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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-hubert-ft
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft](https://hug... | {"tags": ["automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer"], "model-index": [{"name": "aradia-ctc-hubert-ft", "results": []}]} | abdusah/aradia-ctc-hubert-ft | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T07:14:31+00:00 | [] | [] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_300hrs #generated_from_trainer #endpoints_compatible #region-us
| aradia-ctc-hubert-ft
====================
This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft on the ABDUSAHMBZUAI/ARABIC\_SPEECH\_MASSIVE\_300HRS - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8536
* Wer: 0.3737
Model description
-------... | [
"### 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_300hrs #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
null | null | So i made this model because i had nothing to do. it's gpt 2 124m finetuned to a bunch of italian recipes.
I made it using aitextgen, so you can use that to play with the model easily. | {"license": "gpl-3.0"} | Blazeolmo/GPT-RO | null | [
"license:gpl-3.0",
"region:us"
] | null | 2022-03-31T07:19:30+00:00 | [] | [] | TAGS
#license-gpl-3.0 #region-us
| So i made this model because i had nothing to do. it's gpt 2 124m finetuned to a bunch of italian recipes.
I made it using aitextgen, so you can use that to play with the model easily. | [] | [
"TAGS\n#license-gpl-3.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_random_low_pass
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_random_low_pass", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_random_low_pass | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T07:21:35+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_random\_low\_pass
==================================================
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: 1.3227
* Wer: 0.7288
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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. -->
# 2nd-wav2vec2-l-xls-r-300m-turkish-test
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": "2nd-wav2vec2-l-xls-r-300m-turkish-test", "results": []}]} | Khalsuu/2nd-wav2vec2-l-xls-r-300m-turkish-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T07:45:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| 2nd-wav2vec2-l-xls-r-300m-turkish-test
======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6019
* Wer: 0.4444
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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: 0.001\n* tr... |
null | null |
**Fake News Classifier**: Text classification model to detect fake news articles!
**Dataset**: [Kaggle Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
| {"license": "gpl-3.0"} | sndsabin/fake-news-classifier | null | [
"license:gpl-3.0",
"region:us"
] | null | 2022-03-31T07:53:49+00:00 | [] | [] | TAGS
#license-gpl-3.0 #region-us
|
Fake News Classifier: Text classification model to detect fake news articles!
Dataset: Kaggle Fake and real news dataset
| [] | [
"TAGS\n#license-gpl-3.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | YiTian/wav2vec2-common_voice-tr-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T08:39:08+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9841
* Wer: 0.9999
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #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\\... |
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-finetuned-sentiment-analysis
This model is a fine-tuned version of [cardiffnlp/bertweet-base-sentiment](https://hu... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bertweet-base-finetuned-sentiment-analysis", "results": []}]} | rahulacj/bertweet-base-finetuned-sentiment-analysis | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T08:42:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-finetuned-sentiment-analysis
==========================================
This model is a fine-tuned version of cardiffnlp/bertweet-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8458
* Accuracy: 0.6426
* F1: 0.6397
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en
This model is a fine-tuned version of [Helsinki-NLP/opu... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en", "results": []}]} | JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T09:16:30+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| JustAdvanceTechonology/medical\_research\_dataset\_marian-finetuned-kde4-fr-to-en
=================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 17733, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text2text-generation | transformers |
# T5-mini-nl8 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The Hug... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-mini-nl8-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"finnish",
"t5x",
"seq2seq",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-03-31T09:43:36+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| T5-mini-nl8 for Finnish
=======================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tu... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\n... |
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": []}]} | Neulvo/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T09:54:31+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 | from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("MMG/xlm-roberta-base-sa-spanish")
model = AutoModelForSequenceClassification.from_pretrained("MMG/xlm-roberta-base-sa-spanish")
pipe = pipeline("sentiment-analysis", model=model, tokenizer=... | {"language": ["es"], "pipeline_tag": "text-classification"} | MMG/xlm-roberta-base-sa-spanish | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T10:08:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #autotrain_compatible #endpoints_compatible #region-us
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("MMG/xlm-roberta-base-sa-spanish")
model = AutoModelForSequenceClassification.from_pretrained("MMG/xlm-roberta-base-sa-spanish")
pipe = pipeline("sentiment-analysis", model=model, tokenizer=... | [] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #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/1427292844612595720/RC1Y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/youtube/1648735587597/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/youtube | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T13:05:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
YouTube
@youtube
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# distilroberta-base-finetuned-fake-news-english
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [fake-and-real news](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset.
It achieves the following results on the evaluation se... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Wisconsin has not counted more votes than it has registered voters. This tweet is comparing the vote count from 2020 with the number of registered voters from 2018. ... | jaygala24/distilroberta-base-finetuned-fake-news-english | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T13:18:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-fake-news-english
==============================================
This model is a fine-tuned version of distilroberta-base on the fake-and-real news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0020
* Accuracy: 0.9997
* F1: 0.9997
* Precision: 0.9994
* Reca... | [
"### 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* 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 #roberta #text-classification #generated_from_trainer #en #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\\_... |
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/2844974270/7bb6450b90b65... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/timdingmanlive/1648736999131/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/timdingmanlive | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T13:26:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tim Dingman
@timdingmanlive
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | gdwangh/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T13:34:17+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: 0.6532
* Matthews Correlation: 0.5198
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-data2vec-ft
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft](https:/... | {"tags": ["automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer"], "model-index": [{"name": "aradia-ctc-data2vec-ft", "results": []}]} | abdusah/aradia-ctc-data2vec-ft | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T13:34:56+00:00 | [] | [] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_300hrs #generated_from_trainer #endpoints_compatible #region-us
| aradia-ctc-data2vec-ft
======================
This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft on the ABDUSAHMBZUAI/ARABIC\_SPEECH\_MASSIVE\_300HRS - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0464
* Wer: 1.0
Model description
----... | [
"### 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 #data2vec-audio #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_300hrs #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\... |
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. -->
# hausa-4-ha-wa2vec-data-aug-xls-r-300m
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | Tiamz/hausa-4-ha-wa2vec-data-aug-xls-r-300m | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-31T13:47:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| hausa-4-ha-wa2vec-data-aug-xls-r-300m
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3071
* Wer: 0.3304
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 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 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batc... |
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. -->
# xlm-roberta-base-amazon-en-es-fr-mlm
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["Yaxin/amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-base-amazon-en-es-fr-mlm", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "Yaxin/amazon_reviews_multi", "... | Yaxin/xlm-roberta-base-amazon-en-es-fr-mlm | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"dataset:Yaxin/amazon_reviews_multi",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T13:56:00+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #dataset-Yaxin/amazon_reviews_multi #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-base-amazon-en-es-fr-mlm
This model is a fine-tuned version of xlm-roberta-base on the Yaxin/amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3936
- Accuracy: 0.6951
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# xlm-roberta-base-amazon-en-es-fr-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the Yaxin/amazon_reviews_multi dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3936\n- Accuracy: 0.6951",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #dataset-Yaxin/amazon_reviews_multi #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-base-amazon-en-es-fr-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the Yaxin/ama... |
image-classification | transformers |
# Test-Model
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/huggingpi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Nonem100/Test-Model | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T14:19:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Test-Model
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
#### cotton candy
!cotton candy
#### hamburger
!hamburger
#### hot dog
!hot dog
#### nachos
!nachos
#### po... | [
"# Test-Model\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",
"#### cotton candy\n\n!cotton candy",
"#### hamburger\n\n!hamburger",
"#### hot dog\n\n!hot dog... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Test-Model\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wi... |
null | null | # UpsideDownClassifier
This classifier was trained using the [auto-cats-and-dogs](https://huggingface.co/datasets/nateraw/auto-cats-and-dogs) dataset. It was trained over 5 epochs using a pretrained resent18.
The configuration for the model was
```
config = {
"batch_size": 64,
"num_epochs": 5,
"lr": 0.0... | {} | israfelsr/UpsideDownClassifier | null | [
"region:us"
] | null | 2022-03-31T14:41:33+00:00 | [] | [] | TAGS
#region-us
| # UpsideDownClassifier
This classifier was trained using the auto-cats-and-dogs dataset. It was trained over 5 epochs using a pretrained resent18.
The configuration for the model was
## Traning Plots
We can see in the figures below the training plots for accuracy and the loss in both, training and validation set... | [
"# UpsideDownClassifier\n\nThis classifier was trained using the auto-cats-and-dogs dataset. It was trained over 5 epochs using a pretrained resent18. \n\nThe configuration for the model was",
"## Traning Plots\n\nWe can see in the figures below the training plots for accuracy and the loss in both, training and v... | [
"TAGS\n#region-us \n",
"# UpsideDownClassifier\n\nThis classifier was trained using the auto-cats-and-dogs dataset. It was trained over 5 epochs using a pretrained resent18. \n\nThe configuration for the model was",
"## Traning Plots\n\nWe can see in the figures below the training plots for accuracy and the los... |
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... | blacktree/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T14:48:48+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: 0.4883
* Matthews Correlation: 0.5286
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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-finetuned-fakenews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-finetuned-fakenews", "results": []}]} | Tahsin-Mayeesha/distilbert-finetuned-fakenews | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T14:58:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-finetuned-fakenews
=============================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0049
* Accuracy: 0.9995
* F1: 0.9995
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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 | # Fake and real news classification task
Model : [DistilRoBERTa base model](https://huggingface.co/distilroberta-base)
Dataset : [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
| {"license": "mit"} | israel/fake-news-classification | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T15:35:48+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Fake and real news classification task
Model : DistilRoBERTa base model
Dataset : Fake and real news dataset
| [
"# Fake and real news classification task \r\n\r\nModel : DistilRoBERTa base model\r\n\r\nDataset : Fake and real news dataset"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fake and real news classification task \r\n\r\nModel : DistilRoBERTa base model\r\n\r\nDataset : Fake and real news dataset"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_asr_swbd
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_asr_swbd", "results": []}]} | itaihay/wav2vec_asr_swbd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T15:52:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_asr\_swbd
==================
This model is a fine-tuned version of facebook/wav2vec2-large-robust-ft-swbd-300h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3052
* Wer: 0.5302
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0004\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 80\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"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: 0.0004\n* train\\_batch\\_size: 4... |
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/1485398297984389121/DmUf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/stillconor/1648748939988/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/stillconor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T15:59:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
conor
@stillconor
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 |
# orbgan
lightweight GAN trained on my glid-3 orbs (https://huggingface.co/datasets/johnowhitaker/glid3_orbs) for demo I'm working on.
Training notebook: https://colab.research.google.com/drive/16o1TdrxnQ54Msbr813XfPVsnEt2QTRAa?usp=sharing
Inference notebook: https://colab.research.google.com/drive/1e7dR2dptM8F1xhR... | {"language": "en", "license": "apache-2.0", "tags": ["lightweightgan"], "datasets": ["glid3_orbs"]} | johnowhitaker/orbgan_e1 | null | [
"pytorch",
"lightweightgan",
"en",
"dataset:glid3_orbs",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-31T16:14:36+00:00 | [] | [
"en"
] | TAGS
#pytorch #lightweightgan #en #dataset-glid3_orbs #license-apache-2.0 #has_space #region-us
|
# orbgan
lightweight GAN trained on my glid-3 orbs (URL for demo I'm working on.
Training notebook: URL
Inference notebook: URL
The lightwightgan code has an assert requiring a GPU. For inference on the CPU we ned to re-define the Generator class and some other functions - see minimal example here: URL . This appr... | [
"# orbgan\n\nlightweight GAN trained on my glid-3 orbs (URL for demo I'm working on.\n\nTraining notebook: URL\n\nInference notebook: URL\n\nThe lightwightgan code has an assert requiring a GPU. For inference on the CPU we ned to re-define the Generator class and some other functions - see minimal example here: URL... | [
"TAGS\n#pytorch #lightweightgan #en #dataset-glid3_orbs #license-apache-2.0 #has_space #region-us \n",
"# orbgan\n\nlightweight GAN trained on my glid-3 orbs (URL for demo I'm working on.\n\nTraining notebook: URL\n\nInference notebook: URL\n\nThe lightwightgan code has an assert requiring a GPU. For inference on... |
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_hindi_asr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_hindi_asr", "results": []}]} | deepspeechvision/wav2vec2_hindi_asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-31T16:22:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# wav2vec2_hindi_asr
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training ... | [
"# wav2vec2_hindi_asr\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# wav2vec2_hindi_asr\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice data... |
null | null | # DOSMA models
These models are those that are made publicly available in the [DOSMA](https://github.com/ad12/DOSMA).
More information on these models can be found in the [documentation](https://dosma.readthedocs.io/en/latest/models.html).
## Citation
If you use any models, please cite any reference for the model in a... | {"language": "en", "tags": ["mri", "knee", "segmentation"]} | arjundd/dosma-models | null | [
"mri",
"knee",
"segmentation",
"en",
"region:us"
] | null | 2022-03-31T17:30:03+00:00 | [] | [
"en"
] | TAGS
#mri #knee #segmentation #en #region-us
| # DOSMA models
These models are those that are made publicly available in the DOSMA.
More information on these models can be found in the documentation.
If you use any models, please cite any reference for the model in addition to the DOSMA reference below:
| [
"# DOSMA models\nThese models are those that are made publicly available in the DOSMA.\nMore information on these models can be found in the documentation.\n\nIf you use any models, please cite any reference for the model in addition to the DOSMA reference below:"
] | [
"TAGS\n#mri #knee #segmentation #en #region-us \n",
"# DOSMA models\nThese models are those that are made publicly available in the DOSMA.\nMore information on these models can be found in the documentation.\n\nIf you use any models, please cite any reference for the model in addition to the DOSMA reference below... |
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... | JNK789/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 | 2022-03-31T17:53:29+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: 0.1712
* Accuracy: 0.9305
* F1: 0.9308
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: 3",
"### 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... |
null | null | Model files attached | {} | snehakhandelwal/fatima_fellowship_coding_challenge | null | [
"region:us"
] | null | 2022-03-31T18:02:07+00:00 | [] | [] | TAGS
#region-us
| Model files attached | [] | [
"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-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... | novarac23/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 | 2022-03-31T18:05:57+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: 0.2234
* Accuracy: 0.925
* F1: 0.9252
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... |
null | null | # StyleGAN-XL
- https://arxiv.org/abs/2202.00273
- https://github.com/autonomousvision/stylegan-xl
- weights
- https://s3.eu-central-1.amazonaws.com/avg-projects/stylegan_xl/models/imagenet16.pkl
- https://s3.eu-central-1.amazonaws.com/avg-projects/stylegan_xl/models/imagenet32.pkl
- https://s3.eu-central... | {} | public-data/StyleGAN-XL | null | [
"arxiv:2202.00273",
"has_space",
"region:us"
] | null | 2022-03-31T18:13:40+00:00 | [
"2202.00273"
] | [] | TAGS
#arxiv-2202.00273 #has_space #region-us
| # StyleGAN-XL
- URL
- URL
- weights
- URL
- URL
- URL
- URL
- URL
- URL
- URL
| [
"# StyleGAN-XL\n\n- URL\n- URL\n\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2202.00273 #has_space #region-us \n",
"# StyleGAN-XL\n\n- URL\n- URL\n\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] |
image-classification | transformers |
# rock-challenge-ViT-two-by-two
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.co... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | dimbyTa/rock-challenge-ViT-two-by-two | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T18:44:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rock-challenge-ViT-two-by-two
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
#### fines
!fines
#### large
!large
#### medium
!medium
#### pellets
!pellets | [
"# rock-challenge-ViT-two-by-two\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",
"#### fines\n\n!fines",
"#### large\n\n!large",
"#### medium\n\n!medium",
... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rock-challenge-ViT-two-by-two\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nR... |
null | transformers |
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used towards research grants, it should be applicable to any type ... | {"license": "apache-2.0"} | osanseviero/test_model_bertmesh | null | [
"transformers",
"pytorch",
"bert",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T18:47:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #custom_code #license-apache-2.0 #endpoints_compatible #region-us
|
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was ... | [
"# WellcomeBertMesh\r\n\r\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain ... | [
"TAGS\n#transformers #pytorch #bert #custom_code #license-apache-2.0 #endpoints_compatible #region-us \n",
"# WellcomeBertMesh\r\n\r\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to... |
image-classification | null | ## Detecting the Orientation of CelebA pictures using Deep Learning
This model has been trained on a modified version of the CelebA-faces dataset, which was made from flipping 20,000 images upside down and keeping 20,000 images intact.<br>
The model relies on Resnet-18 as a backbone and is connected to one output node... | {"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["image-classification", "pytorch"], "datasets": ["nielsr/CelebA-faces"], "model-index": [{"name": "celebA_orientation_detection_model", "results": [{"task": {"type": "image_classification", "name": "Image Classification"}, "dataset": {"name": "CelebA-faces", "typ... | anisdismail/celebA-orientation-detection | null | [
"image-classification",
"pytorch",
"en",
"dataset:nielsr/CelebA-faces",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2022-03-31T18:48:26+00:00 | [] | [
"en"
] | TAGS
#image-classification #pytorch #en #dataset-nielsr/CelebA-faces #license-cc-by-nc-4.0 #model-index #region-us
| ## Detecting the Orientation of CelebA pictures using Deep Learning
This model has been trained on a modified version of the CelebA-faces dataset, which was made from flipping 20,000 images upside down and keeping 20,000 images intact.<br>
The model relies on Resnet-18 as a backbone and is connected to one output node... | [
"## Detecting the Orientation of CelebA pictures using Deep Learning\nThis model has been trained on a modified version of the CelebA-faces dataset, which was made from flipping 20,000 images upside down and keeping 20,000 images intact.<br> \nThe model relies on Resnet-18 as a backbone and is connected to one outp... | [
"TAGS\n#image-classification #pytorch #en #dataset-nielsr/CelebA-faces #license-cc-by-nc-4.0 #model-index #region-us \n",
"## Detecting the Orientation of CelebA pictures using Deep Learning\nThis model has been trained on a modified version of the CelebA-faces dataset, which was made from flipping 20,000 images ... |
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... | magitz/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 | 2022-03-31T19:41:54+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: 0.2235
* Accuracy: 0.9265
* F1: 0.9268
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... |
null | null | Preprocessing before feeding to model
```
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device='cuda')
...
embeddings = model.encode([text])
return embeddings[0]
``` | {} | ghees/FatimeFellowship | null | [
"region:us"
] | null | 2022-03-31T19:45:21+00:00 | [] | [] | TAGS
#region-us
| Preprocessing before feeding to model
| [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# State Social Operator Detector
## Overview
State-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence [elections... | {"language": ["en"], "license": "apache-2.0", "tags": ["classification"], "widget": [{"text": "Zimbabwe has all the Brilliant Minds to become the Next Dubai of Africa No wonder why is so confide | Invest Surplus yako iye into Healthcare that will save lives amp creat real Jobs in Healthcare Sector | To the African Dias... | lingwave-admin/state-op-detector | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T20:52:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# State Social Operator Detector
## Overview
State-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence elections ... | [
"# State Social Operator Detector",
"## Overview\nState-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence e... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# State Social Operator Detector",
"## Overview\nState-funded social media operators are a hard-to-detect but significant threat to any democracy wi... |
text-generation | transformers |
# Run 3 :)
# An exceedingly special thanks to Lynn Zheng for the tutorial on how to do this. | {"tags": ["conversational"]} | AAAA-4/DialoGPT-small-player_03 | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T21:09:35+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Run 3 :)
# An exceedingly special thanks to Lynn Zheng for the tutorial on how to do this. | [
"# Run 3 :)",
"# An exceedingly special thanks to Lynn Zheng for the tutorial on how to do this."
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Run 3 :)",
"# An exceedingly special thanks to Lynn Zheng for the tutorial on how to do this."
] |
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-finetuned-mnli-rte-wnli-10
This model is a fine-tuned version of [yy642/bert-base-uncased-finetuned-mnli-rte-w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-mnli-rte-wnli-10", "results": []}]} | yy642/bert-base-uncased-finetuned-mnli-rte-wnli-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T22:51:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-mnli-rte-wnli-10
============================================
This model is a fine-tuned version of yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5876
* Accuracy: 0.9206
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: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_batch\\... |
text-generation | transformers |
# Harry Potter Model | {"tags": ["conversational"]} | Teyronebigdick/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T22:53:01+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter Model | [
"# Harry Potter Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter Model"
] |
null | null | ## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle
## The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
num_epochs: 2
| {} | ahmedzaky91/Fatima-Fake_news_calssifier | null | [
"region:us"
] | null | 2022-03-31T23:00:39+00:00 | [] | [] | TAGS
#region-us
| ## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle
## The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
num_epochs: 2
| [
"## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle",
"## The following hyperparameters were used during training:\n\n learning_rate: 5e-05\n train_batch_size: 8\n num_epochs: 2"
] | [
"TAGS\n#region-us \n",
"## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle",
"## The following hyperparameters were used during training:\n\n learning_rate: 5e-05\n train_batch_size: 8\n num_epochs: 2"
] |
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. -->
# output
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "output", "results": []}]} | tonyalves/output | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"pt",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T23:34:39+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| output
======
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1505
* Wer: 0.1352
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pt #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* ... |
null | keras | Glove Fake news Identification
This model is a fine-tuned of glove pre-trained model
In near future to be a fine-tuned of BERT and to make multiple comparisons based on updated tuning accuracy.
---
thumbnail: "https://miro.medium.com/max/600/0*a6XSwHsfvz_oWSSJ.jpg"
tags:
- python
- tensorflow
- Keras
- KerasT... | {} | jszeina/glove-fakenews-classifier | null | [
"keras",
"region:us"
] | null | 2022-03-31T23:50:54+00:00 | [] | [] | TAGS
#keras #region-us
| Glove Fake news Identification
This model is a fine-tuned of glove pre-trained model
In near future to be a fine-tuned of BERT and to make multiple comparisons based on updated tuning accuracy.
---
thumbnail: "URL
tags:
- python
- tensorflow
- Keras
- KerasTuner
- glove
- LSTM
datasets:
- URL
metrics:
- a... | [] | [
"TAGS\n#keras #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. -->
# Fake-news-detection-bert-based-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fake-news-detection-bert-based-uncased", "results": []}]} | Aymene/Fake-news-detection-bert-based-uncased | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T00:33:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Fake-news-detection-bert-based-uncased
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
"# Fake-news-detection-bert-based-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fake-news-detection-bert-based-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\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. -->
# results
This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "results", "results": []}]} | FrankCorrigan/results | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T00:41:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of linydub/bart-large-samsum on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0158
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information ... | [
"### 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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-samsum #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\\_bat... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | dchung117/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T00:51:41+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
token-classification | transformers | # Figured out labels | {} | blckwdw61/sysformbatches2acs | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T01:03:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # Figured out labels | [
"# Figured out labels"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Figured out labels"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit_beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "args": "default"}, "metrics": [{"type"... | johnnydevriese/vit_beans | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T01:16:27+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit_beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1176
- Accuracy: 0.9699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# vit_beans\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1176\n- Accuracy: 0.9699",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit_beans\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.\nIt achieves th... |
text-generation | transformers |
# my chatbot model | {"tags": ["conversational"]} | Sammith/DialoGPT-small-miachael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T03:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# my chatbot model | [
"# my chatbot model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# my chatbot 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-uncased-multi-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-uncased-multi-128", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | xxr/bert-base-uncased-multi-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T04:36:26+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-multi-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: 2.7101
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: 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: 16",
"### Trainin... | [
"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: 8\n* eval\\_batc... |
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-xlsr-53_toy_train_data_random_low_pass
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_random_low_pass", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_random_low_pass | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T05:18:47+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_random\_low\_pass
===========================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6572
* Wer: 0.4973
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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. -->
# discharge-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "discharge-classifier", "results": []}]} | joniponi/discharge-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T05:24:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| discharge-classifier
====================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2473
* Accuracy: 0.9172
* F1: 0.9169
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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... |
null | null | Just a placeholder for a future model | {} | pere/pk-nb-t5x | null | [
"region:us"
] | null | 2022-04-01T05:33:23+00:00 | [] | [] | TAGS
#region-us
| Just a placeholder for a future model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [z5ying/distilgpt2-finetuned-wikitext2](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | z5ying/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T06:10:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of z5ying/distilgpt2-finetuned-wikitext2 on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training ... | [
"### 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Nxtxn01/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T06:15:12+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"
] |
automatic-speech-recognition | transformers | The model is a reproduction of the baseline trained with Wav2vec2-small on PSST
pssteval INFO: ASR metrics for split `valid` FER: 10.4% PER: 23.1% | {} | birgermoell/psst-base-rep | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T06:58:20+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| The model is a reproduction of the baseline trained with Wav2vec2-small on PSST
pssteval INFO: ASR metrics for split 'valid' FER: 10.4% PER: 23.1% | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# Hungarian Named Entity Recognition Model with huBERT
For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor)
- NE categories are: PER, LOC, MISC, ORG
... | {"language": ["hu"], "license": "apache-2.0", "tags": ["token-classification"], "metrics": ["f1"], "widget": [{"text": "A Kov\u00e1csn\u00e9 Nagy Erzs\u00e9bet nagyon j\u00f3l \u00e9rzi mag\u00e1t a Noki\u00e1n\u00e1l, azonban a N\u00e9metorsz\u00e1gb\u00f3l \u00e9rkezett Kov\u00e1cs P\u00e9ter nehezen boldogul a beill... | NYTK/named-entity-recognition-nerkor-hubert-hungarian | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T07:37:36+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bert #token-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Hungarian Named Entity Recognition Model with huBERT
For further models, scripts and details, see our demo site.
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on NYTK-NerKor
- NE categories are: PER, LOC, MISC, ORG
## Limitations
- max_seq_length = 128
## Results
F-score: 90.18%
## U... | [
"# Hungarian Named Entity Recognition Model with huBERT\n\nFor further models, scripts and details, see our demo site.\n\n - Pretrained model used: SZTAKI-HLT/hubert-base-cc\n - Finetuned on NYTK-NerKor\n - NE categories are: PER, LOC, MISC, ORG",
"## Limitations\n\n- max_seq_length = 128",
"## Results\n\nF-... | [
"TAGS\n#transformers #pytorch #bert #token-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Hungarian Named Entity Recognition Model with huBERT\n\nFor further models, scripts and details, see our demo site.\n\n - Pretrained model used: SZTAKI-HLT/hubert-base-... |
image-classification | transformers |
# llama-or-potato
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/hugg... | {"tags": ["image-classification", "pytorch", "huggingpics", "llama-leaderboard"], "metrics": ["accuracy"]} | osanseviero/llama-or-potato | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-01T08:05:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# llama-or-potato
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
#### llamas
!llamas
#### potato
!potato | [
"# llama-or-potato\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",
"#### llamas\n\n!llamas",
"#### potato\n\n!potato"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# llama-or-potato\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Go... |
image-classification | transformers |
# llama-alpaca-snake
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/h... | {"tags": ["image-classification", "pytorch", "huggingpics", "llama-leaderboard"], "metrics": ["accuracy"]} | osanseviero/llama-alpaca-snake | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T08:20:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# llama-alpaca-snake
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
#### alpaca
!alpaca
#### llamas
!llamas
#### snake
!snake | [
"# llama-alpaca-snake\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",
"#### alpaca\n\n!alpaca",
"#### llamas\n\n!llamas",
"#### snake\n\n!snake"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# llama-alpaca-snake\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Col... |
image-classification | transformers |
# llama-horse-zebra
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/hu... | {"tags": ["image-classification", "pytorch", "huggingpics", "llama-leaderboard"], "metrics": ["accuracy"], "inference": false} | osanseviero/llama-horse-zebra | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"model-index",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-04-01T08:42:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #has_space #region-us
|
# llama-horse-zebra
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
#### horse
!horse
#### llama
!llama
#### zebra
!zebra | [
"# llama-horse-zebra\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",
"#### horse\n\n!horse",
"#### llama\n\n!llama",
"#### zebra\n\n!zebra"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #model-index #autotrain_compatible #has_space #region-us \n",
"# llama-horse-zebra\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRepor... |
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. -->
# sbert_large_nlu_ru-finetuned-squad-full
This model is a fine-tuned version of [ruselkomp/sbert_large_nlu_ru-finetuned-squad-full... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "sbert_large_nlu_ru-finetuned-squad-full", "results": []}]} | Timur1984/sbert_large_nlu_ru-finetuned-squad-full | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T09:36:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| sbert\_large\_nlu\_ru-finetuned-squad-full
==========================================
This model is a fine-tuned version of ruselkomp/sbert\_large\_nlu\_ru-finetuned-squad-full on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6119
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 6\n* eval\\_batch\\_size: 6\n* seed:... |
token-classification | 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. -->
# ner-dummy-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ner-dummy-model", "results": []}]} | avialfont/ner-dummy-model | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T09:59:27+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ner-dummy-model
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# ner-dummy-model\n\nThis model is a fine-tuned version of bert-base-cased 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",
"## Training and evaluation data\n\nMor... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ner-dummy-model\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluatio... |
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-irish-colab_test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-irish-colab_test", "results": []}]} | jfealko/wav2vec2-large-xls-r-300m-irish-colab_test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T10:29:55+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-irish-colab\_test
===========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7839
* Wer: 0.6220
Model description
-----------------
More ... | [
"### 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... |
null | null | # poetry-generation-nextline-mbart-ws-fi-single
* `nextline`: generates a poem line from previous line(s)
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `ws`: trained on Wikisource data
* `fi`: Finnish language
* `single`: uses only last poem line as input... | {} | bmichele/poetry-generation-nextline-mbart-ws-fi-single | null | [
"pytorch",
"region:us"
] | null | 2022-04-01T10:35:07+00:00 | [] | [] | TAGS
#pytorch #region-us
| # poetry-generation-nextline-mbart-ws-fi-single
* 'nextline': generates a poem line from previous line(s)
* 'mbart': base model is facebook/mbart-large-cc25
* 'ws': trained on Wikisource data
* 'fi': Finnish language
* 'single': uses only last poem line as input for generation | [
"# poetry-generation-nextline-mbart-ws-fi-single\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'ws': trained on Wikisource data\n * 'fi': Finnish language\n * 'single': uses only last poem line as input for generation"
] | [
"TAGS\n#pytorch #region-us \n",
"# poetry-generation-nextline-mbart-ws-fi-single\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'ws': trained on Wikisource data\n * 'fi': Finnish language\n * 'single': uses only last poem line as input for ge... |
null | null |
# Up-Down Classification
This repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task). | {"language": "en", "tags": ["classification"], "datasets": ["cifar10-custom"], "metrics": ["accuracy"]} | bharatR/up_down | null | [
"classification",
"en",
"dataset:cifar10-custom",
"region:us"
] | null | 2022-04-01T11:19:00+00:00 | [] | [
"en"
] | TAGS
#classification #en #dataset-cifar10-custom #region-us
|
# Up-Down Classification
This repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task). | [
"# Up-Down Classification\n\nThis repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task)."
] | [
"TAGS\n#classification #en #dataset-cifar10-custom #region-us \n",
"# Up-Down Classification\n\nThis repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task)."
] |
token-classification | spacy | ## Model description (NerIta)
**it_nerIta_trf** is a fine-tuned spacy model ready to be used for **Named Entity Recognition** on **Italian language** texts based on a pipeline composed by the **hseBert-it-cased** transformer.
It has been trained to recognize 18 types of entities: PER, NORP, ORG, GPE, LOC, DATE, MONEY, ... | {"language": ["it"], "license": "apache-2.0", "tags": ["spacy", "token-classification"], "widget": [{"text": "E' stato pubblicato il decreto legge recante \u201cdisposizioni urgenti per il superamento delle misure di contrasto alla diffusione dell'epidemia da COVID-19, in conseguenza della cessazione dello stato di eme... | bullmount/it_nerIta_trf | null | [
"spacy",
"token-classification",
"it",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-04-01T11:19:57+00:00 | [] | [
"it"
] | TAGS
#spacy #token-classification #it #license-apache-2.0 #model-index #region-us
| Model description (NerIta)
--------------------------
it\_nerIta\_trf is a fine-tuned spacy model ready to be used for Named Entity Recognition on Italian language texts based on a pipeline composed by the hseBert-it-cased transformer.
It has been trained to recognize 18 types of entities: PER, NORP, ORG, GPE, LOC, D... | [
"### Label Scheme\n\n\n\nView label scheme (18 labels)\nPredicts 18 tags:",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #it #license-apache-2.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (18 labels)\nPredicts 18 tags:",
"### Accuracy"
] |
token-classification | spacy | Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner
| Feature | Description |
| --- | --- |
| **Name** | `hu_core_news_trf` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `transformer`, `senter`, `ta... | {"language": ["hu"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | huspacy/hu_core_news_trf | null | [
"spacy",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-04-01T11:20:59+00:00 | [] | [
"hu"
] | TAGS
#spacy #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us
| Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner
### Label Scheme
View label scheme (1217 labels for 4 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1217 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1217 labels for 4 components)",
"### Accuracy"
] |
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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | blacktree/distilbert-base-uncased-finetuned-sst2 | 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 | 2022-04-01T11:29:26+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-sst2
======================================
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: 0.7027
* Accuracy: 0.5092
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.01\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: 5",
"### Trainin... | [
"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... |
null | null | TASK 1 of Faltima Fellowship- UpsideDown detector | {} | Suman123/upside-down-detector | null | [
"region:us"
] | null | 2022-04-01T11:56:45+00:00 | [] | [] | TAGS
#region-us
| TASK 1 of Faltima Fellowship- UpsideDown detector | [] | [
"TAGS\n#region-us \n"
] |
null | null | TODO: This is still a demo model, the file does not match with the model card!!!
# poetry-generation-firstline-mbart-ws-fi-sorted
* `nextline`: generates the first poem line from keywords
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `ws`: trained on Wikis... | {} | bmichele/poetry-generation-firstline-mbart-ws-fi-sorted | null | [
"pytorch",
"region:us"
] | null | 2022-04-01T11:58:00+00:00 | [] | [] | TAGS
#pytorch #region-us
| TODO: This is still a demo model, the file does not match with the model card!!!
# poetry-generation-firstline-mbart-ws-fi-sorted
* 'nextline': generates the first poem line from keywords
* 'mbart': base model is facebook/mbart-large-cc25
* 'ws': trained on Wikisource data
* 'fi': Finnish language
* 'sorted': th... | [
"# poetry-generation-firstline-mbart-ws-fi-sorted\n\n * 'nextline': generates the first poem line from keywords\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'ws': trained on Wikisource data\n * 'fi': Finnish language\n * 'sorted': the order of input keywords matter when generating candidates"
] | [
"TAGS\n#pytorch #region-us \n",
"# poetry-generation-firstline-mbart-ws-fi-sorted\n\n * 'nextline': generates the first poem line from keywords\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'ws': trained on Wikisource data\n * 'fi': Finnish language\n * 'sorted': the order of input keywords matter when... |
text-generation | transformers |
# HowTo QA with GPT-2 base
GPT-2 English language model fine-tuned with ±2.000 entries from WikiHow.
You can try it here: https://how-to-generator.herokuapp.com/
Input prompt should follow the following format:
`\n<|startoftext|>[WP] How to {text} \n[RESPONSE]`
Example:
`\n<|startoftext|>[WP] How to create a un... | {"language": "en", "license": "mit"} | soyasis/gpt2-finetuned-how-to-qa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T12:10:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# HowTo QA with GPT-2 base
GPT-2 English language model fine-tuned with ±2.000 entries from WikiHow.
You can try it here: URL
Input prompt should follow the following format:
'\n<|startoftext|>[WP] How to {text} \n[RESPONSE]'
Example:
'\n<|startoftext|>[WP] How to create a universe \n[RESPONSE]'
| [
"# HowTo QA with GPT-2 base\n\nGPT-2 English language model fine-tuned with ±2.000 entries from WikiHow.\nYou can try it here: URL\n\nInput prompt should follow the following format: \n'\\n<|startoftext|>[WP] How to {text} \\n[RESPONSE]'\n\nExample: \n'\\n<|startoftext|>[WP] How to create a universe \\n[RESPONSE]... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# HowTo QA with GPT-2 base\n\nGPT-2 English language model fine-tuned with ±2.000 entries from WikiHow.\nYou can try it here: URL\n\nInput prompt should fol... |
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. -->
# indobert-classification
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchm... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "indobert-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metr... | afbudiman/indobert-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T12:17:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| indobert-classification
=======================
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3707
* Accuracy: 0.9397
* F1: 0.9393
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-indonlu #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: 2e-05\n* train... |
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. -->
# mbart-large-cc25-finetuned-hi-to-en-v1
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-cc25-finetuned-hi-to-en-v1", "results": []}]} | rahulacj/mbart-large-cc25-finetuned-hi-to-en-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T12:41:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-cc25-finetuned-hi-to-en-v1
======================================
This model is a fine-tuned version of facebook/mbart-large-cc25 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4978
* Bleu: 33.3366
* Gen Len: 22.7806
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #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: 1\n* eval\\... |
text-classification | spacy |
## es_tweets_laboral ##
Modelo creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21
| {"language": "es", "tags": ["spacy", "text-classification"], "widget": [{"text": "todos merecemos un salario justo"}]} | hackathon-pln-es/es_tweets_laboral | null | [
"spacy",
"text-classification",
"es",
"region:us"
] | null | 2022-04-01T12:48:09+00:00 | [] | [
"es"
] | TAGS
#spacy #text-classification #es #region-us
|
## es_tweets_laboral ##
Modelo creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21
| [
"## es_tweets_laboral ##\r\n\r\nModelo creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21"
] | [
"TAGS\n#spacy #text-classification #es #region-us \n",
"## es_tweets_laboral ##\r\n\r\nModelo creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21"
] |
null | null | # Fatima Fellowship challenge | {} | Oxies/CartPole_v1_DQN_gym | null | [
"region:us"
] | null | 2022-04-01T13:17:58+00:00 | [] | [] | TAGS
#region-us
| # Fatima Fellowship challenge | [
"# Fatima Fellowship challenge"
] | [
"TAGS\n#region-us \n",
"# Fatima Fellowship challenge"
] |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | {} | ydshieh/bert-base-uncased-yelp-polarity | null | [
"transformers",
"tf",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T14:17:35+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 5e-05, and a maximum sequence length of 256. \nS... | [
"TAGS\n#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-t... |
question-answering | transformers |
This is a BERT base cased model trained on SQuAD v2 | {"license": "cc-by-4.0"} | ydshieh/bert-base-cased-squad2 | null | [
"transformers",
"tf",
"bert",
"question-answering",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T14:23:10+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #license-cc-by-4.0 #endpoints_compatible #region-us
|
This is a BERT base cased model trained on SQuAD v2 | [] | [
"TAGS\n#transformers #tf #bert #question-answering #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | maxhilsdorf/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T14:32:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2991
- eval_accuracy: 0.91
- eval_f1: 0.9083
- eval_runtime: 3.258
- eval_samples_per_second: 613.873
- eval_steps... | [
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2991\n- eval_accuracy: 0.91\n- eval_f1: 0.9083\n- eval_runtime: 3.258\n- eval_samples_per_second: 613.873\n-... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
fill-mask | transformers |
<span style="font-size:larger;">**Clinical-BigBird**</span> is a clinical knowledge enriched version of BigBird that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-BigBird consistently out-performs ClinicalBERT across 10 baseline dataset. Those downstr... | {"language": "en", "tags": ["BigBird", "clinical"]} | yikuan8/Clinical-BigBird | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"BigBird",
"clinical",
"en",
"arxiv:2201.11838",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T14:44:00+00:00 | [
"2201.11838"
] | [
"en"
] | TAGS
#transformers #pytorch #big_bird #fill-mask #BigBird #clinical #en #arxiv-2201.11838 #autotrain_compatible #endpoints_compatible #region-us
|
<span style="font-size:larger;">Clinical-BigBird</span> is a clinical knowledge enriched version of BigBird that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-BigBird consistently out-performs ClinicalBERT across 10 baseline dataset. Those downstream ... | [
"### Pre-training\nWe initialized Clinical-BigBird from the pre-trained weights of the base version of BigBird. The pre-training process was distributed in parallel to 6 32GB Tesla V100 GPUs. FP16 precision was enabled to accelerate training. We pre-trained Clinical-BigBird for 300,000 steps with batch size of 6×2.... | [
"TAGS\n#transformers #pytorch #big_bird #fill-mask #BigBird #clinical #en #arxiv-2201.11838 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Pre-training\nWe initialized Clinical-BigBird from the pre-trained weights of the base version of BigBird. The pre-training process was distributed in parall... |
text2text-generation | transformers |
# Model name
## Model description
This model mines the question-answer pairs from a given context in an end2end fashion. It takes a context as an input and generates a list of questions and answers as an output. It is based on a pre-trained `t5-small` model and uses a prompt enigneering technique to train.
#### H... | {"license": "mit", "tags": ["question-generation", "question-answer mining"], "datasets": ["squad"], "widget": [{"text": "context: The English name 'Normans' comes from the French words Normans/Normanz, plural of Normant, modern French normand, which is itself borrowed from Old Low Franconian Nortmann 'Northman' or dir... | mojians/E2E-QA-Mining | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"question-answer mining",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T15:03:34+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #question-answer mining #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model name
## Model description
This model mines the question-answer pairs from a given context in an end2end fashion. It takes a context as an input and generates a list of questions and answers as an output. It is based on a pre-trained 't5-small' model and uses a prompt enigneering technique to train.
#### H... | [
"# Model name",
"## Model description\n\nThis model mines the question-answer pairs from a given context in an end2end fashion. It takes a context as an input and generates a list of questions and answers as an output. It is based on a pre-trained 't5-small' model and uses a prompt enigneering technique to train... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #question-answer mining #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model name",
"## Model description\n\nThis model mines the question-answer pairs f... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# canine-c-finetuned-mrpc
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "canine-c-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metric... | vicl/canine-c-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T15:05:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-c-finetuned-mrpc
=======================
This model is a fine-tuned version of google/canine-c on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4066
* Accuracy: 0.8627
* F1: 0.9014
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-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-distilbert-fakenews-detection
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-distilbert-fakenews-detection", "results": []}]} | bitsanlp/distilbert-base-uncased-distilbert-fakenews-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T15:12:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilbert-fakenews-detection
=====================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
* F1: 1.0
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers | # BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) fine-tuned on [Google NaturalQuestions](https://ai.google.com/research/NaturalQuestions/) for **Question Generation** by treating long answer as input, and question as output.
## Details of BA... | {"license": "cc-by-4.0"} | McGill-NLP/bart-qg-nq-checkpoint | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T15:32:49+00:00 | [
"1910.13461"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| BART-base fine-tuned on NaturalQuestions for Question Generation
================================================================
BART Model fine-tuned on Google NaturalQuestions for Question Generation by treating long answer as input, and question as output.
Details of BART
---------------
The BART model was pr... | [
"# samples: 97650\nDataset: NaturalQuestions, Split: valid, # samples: 10850\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script can be found here\n\n\nModel in Action\n---------------\n\n\nIf you want to cite this model you can use this:\n\n\n\n> \n> Created by Devang Kulshreshtha\n> \n> \n> \... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# samples: 97650\nDataset: NaturalQuestions, Split: valid, # samples: 10850\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script can be fo... |
null | null | Fatima Fellowship Quick Coding Challenge (Pick 1):
- Deep Learning for Vision
| {} | asebaq/up_down_model | null | [
"region:us"
] | null | 2022-04-01T15:57:55+00:00 | [] | [] | TAGS
#region-us
| Fatima Fellowship Quick Coding Challenge (Pick 1):
- Deep Learning for Vision
| [] | [
"TAGS\n#region-us \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. -->
# poem-gen-spanish-t5-small-d2
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-spanish-t5-small-d2", "results": []}]} | DrishtiSharma/poem-gen-spanish-t5-small-d2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T16:08:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-spanish-t5-small-d2
============================
This model is a fine-tuned version of flax-community/spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9027
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-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: 0.000... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# canine-c-finetuned-cola
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "canine-c-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "... | vicl/canine-c-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T16:13:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-c-finetuned-cola
=======================
This model is a fine-tuned version of google/canine-c on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6246
* Matthews Correlation: 0.0990
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-classification | 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. -->
# juaner/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "juaner/distilbert-base-uncased-finetuned-cola", "results": []}]} | juaner/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T16:59:52+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| juaner/distilbert-base-uncased-finetuned-cola
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1909
* Validation Loss: 0.5553
* Train Matthews Correlation: 0.527... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'lear... |
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... | cj-mills/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 | 2022-04-01T17:58:12+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: 0.2205
* Accuracy: 0.936
* F1: 0.9361
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... |
automatic-speech-recognition | transformers | pssteval INFO: ASR metrics for split `valid` FER: 9.8% PER: 20.9% | {} | birgermoell/psst-libri960_big | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T18:05:31+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| pssteval INFO: ASR metrics for split 'valid' FER: 9.8% PER: 20.9% | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #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. -->
# canine-s-finetuned-stsb
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "canine-s-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "stsb"}, "metrics": [... | vicl/canine-s-finetuned-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T18:47:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-s-finetuned-stsb
=======================
This model is a fine-tuned version of google/canine-s on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7223
* Pearson: 0.8397
* Spearmanr: 0.8397
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-classification | transformers |
# Company Classifier
This fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either finance or biotech. The demo can be found on my profile under Spaces (https://huggingface.co/erikacardenas300).
I hope you enjoy it! | {"language": "en", "datasets": ["Crunchbase"]} | erikacardenas300/StartupClassifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Crunchbase",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T19:53:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-Crunchbase #autotrain_compatible #endpoints_compatible #region-us
|
# Company Classifier
This fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either finance or biotech. The demo can be found on my profile under Spaces (URL
I hope you enjoy it! | [
"# Company Classifier \nThis fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either finance or biotech. The demo can be found on my profile under Spaces (URL \n\nI hope you enjoy it!"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-Crunchbase #autotrain_compatible #endpoints_compatible #region-us \n",
"# Company Classifier \nThis fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either financ... |
text-classification | transformers | # CentraleSupelec - Natural language processing
# Practical session n°7
## Natural Language Inferencing (NLI):
(NLI) is a classical NLP (Natural Language Processing) problem that involves taking two sentences (the premise and the hypothesis ), and deciding how they are related (if the premise *entails* the hypo... | {} | youssefadarrab/TP_NLP_SNLI_Adarrab_Baziz_Malige | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-01T20:11:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| CentraleSupelec - Natural language processing
=============================================
Practical session n°7
=====================
Natural Language Inferencing (NLI):
-----------------------------------
(NLI) is a classical NLP (Natural Language Processing) problem that involves taking two sentences (the pre... | [
"### Stanford NLI (SNLI) corpus\n\n\nIn this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.\n\n\n\n```\nfrom datasets import load_dataset\nsnli = load_dataset(\"snli\")"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Stanford NLI (SNLI) corpus\n\n\nIn this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.\n\n\n\n```\nfrom datasets im... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 694821095
- CO2 Emissions (in grams): 2313.4037079026934
## Validation Metrics
- Loss: 3.0294156074523926
- Rouge1: 2.1467
- Rouge2: 0.0853
- RougeL: 2.1524
- RougeLsum: 2.1534
- Gen Len: 18.5603
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autotrain", "datasets": ["abd-1999/autotrain-data-bbc-news-summarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2313.4037079026934} | abd-1999/autotrain-bbc-news-summarization-694821095 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:abd-1999/autotrain-data-bbc-news-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T20:16:19+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-abd-1999/autotrain-data-bbc-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 694821095
- CO2 Emissions (in grams): 2313.4037079026934
## Validation Metrics
- Loss: 3.0294156074523926
- Rouge1: 2.1467
- Rouge2: 0.0853
- RougeL: 2.1524
- RougeLsum: 2.1534
- Gen Len: 18.5603
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 694821095\n- CO2 Emissions (in grams): 2313.4037079026934",
"## Validation Metrics\n\n- Loss: 3.0294156074523926\n- Rouge1: 2.1467\n- Rouge2: 0.0853\n- RougeL: 2.1524\n- RougeLsum: 2.1534\n- Gen Len: 18.5603",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-abd-1999/autotrain-data-bbc-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 69... |
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-wikihow_3epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikih... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "wiki... | Chikashi/t5-small-finetuned-wikihow_3epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T20:20:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch
==================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5163
* Rouge1: 25.5784
* Rouge2: 8.9929
* Rougel: 21.5345
* Rougelsum: 24.9382
* Gen Len: 18.384
Model des... | [
"### 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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
text-generation | transformers |
# Terry Davis DialoGPT Model | {"tags": ["conversational"]} | Teyronebigdick/DialoGPT-small-terrydavis | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T20:30:37+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Terry Davis DialoGPT Model | [
"# Terry Davis DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Terry Davis DialoGPT Model"
] |
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/1191821996759404547/HY5C... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chapocheck/1648850858747/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chapocheck | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-01T21:06:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Cum Town (mostly Nick Mullen) quotes
@chapocheck
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.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
| {"title": "DualStyleGAN", "emoji": "\ud83d\udc40", "colorFrom": "green", "colorTo": "gray", "sdk": "gradio", "sdk_version": "2.8.13", "app_file": "app.py", "pinned": false} | hylee/DualStyleGAN | null | [
"has_space",
"region:us"
] | null | 2022-04-02T00:54:21+00:00 | [] | [] | TAGS
#has_space #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488574779351187458/RlIQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/clortown/1648875085007/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/clortown | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-02T01:36:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
yeosang elf agenda
@clortown
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-classification | transformers |
# vliegmachine
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"]} | johnnydevriese/vliegmachine | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-02T01:54:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vliegmachine
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
#### f117
!f117
#### f16
!f16
#### f18
!f18 | [
"# vliegmachine\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",
"#### f117\n\n!f117",
"#### f16\n\n!f16",
"#### f18\n\n!f18"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vliegmachine\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
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