repository contains a number of experiments for the PSST Challenge.
As the test set is unavailable, all numbers are based on the validation set.
The experiments in the tables below were finetuned on Wav2vec 2.0 Base, No finetuning
Our overall best performing model (FER 9.2%, PER: 21.0%) was based on Wav2vec ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #en #dataset-jimregan/psst #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
#
[Federico Bianchi](https://federicobianchi.io/) •
[Debora Nozza](http://dnozza.github.io/) •
[Dirk Hovy](http://www.dirkhovy.com/)
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different la... | {"language": "multilingual", "tags": ["emotion", "emotion-analysis", "multilingual"], "widget": [{"text": "Guarda! ci sono dei bellissimi capibara!", "example_title": "Emotion Classification 1"}, {"text": "Sei una testa di cazzo!!", "example_title": "Emotion Classification 2"}, {"text": "Quelle bonne nouvelle!", "examp... | MilaNLProc/xlm-emo-t | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"emotion",
"emotion-analysis",
"multilingual",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-06T07:56:26+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #emotion #emotion-analysis #multilingual #autotrain_compatible #endpoints_compatible #has_space #region-us
|
#
Federico Bianchi •
Debora Nozza •
Dirk Hovy
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available e... | [
"## Abstract\n\nDetecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 langu... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #emotion #emotion-analysis #multilingual #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Abstract\n\nDetecting emotion in text allows social and computational scientists to study how people behave and react to online events... |
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... | thangcv/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 | 2022-04-06T08:11:14+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: 0.2156
* Accuracy: 0.924
* F1: 0.9243
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. -->
# t5-small-finetuned-wikisql
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wiki_sql dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wiki_sql"], "model-index": [{"name": "t5-small-finetuned-wikisql", "results": []}]} | edangx100/t5-small-finetuned-wikisql | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wiki_sql",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T08:15:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wiki_sql #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikisql
==========================
This model is a fine-tuned version of t5-small on the wiki\_sql dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1246
* Rouge2 Precision: 0.8187
* Rouge2 Recall: 0.7269
* Rouge2 Fmeasure: 0.7629
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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 #t5 #text2text-generation #generated_from_trainer #dataset-wiki_sql #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\... |
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-small-Linguists_summariser
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-Linguists_summariser", "results": []}]} | Linguist/t5-small-Linguists_summariser | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T08:25:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-Linguists_summariser
This model is a fine-tuned version of t5-small on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# t5-small-Linguists_summariser\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-Linguists_summariser\n\nThis model is a fine-tuned version of t5-small on the xsum datas... |
fill-mask | 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. -->
# distilbert-base-uncased-finetuned-powo
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-powo", "results": []}]} | ViktorDo/distilbert-base-uncased-finetuned-powo | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T08:29:11+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-powo
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# distilbert-base-uncased-finetuned-powo\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-powo\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the followin... |
text-classification | transformers |
# INT8 BERT base uncased finetuned MRPC
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes fr... | {"language": "en", "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "QuantizationAwareTraining"], "datasets": ["mrpc"], "metrics": ["f1"]} | Intel/bert-base-uncased-mrpc-int8-qat | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"QuantizationAwareTraining",
"en",
"dataset:mrpc",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T08:33:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #QuantizationAwareTraining #en #dataset-mrpc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| INT8 BERT base uncased finetuned MRPC
=====================================
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.
##... | [
"### QuantizationAwareTraining\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.",
"### Test result",
"### Load with optimum:",
"### Training hype... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #QuantizationAwareTraining #en #dataset-mrpc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### QuantizationAwareTraining\n\n\nThis is an INT8 PyTorch model quantized with... |
image-segmentation | transformers |
# Segformer-b0, fine-tuned on Sidewalk
This repository contains the weights of a `SegFormerForSemanticSegmentation` model.
It was trained using the example script. | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"], "widget": [{"src": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg", "example_title": "Brugge"}]} | nielsr/segformer-finetuned-sidewalk | null | [
"transformers",
"pytorch",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-06T08:56:13+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Segformer-b0, fine-tuned on Sidewalk
This repository contains the weights of a 'SegFormerForSemanticSegmentation' model.
It was trained using the example script. | [
"# Segformer-b0, fine-tuned on Sidewalk\n\nThis repository contains the weights of a 'SegFormerForSemanticSegmentation' model.\n\nIt was trained using the example script."
] | [
"TAGS\n#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Segformer-b0, fine-tuned on Sidewalk\n\nThis repository contains the weights of a 'SegFormerForSemanticSegmentation' model.\... |
fill-mask | 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-chinese-complaint-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-chinese-complaint-128", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | xxr/bert-base-chinese-complaint-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T09:12:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-complaint-128
===============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3004
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-to-speech | espnet |
# Tacotron2 Gronings
| {"language": "gos", "tags": ["espnet", "audio", "text-to-speech"]} | wietsedv/tacotron2-gronings | null | [
"espnet",
"audio",
"text-to-speech",
"gos",
"has_space",
"region:us"
] | null | 2022-04-06T09:59:15+00:00 | [] | [
"gos"
] | TAGS
#espnet #audio #text-to-speech #gos #has_space #region-us
|
# Tacotron2 Gronings
| [
"# Tacotron2 Gronings"
] | [
"TAGS\n#espnet #audio #text-to-speech #gos #has_space #region-us \n",
"# Tacotron2 Gronings"
] |
null | null | # insightface
- https://github.com/deepinsight/insightface
- SCRFD
- https://github.com/deepinsight/insightface/tree/master/detection/scrfd
- https://1drv.ms/u/s!AswpsDO2toNKqyYWxScdiTITY4TQ?e=DjXof9
- https://1drv.ms/u/s!AswpsDO2toNKqyPVLI44ahNBsOMR?e=esPrBL
- https://1... | {} | public-data/insightface | null | [
"onnx",
"region:us",
"has_space"
] | null | 2022-04-06T10:15:15+00:00 | [] | [] | TAGS
#onnx #region-us #has_space
| # insightface
- URL
- SCRFD
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- Person Detection
- URL
- URL
- Face Alignment (FaceSynthetics)
- URL
- URL
... | [
"# insightface\n\n- URL\n - SCRFD\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - Person Detection\n - URL\n - URL\n - Face Alignment (FaceSynthetics)\n - URL\... | [
"TAGS\n#onnx #region-us #has_space \n",
"# insightface\n\n- URL\n - SCRFD\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - Person Detection\n - URL\n - URL\n - Face ... |
text-to-speech | espnet |
# Tacotron2 Gronings
| {"language": "gos", "tags": ["espnet", "audio", "text-to-speech"]} | wietsedv/tacotron2-dutch | null | [
"espnet",
"audio",
"text-to-speech",
"gos",
"has_space",
"region:us"
] | null | 2022-04-06T10:25:48+00:00 | [] | [
"gos"
] | TAGS
#espnet #audio #text-to-speech #gos #has_space #region-us
|
# Tacotron2 Gronings
| [
"# Tacotron2 Gronings"
] | [
"TAGS\n#espnet #audio #text-to-speech #gos #has_space #region-us \n",
"# Tacotron2 Gronings"
] |
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... | pitspits/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 | 2022-04-06T10:54:02+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: 0.2236
* Accuracy: 0.925
* F1: 0.9251
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... |
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. -->
# AlbertoBertnews
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://h... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "AlbertoBertnews", "results": []}]} | GioReg/AlbertoBertnews | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T11:00:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# AlbertoBertnews
This model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1382
- Accuracy: 0.9640
- F1: 0.9635
## Model description
More information needed
## Intended uses & limit... | [
"# AlbertoBertnews\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1382\n- Accuracy: 0.9640\n- F1: 0.9635",
"## Model description\n\nMore information needed",
"## Int... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# AlbertoBertnews\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.\nIt achieves the... |
null | transformers |
# Japanese Dummy Tokenizer
Repository containing a dummy Japanese Tokenizer trained on ```snow_simplified_japanese_corpus``` dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner.
## Intended uses & limitations
You can use this tokenizer to tokenize Japanese sentences.
## How t... | {"language": ["en", "ja"], "license": "mit", "tags": ["ja", "japanese", "tokenizer"], "datasets": ["snow_simplified_japanese_corpus"], "widget": [{"text": "\u8ab0\u304c\u4e00\u756a\u306b\u7740\u304f\u304b\u79c1\u306b\u306f\u5206\u304b\u308a\u307e\u305b\u3093\u3002"}]} | ybelkada/japanese-dummy-tokenizer | null | [
"transformers",
"ja",
"japanese",
"tokenizer",
"en",
"dataset:snow_simplified_japanese_corpus",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T11:31:37+00:00 | [] | [
"en",
"ja"
] | TAGS
#transformers #ja #japanese #tokenizer #en #dataset-snow_simplified_japanese_corpus #license-mit #endpoints_compatible #region-us
|
# Japanese Dummy Tokenizer
Repository containing a dummy Japanese Tokenizer trained on dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner.
## Intended uses & limitations
You can use this tokenizer to tokenize Japanese sentences.
## How to use it
## How to train the token... | [
"# Japanese Dummy Tokenizer\n\nRepository containing a dummy Japanese Tokenizer trained on dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner.",
"## Intended uses & limitations\n\nYou can use this tokenizer to tokenize Japanese sentences.",
"## How to use it",
"## How t... | [
"TAGS\n#transformers #ja #japanese #tokenizer #en #dataset-snow_simplified_japanese_corpus #license-mit #endpoints_compatible #region-us \n",
"# Japanese Dummy Tokenizer\n\nRepository containing a dummy Japanese Tokenizer trained on dataset. The tokenizer has been trained using Hugging Face datasets in a streami... |
audio-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. -->
# hubert-base-common-language
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/h... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-common-language", "results": []}]} | Graphcore/hubert-base-common-language | null | [
"transformers",
"pytorch",
"safetensors",
"optimum_graphcore",
"hubert",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T11:36:35+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# hubert-base-common-language
This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# hubert-base-common-language\n\nThis model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3477\n- Accuracy: 0.7317",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# hubert-base-common-language\n\nThis model is a fine-tuned version of ... |
audio-classification | transformers |
# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fi... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification", "emotion-recognition"], "datasets": ["msp-podcast"], "inference": true, "pipeline_tag": "audio-classification"} | audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"speech",
"audio",
"audio-classification",
"emotion-recognition",
"en",
"dataset:msp-podcast",
"arxiv:2203.07378",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-06T11:40:02+00:00 | [
"2203.07378"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #speech #audio #audio-classification #emotion-recognition #en #dataset-msp-podcast #arxiv-2203.07378 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fi... | [
"# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0\n\nThe model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #speech #audio #audio-classification #emotion-recognition #en #dataset-msp-podcast #arxiv-2203.07378 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0\n\nThe model ... |
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | ankitkupadhyay/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T11:55:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
text-classification | transformers | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data. | {} | Stremie/bert-base-uncased-clickbait | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T11:58:26+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #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. -->
# AlbertoBertrecensioni
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](htt... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "AlbertoBertrecensioni", "results": []}]} | GioReg/AlbertoBertrecensioni | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T12:10:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# AlbertoBertrecensioni
This model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trai... | [
"# AlbertoBertrecensioni\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# AlbertoBertrecensioni\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Mo... |
text-classification | transformers |
This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data.
In order to test this model, try a tweet on the right! | {"language": ["eng"], "license": "apache-2.0", "tags": ["Tweet", "Twitter", "Clickbait", "Spam"], "datasets": ["Webis-Clickbait-17"], "widget": [{"text": "In just 4 days you can increase your net worth."}, {"text": "Nasa aborts second attempt to launch giant Moon rocket"}, {"text": "The most successful people do these ... | Stremie/roberta-base-clickbait | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"Tweet",
"Twitter",
"Clickbait",
"Spam",
"eng",
"dataset:Webis-Clickbait-17",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T12:25:45+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #roberta #text-classification #Tweet #Twitter #Clickbait #Spam #eng #dataset-Webis-Clickbait-17 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data.
In order to test this model, try a tweet on the right! | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #Tweet #Twitter #Clickbait #Spam #eng #dataset-Webis-Clickbait-17 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the PharmaCoNER dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitat... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "eHR", "spanish"], "datasets": ["PlanTL-GOB-ES/pharmaconer"], "metrics": ["f1"], "widget": [{"text": "Se realiz\u00f3 estudio anal\u00edtico destacando incremento de niveles de PTH y vitamina D (103,7 pg/ml y 272 ng/ml, respectivamente), a... | PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"biomedical",
"clinical",
"eHR",
"spanish",
"es",
"dataset:PlanTL-GOB-ES/pharmaconer",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T12:43:19+00:00 | [
"1907.11692"
] | [
"es"
] | TAGS
#transformers #pytorch #roberta #token-classification #biomedical #clinical #eHR #spanish #es #dataset-PlanTL-GOB-ES/pharmaconer #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the PharmaCoNER dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- Model description
- Intended uses and limitations
- How to use
- Limitations and bias
- Training
- Evaluation
- Additional info... | [
"# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the PharmaCoNER dataset.",
"## Table of contents\n<details>\n<summary>Click to expand</summary>\n\n- Model description\n- Intended uses and limitations\n- How to use\n- Limitations and bias\n- Training\n- Evaluation\... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #biomedical #clinical #eHR #spanish #es #dataset-PlanTL-GOB-ES/pharmaconer #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Spanish RoBERTa-base biomedical model finetuned for the Named En... |
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/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chrismedlandf1-elonmusk-scarbstech/1649253035547/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chrismedlandf1-elonmusk-scarbstech | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T12:44:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Craig Scarborough & Chris Medland
@chrismedlandf1-elonmusk-scarbstech
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 d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
Base model: [roberta-large](https://huggingface.co/roberta-large)
Fine tuned for persuadee donation detection on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete dialogue from Persuasion For Good, the task is to predict the binary label:
... | {"license": "mit"} | LACAI/roberta-large-PFG-donation-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T13:00:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Base model: roberta-large
Fine tuned for persuadee donation detection on the Persuasion For Good Dataset (Wang et al., 2019):
Given a complete dialogue from Persuasion For Good, the task is to predict the binary label:
- 0: the persuadee does not intend to donate
- 1: the persuadee intends to donate
Only... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data. | {} | Stremie/xlm-roberta-base-clickbait | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T13:00:23+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data. | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #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/1433115414679150596/6E1j... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/twommof1/1649253931186/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/twommof1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T13:02:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tommo
@twommof1
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
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ## GPT2 being trained on Ukrainian news.
### General info:
The model is not ready yet but I'm working on it. It also has a relatively small context window, which makes it quite uninteresting.
### Example of usage:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_p... | {"language": "uk", "license": "afl-3.0"} | kyryl0s/gpt2-uk-xxs | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"uk",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T13:04:49+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #uk #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## GPT2 being trained on Ukrainian news.
### General info:
The model is not ready yet but I'm working on it. It also has a relatively small context window, which makes it quite uninteresting.
### Example of usage:
| [
"## GPT2 being trained on Ukrainian news.",
"### General info:\nThe model is not ready yet but I'm working on it. It also has a relatively small context window, which makes it quite uninteresting.",
"### Example of usage:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #uk #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 being trained on Ukrainian news.",
"### General info:\nThe model is not ready yet but I'm working on it. It also has a relatively small contex... |
text-generation | transformers | # Fairseq-dense 13B - Janeway
## Model Description
Fairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts o... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-13B-Janeway | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-06T13:36:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 13B - Janeway
## Model Description
Fairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts o... | [
"# Fairseq-dense 13B - Janeway",
"## Model Description\r\nFairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.",
"## Training data\r\nThe training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janew... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fairseq-dense 13B - Janeway",
"## Model Description\r\nFairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.",
"## Training data\r\nThe tra... |
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/1252178304192389120/bXT3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chrismedlandf1/1649255880540/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chrismedlandf1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T13:36:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Chris Medland
@chrismedlandf1
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"
] |
image-segmentation | 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. -->
# sidewalk-semantic-demo
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None d... | {"license": "apache-2.0", "tags": ["vision", "generated_from_trainer", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"], "widget": [{"src": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg", "example_title": "Brugge"}], "base_model": "nvid... | nielsr/sidewalk-semantic-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"generated_from_trainer",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"base_model:nvidia/mit-b0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T13:51:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #generated_from_trainer #image-segmentation #dataset-segments/sidewalk-semantic #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us
| sidewalk-semantic-demo
======================
This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7591
* Mean Iou: 0.1135
* Mean Accuracy: 0.1608
* Overall Accuracy: 0.6553
* Per Category Iou: [nan, 0.38512238586129177, 0.723869... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #generated_from_trainer #image-segmentation #dataset-segments/sidewalk-semantic #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
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. -->
# robbert-twitter-sentiment
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelob... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["dutch_social"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "robbert-twitter-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dutch_social", "type"... | btjiong/robbert-twitter-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:dutch_social",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T13:54:31+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| robbert-twitter-sentiment
=========================
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on the dutch\_social dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6818
* Accuracy: 0.749
* F1: 0.7492
* Precision: 0.7494
* Recall: 0.749
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #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: 5e-05\... |
null | null | # Fatima Fellowship Challenge
**This repo contains a trained keras model built to effectively classify between fake and real news** | {"license": "afl-3.0"} | Busayor/Fake_news_classifier_bert | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-06T14:03:39+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| # Fatima Fellowship Challenge
This repo contains a trained keras model built to effectively classify between fake and real news | [
"# Fatima Fellowship Challenge\r\n\r\nThis repo contains a trained keras model built to effectively classify between fake and real news"
] | [
"TAGS\n#license-afl-3.0 #region-us \n",
"# Fatima Fellowship Challenge\r\n\r\nThis repo contains a trained keras model built to effectively classify between fake and real news"
] |
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... | Danni/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 | 2022-04-06T14:04:28+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: 0.4994
* Matthews Correlation: 0.4411
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: 1",
"### 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | moshew/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T14:27:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7703
* Accuracy: 0.9187
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\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-clinc_oos #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* lea... |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | pitspits/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T14:27:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1398
* F1: 0.8651
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# deberta-base-squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "deberta-base-squad", "results": []}]} | Graphcore/deberta-base-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"optimum_graphcore",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T14:38:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #optimum_graphcore #deberta #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# deberta-base-squad
This model is a fine-tuned version of microsoft/deberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# deberta-base-squad\n\nThis model is a fine-tuned version of microsoft/deberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #optimum_graphcore #deberta #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# deberta-base-squad\n\nThis model is a fine-tuned version of microsoft/deberta-base on the squad dataset.",
"## Model desc... |
null | keras |
DEMO MODEL --
Selahattin Serdar Helli and Andaç Hamamcı with the Department of Biomedical Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Turkey
| {"tags": ["heatmapregression", "landmarkdetection", "medicalimaging", "kneeview"]} | SerdarHelli/Knee-View-Merchant-Landmark-Detection | null | [
"keras",
"heatmapregression",
"landmarkdetection",
"medicalimaging",
"kneeview",
"has_space",
"region:us"
] | null | 2022-04-06T14:54:10+00:00 | [] | [] | TAGS
#keras #heatmapregression #landmarkdetection #medicalimaging #kneeview #has_space #region-us
|
DEMO MODEL --
Selahattin Serdar Helli and Andaç Hamamcı with the Department of Biomedical Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Turkey
| [] | [
"TAGS\n#keras #heatmapregression #landmarkdetection #medicalimaging #kneeview #has_space #region-us \n"
] |
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. -->
# flyswot
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-2... | {"tags": ["generated_from_trainer"], "base_model": "flyswot/convnext-tiny-224_flyswot", "model-index": [{"name": "flyswot", "results": []}]} | flyswot/flyswot | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:flyswot/convnext-tiny-224_flyswot",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T14:56:05+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #generated_from_trainer #base_model-flyswot/convnext-tiny-224_flyswot #autotrain_compatible #endpoints_compatible #region-us
| flyswot
=======
This model is a fine-tuned version of flyswot/convnext-tiny-224\_flyswot on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #generated_from_trainer #base_model-flyswot/convnext-tiny-224_flyswot #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\... |
sentence-similarity | sentence-transformers |
# DistilUSE Podcast Natural Questions
This is a [sentence-transformers](https://www.SBERT.net) model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as [described here](https://www.pinecone.io/learn/spotify-podcast-search/).
## Usage ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pinecone/distiluse-podcast-nq | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T14:57:43+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# DistilUSE Podcast Natural Questions
This is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tran... | [
"# DistilUSE Podcast Natural Questions\n\nThis is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have s... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# DistilUSE Podcast Natural Questions\n\nThis is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spoti... |
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": []}]} | gary109/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 | 2022-04-06T14:57:56+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.
It achieves the following results on the evaluation set:
* Loss: 0.4707
* Wer: 0.3411
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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: 3... |
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-casedfinetuned-fake-news-detection
This model is a fine-tuned version of [distilbert-base-cased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "distilbert-base-casedfinetuned-fake-news-detection", "results": []}]} | raileymontalan/distilbert-base-casedfinetuned-fake-news-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T15:06:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-casedfinetuned-fake-news-detection
==================================================
This model is a fine-tuned version of distilbert-base-cased on the Fake and Reals News dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0019
* F1: 0.9998
* Accuracy: 0.9998
The Fake and... | [
"### 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: 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... |
fill-mask | transformers |
A simple fake news detector that utilizes RoBERTa. <br/>
It was fine-tuned on [clmentbisaillon/fake-and-real-news-dataset](https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) | {"license": "cc-by-nc-sa-4.0"} | Ramansh/RoBERTa-fake-news-detection | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T15:08:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
A simple fake news detector that utilizes RoBERTa. <br/>
It was fine-tuned on clmentbisaillon/fake-and-real-news-dataset | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | {} | Stremie/bert-base-uncased-clickbait-keywords | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T15:21:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #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. -->
# QuBERTa-finetuned-pos
This model is a fine-tuned version of [Llamacha/QuBERTa](https://huggingface.co/Llamacha/QuBERTa) on the N... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "QuBERTa-finetuned-pos", "results": []}]} | millawell/QuBERTa-finetuned-pos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T15:23:33+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| QuBERTa-finetuned-pos
=====================
This model is a fine-tuned version of Llamacha/QuBERTa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4249
* Precision: 0.8372
* Recall: 0.8702
* F1: 0.8534
* Accuracy: 0.8623
Model description
-----------------
More informatio... | [
"### 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 #roberta #token-classification #generated_from_trainer #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: 16\n* eval\\_batch\\_s... |
text-classification | transformers | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | {} | Stremie/roberta-base-clickbait-keywords | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T15:57:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
audio-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. -->
# xtreme_s_xlsr_300m_fleurs_langid
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"language": ["all"], "license": "apache-2.0", "tags": ["fleurs-lang_id", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "metrics": ["accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_langid", "results": []}]} | anton-l/xtreme_s_xlsr_300m_fleurs_langid | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"fleurs-lang_id",
"google/xtreme_s",
"generated_from_trainer",
"all",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T16:16:59+00:00 | [] | [
"all"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #fleurs-lang_id #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_fleurs\_langid
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.7271
* Accuracy Af Za: 0.3865
* Accuracy Am Et: 0.8818
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 8\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #fleurs-lang_id #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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-cased-finetuned-fake-news-detection
This model is a fine-tuned version of [distilbert-base-cased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-fake-news-detection", "results": []}]} | raileymontalan/distilbert-base-cased-finetuned-fake-news-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T16:40:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased-finetuned-fake-news-detection
===================================================
This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0043
* F1: 0.9996
* Accuracy: 0.9996
Model description
-------... | [
"### 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: 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... |
text-classification | transformers |
# Bert-base-uncased-sentiment
BERT stands for Bidirectional Encoder Representations from Transformers. It is a recent paper published by researchers at Google AI Language. BERT makes use of Transformer, an attention mechanism that learns contextual relations between words (or sub-words) in a text. In its vanilla for... | {"language": ["en"]} | Miniproject/BERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T16:52:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
| Bert-base-uncased-sentiment
===========================
BERT stands for Bidirectional Encoder Representations from Transformers. It is a recent paper published by researchers at Google AI Language. BERT makes use of Transformer, an attention mechanism that learns contextual relations between words (or sub-words) in a... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | hafidber/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T16:52:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
null | keras | ## Overview
MNIST Net is a tensorflow Mobile Net V2 model fine tuned for 28x28x1 MNIST Handwritten Digits Classification
The Images are internally rescaled in [0, 1] range and since Mobile Net V2 expects atleast 32x32 images, zero padding is added, also to translate the image to 3 color channels, we have used 1x1 Conv2... | {} | Ritvik19/mnist-net | null | [
"keras",
"region:us"
] | null | 2022-04-06T17:14:16+00:00 | [] | [] | TAGS
#keras #region-us
| ## Overview
MNIST Net is a tensorflow Mobile Net V2 model fine tuned for 28x28x1 MNIST Handwritten Digits Classification
The Images are internally rescaled in [0, 1] range and since Mobile Net V2 expects atleast 32x32 images, zero padding is added, also to translate the image to 3 color channels, we have used 1x1 Conv2... | [
"## Overview\nMNIST Net is a tensorflow Mobile Net V2 model fine tuned for 28x28x1 MNIST Handwritten Digits Classification\nThe Images are internally rescaled in [0, 1] range and since Mobile Net V2 expects atleast 32x32 images, zero padding is added, also to translate the image to 3 color channels, we have used 1x... | [
"TAGS\n#keras #region-us \n",
"## Overview\nMNIST Net is a tensorflow Mobile Net V2 model fine tuned for 28x28x1 MNIST Handwritten Digits Classification\nThe Images are internally rescaled in [0, 1] range and since Mobile Net V2 expects atleast 32x32 images, zero padding is added, also to translate the image to 3... |
question-answering | transformers | Model trained for 1 epoch on 1000 examples from the `adversarial_qa` dataset
| {} | KrishnaAgarwal16/607-project-adversarial | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T17:23:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| Model trained for 1 epoch on 1000 examples from the 'adversarial_qa' dataset
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #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. -->
# aradia-ctc-distilhubert-ft
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilh... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_sm", "generated_from_trainer"], "model-index": [{"name": "aradia-ctc-distilhubert-ft", "results": []}]} | abdusah/aradia-ctc-distilhubert-ft | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_sm",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T17:40:14+00:00 | [] | [] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_sm #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| aradia-ctc-distilhubert-ft
==========================
This model is a fine-tuned version of ntu-spml/distilhubert on the ABDUSAHMBZUAI/ARABIC\_SPEECH\_MASSIVE\_SM - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7114
* Wer: 0.8908
Model description
-----------------
More informat... | [
"### 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 #hubert #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_sm #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: 0.0003\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... | schorndorfer/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 | 2022-04-06T19:24:40+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: 0.2177
* Accuracy: 0.924
* F1: 0.9245
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... |
text-classification | transformers | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | {} | Stremie/xlm-roberta-base-clickbait-keywords | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T19:56:11+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model classifies whether a tweet is clickbait or not. It has been trained using Webis-Clickbait-17 dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #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. -->
# bertweet-base-cased-covid19-hateval
This model is a fine-tuned version of [vinai/bertweet-covid19-base-cased](https://huggingfac... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bertweet-base-cased-covid19-hateval", "results": []}]} | ChrisZeng/bertweet-base-cased-covid19-hateval | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T20:52:44+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-cased-covid19-hateval
===================================
This model is a fine-tuned version of vinai/bertweet-covid19-base-cased on the HatEval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4817
* Accuracy: 0.773
* F1: 0.7722
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
image-classification | null |
# CIFAR-10 Upside Down Classifier
For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track.
<a href="https://wandb.ai/dealer56/cifar-updown-classifier/reports/CIFAR-10-Upside-Down-Classifier-Fatima-Fellowship-2022-Coding-Challenge-Vision---VmlldzoxODA2MDE4" target="_parent"><img src="https://img.shields.... | {"license": "cc-by-sa-4.0", "tags": ["image-classification"], "datasets": ["cifar10"], "metrics": ["accuracy"], "thumbnail": "https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png", "inference": false} | ID56/FF-Vision-CIFAR | null | [
"pytorch",
"image-classification",
"dataset:cifar10",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-04-06T21:02:53+00:00 | [] | [] | TAGS
#pytorch #image-classification #dataset-cifar10 #license-cc-by-sa-4.0 #region-us
|
# CIFAR-10 Upside Down Classifier
For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track.
<a href="URL target="_parent"><img src="URL alt="W&B Report"/></a>
<img src="URL alt="Cover Image" width="800"/>
## Usage
### Model Definition
### Loading the Model from Hub
### Running Inference
| [
"# CIFAR-10 Upside Down Classifier\n\nFor the Fatima Fellowship 2022 Coding Challenge, DL for Vision track.\n\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"W&B Report\"/></a> \n\n<img src=\"URL alt=\"Cover Image\" width=\"800\"/>",
"## Usage",
"### Model Definition",
"### Loading the Model from Hub",... | [
"TAGS\n#pytorch #image-classification #dataset-cifar10 #license-cc-by-sa-4.0 #region-us \n",
"# CIFAR-10 Upside Down Classifier\n\nFor the Fatima Fellowship 2022 Coding Challenge, DL for Vision track.\n\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"W&B Report\"/></a> \n\n<img src=\"URL alt=\"Cover Image\... |
text2text-generation | transformers | # it5-small-lfqa
It is a (test) T5 ([IT5](https://huggingface.co/gsarti/it5-small)) small model trained on a lfqa dataset.
<p align="center">
<img src="https://www.arthipo.com/image/cache/catalog/artists-painters/y/yayoi-kusama/yoiku378-Yayoi-Kusama-A-Circus-Rider's-Dream-837x1000.jpg" width="400"> </br>
Ya... | {"language": ["it"], "datasets": ["custom"]} | efederici/it5-small-lfqa | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"it",
"dataset:custom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T21:17:14+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #it #dataset-custom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # it5-small-lfqa
It is a (test) T5 (IT5) small model trained on a lfqa dataset.
<p align="center">
<img src="URL width="400"> </br>
Yayoi Kusama, A circus Rider's Dream, 1955
</p>
## Training Data
This model was trained on a lfqa dataset. The model provide long-form answers to open domain questions (maybe... | [
"# it5-small-lfqa\n\nIt is a (test) T5 (IT5) small model trained on a lfqa dataset. \n\n<p align=\"center\">\n <img src=\"URL width=\"400\"> </br>\n Yayoi Kusama, A circus Rider's Dream, 1955\n</p>",
"## Training Data\n\nThis model was trained on a lfqa dataset. The model provide long-form answers to open ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #it #dataset-custom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# it5-small-lfqa\n\nIt is a (test) T5 (IT5) small model trained on a lfqa dataset. \n\n<p align=\"center\">\n <img src=\"URL width... |
null | null |
# Temp Model
Hello there what is up! | {"extra_gated_prompt": "You agree to not use the model to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company": "text", "Country": "text", "I agree to use this model for non-commerical use ONLY": "checkbox"}} | NimaBoscarino/temp-model | null | [
"region:us"
] | null | 2022-04-06T21:27:34+00:00 | [] | [] | TAGS
#region-us
|
# Temp Model
Hello there what is up! | [
"# Temp Model\n\nHello there what is up!"
] | [
"TAGS\n#region-us \n",
"# Temp Model\n\nHello there what is up!"
] |
null | null |
## General Information:
Used Dataset: cats_vs_dogs (https://huggingface.co/datasets/cats_vs_dogs)
Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0.
Used Library: Pytorch
Used Model: ResNet18 from torchvision
Number of classes: 2 (0 means No flip and 1 means Fl... | {"license": "apache-2.0"} | shamimtowhid/upside_down_detector | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-04-06T21:32:45+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
## General Information:
Used Dataset: cats_vs_dogs (URL
Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0.
Used Library: Pytorch
Used Model: ResNet18 from torchvision
Number of classes: 2 (0 means No flip and 1 means Flipped image)
Train Test Split: 70-30
... | [
"## General Information:\r\nUsed Dataset: cats_vs_dogs (URL\r\n\r\nUsed Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. \r\n\r\nUsed Library: Pytorch\r\n\r\nUsed Model: ResNet18 from torchvision\r\n\r\nNumber of classes: 2 (0 means No flip and 1 means Flipped image)\r\n\r\nT... | [
"TAGS\n#license-apache-2.0 #region-us \n",
"## General Information:\r\nUsed Dataset: cats_vs_dogs (URL\r\n\r\nUsed Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. \r\n\r\nUsed Library: Pytorch\r\n\r\nUsed Model: ResNet18 from torchvision\r\n\r\nNumber of classes: 2 (0 mean... |
null | null | The files in this repository were used for detecting accounting fraud using VAE-GAN and other models. Here is a breakdown of the files:
20220409-21_35_52_ep_3_decoder_model.pth - Decoder I trained that has the best results.
20220409-21_35_52_ep_3_discriminator_model.pth - Discriminator I trained that has the best resu... | {} | kmasiak/FraudDetection | null | [
"region:us"
] | null | 2022-04-06T22:36:56+00:00 | [] | [] | TAGS
#region-us
| The files in this repository were used for detecting accounting fraud using VAE-GAN and other models. Here is a breakdown of the files:
20220409-21_35_52_ep_3_decoder_model.pth - Decoder I trained that has the best results.
20220409-21_35_52_ep_3_discriminator_model.pth - Discriminator I trained that has the best resu... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | lilapapazian/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-06T23:23:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
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