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null | null | # The fastai models - PETS
This model is based on Lesson 1 of [fastai](https://course.fast.ai) and of [Walk with fastai](https://walkwithfastai.com/Pets)
## Dataset Used
This model was created with the [Oxford Pets](https://docs.fast.ai/data.external.html#Image-Classification-datasets) dataset in the fastai framework... | {} | muellerzr/fastai-pets-resnet-34 | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # The fastai models - PETS
This model is based on Lesson 1 of fastai and of Walk with fastai
## Dataset Used
This model was created with the Oxford Pets dataset in the fastai framework
## Model Training
The model was trained as a binary classifier, for cats or dogs
## How to use:
First, ensure that 'huggingface_hub... | [
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text-generation | transformers |
# The Office - Pam DialoGPT Model | {"tags": ["conversational"]} | muhardianab/DialoGPT-small-theoffice | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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fill-mask | transformers |
# TajBERTo: RoBERTa-like Language model trained on Tajik
## First ever Tajik NLP model 🔥
# Dataset:
# This model was trained on filtered and merged version of Leipzig Corpora https://wortschatz.unileipzig.de/en/download/Tajik
## Intended use
# You can use the raw model for masked text generation or fine-tune i... | {"language": ["tg"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u041f\u043e\u0439\u0442\u0430\u0445\u0442\u0438 <mask> \u0414\u0443\u0448\u0430\u043d\u0431\u0435"}, {"text": "<mask> \u0431\u0430 \u0438\u043d \u0441\u0430\u0439\u0442\u0438 \u0448\u0443\u043c\u043e \u043c\u0435\u0434\u0430\u0440\u043e\u044... | muhtasham/TajBERTo | null | [
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# TajBERTo: RoBERTa-like Language model trained on Tajik
## First ever Tajik NLP model
# Dataset:
# This model was trained on filtered and merged version of Leipzig Corpora URL
## Intended use
# You can use the raw model for masked text generation or fine-tune it to a downstream task.
## Example pipeline
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595544
- CO2 Emissions (in grams): 92.87363201770962
## Validation Metrics
- Loss: 0.3001164197921753
- MSE: 0.3001164197921753
- MAE: 0.24272102117538452
- R2: 0.8465975006681247
- RMSE: 0.5478288531303406
- Explained Variance: 0.... | {"language": "de", "tags": "autonlp", "datasets": ["muhtasham/autonlp-data-Doctor_DE"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 92.87363201770962} | muhtasham/autonlp-Doctor_DE-24595544 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595544
- CO2 Emissions (in grams): 92.87363201770962
## Validation Metrics
- Loss: 0.3001164197921753
- MSE: 0.3001164197921753
- MAE: 0.24272102117538452
- R2: 0.8465975006681247
- RMSE: 0.5478288531303406
- Explained Variance: 0.... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595545
- CO2 Emissions (in grams): 203.30658367993382
## Validation Metrics
- Loss: 0.30214861035346985
- MSE: 0.30214861035346985
- MAE: 0.25911855697631836
- R2: 0.8455587614373526
- RMSE: 0.5496804714202881
- Explained Variance:... | {"language": "de", "tags": "autonlp", "datasets": ["muhtasham/autonlp-data-Doctor_DE"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 203.30658367993382} | muhtasham/autonlp-Doctor_DE-24595545 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595545
- CO2 Emissions (in grams): 203.30658367993382
## Validation Metrics
- Loss: 0.30214861035346985
- MSE: 0.30214861035346985
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- R2: 0.8455587614373526
- RMSE: 0.5496804714202881
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Loss: 0.3092539310455322
- MSE: 0.30925390124320984
- MAE: 0.25015318393707275
- R2: 0.841926941198094
- RMSE: 0.5561060309410095
- Explained Variance: 0.... | {"language": "de", "tags": "autonlp", "datasets": ["muhtasham/autonlp-data-Doctor_DE"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 210.5957437893554} | muhtasham/autonlp-Doctor_DE-24595546 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Loss: 0.3092539310455322
- MSE: 0.30925390124320984
- MAE: 0.25015318393707275
- R2: 0.841926941198094
- RMSE: 0.5561060309410095
- Explained Variance: 0.... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595547
- CO2 Emissions (in grams): 396.5529429198159
## Validation Metrics
- Loss: 1.9565489292144775
- MSE: 1.9565489292144775
- MAE: 0.9890901446342468
- R2: -7.68965036332947e-05
- RMSE: 1.3987668752670288
- Explained Variance: ... | {"language": "de", "tags": "autonlp", "datasets": ["muhtasham/autonlp-data-Doctor_DE"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 396.5529429198159} | muhtasham/autonlp-Doctor_DE-24595547 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595547
- CO2 Emissions (in grams): 396.5529429198159
## Validation Metrics
- Loss: 1.9565489292144775
- MSE: 1.9565489292144775
- MAE: 0.9890901446342468
- R2: -7.68965036332947e-05
- RMSE: 1.3987668752670288
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595548
- CO2 Emissions (in grams): 183.88911013564527
## Validation Metrics
- Loss: 0.3050823509693146
- MSE: 0.3050823509693146
- MAE: 0.2664000689983368
- R2: 0.844059188176304
- RMSE: 0.5523425936698914
- Explained Variance: 0.8... | {"language": "de", "tags": "autonlp", "datasets": ["muhtasham/autonlp-data-Doctor_DE"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 183.88911013564527} | muhtasham/autonlp-Doctor_DE-24595548 | null | [
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# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595548
- CO2 Emissions (in grams): 183.88911013564527
## Validation Metrics
- Loss: 0.3050823509693146
- MSE: 0.3050823509693146
- MAE: 0.2664000689983368
- R2: 0.844059188176304
- RMSE: 0.5523425936698914
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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. -->
# tolkien-mythopoeic-gen
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Tolkien's mythopoeic works, ... | {"license": "mit", "tags": ["generated_from_trainer"]} | muirkat/tolkien-mythopoeic-gen | null | [
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| tolkien-mythopoeic-gen
======================
This model is a fine-tuned version of gpt2 on Tolkien's mythopoeic works, namely The Silmarillion and Unfinished Tales of Numenor and Middle Earth.
It achieves the following results on the evaluation set:
* Loss: 3.5110
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2_mnli_bc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2_mnli_bc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args"... | mujeensung/albert-base-v2_mnli_bc | null | [
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| albert-base-v2\_mnli\_bc
========================
This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2952
* Accuracy: 0.9399
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_mnli_bc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI ... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base_mnli_bc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}... | mujeensung/roberta-base_mnli_bc | null | [
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#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base\_mnli\_bc
======================
This model is a fine-tuned version of roberta-base on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2125
* Accuracy: 0.9584
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pre... | [
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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-finetuned-QnA-v1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-QnA-v1", "results": []}]} | mujerry/bert-base-uncased-finetuned-QnA-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-QnA-v1
==================================
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: 2.7610
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 8\n... |
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-finetuned-QnA
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-uncased-finetuned-QnA", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | mujerry/bert-base-uncased-finetuned-QnA | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-QnA
===============================
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: 3.0604
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers | hello
| {} | mukherjeearnab/opsolBERT | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # PrivBERT
PrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at [https://privaseer.ist.psu.edu/data](https://privaseer.ist.psu.edu/data)
## Usage
```
from transformers import AutoTokenizer, AutoModel
to... | {} | mukund/privbert | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # PrivBERT
PrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at URL
## Usage
## License
If you use this dataset in research, you must cite the below paper.
For research, teaching, and scholarship pu... | [
"# PrivBERT \nPrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at URL",
"## Usage",
"## License \nIf you use this dataset in research, you must cite the below paper.\n\nFor research, teaching, an... | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# PrivBERT \nPrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at URL",
"## Usage... |
text-generation | transformers |
# aot DialoGPT Model
| {"tags": ["conversational"]} | munezah/DialoGPT-small-aot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# aot DialoGPT Model
| [
"# aot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# aot DialoGPT Model"
] |
text-generation | transformers |
# sherlock DialoGPT Model | {"tags": ["conversational"]} | munezah/DialoGPT-small-sherlock | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# sherlock DialoGPT Model | [
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"# sherlock DialoGPT Model"
] |
text2text-generation | transformers |
## Prefix use
Use prefix "question: {question} context: {context}" before input to generate the question answering
e.g
"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa"
for generate question prefix
generate questions: nama saya andi. saya tinggal di jakarta. i... | {"language": "id", "license": "mit", "datasets": ["Squad", "XQuad", "Tydiqa"], "widget": [{"text": "I love you"}]} | acul3/mt5-large-id-qgen-qa | null | [
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"id",
"dataset:Squad",
"dataset:XQuad",
"dataset:Tydiqa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-Squad #dataset-XQuad #dataset-Tydiqa #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Prefix use
Use prefix "question: {question} context: {context}" before input to generate the question answering
e.g
"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa"
for generate question prefix
generate questions: nama saya andi. saya tinggal di jakarta. i... | [
"## Prefix use\nUse prefix \"question: {question} context: {context}\" before input to generate the question answering\n\ne.g\n\n\"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa\"\n\nfor generate question prefix\n\ngenerate questions: nama saya andi. saya ting... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-Squad #dataset-XQuad #dataset-Tydiqa #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Prefix use\nUse prefix \"question: {question} context: {context}\" before input to generate the question... |
translation | transformers |
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligned (en-id sentence pair)
| {"language": "id", "license": "mit", "tags": ["translation"], "datasets": ["OPUS", "CC-aligned"], "widget": [{"text": "I love you"}]} | acul3/mt5-translate-en-id | null | [
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"dataset:OPUS",
"dataset:CC-aligned",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #id #dataset-OPUS #dataset-CC-aligned #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligned (en-id sentence pair)
| [
"## MT5-Large-Translate-en-id",
"## Prefix use\nUse prefix \"translate:\" before input to generate the translation\ne.g\n\"translate: i love you\"",
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] | [
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"## MT5-Large-Translate-en-id",
"## Prefix use\nUse prefix \"translate:\" before input to generate the tr... |
automatic-speech-recognition | transformers | ## Evaluation on Common Voice ID Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "munggok/xlsr_indonesia"
device = "cuda"
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' #... | {"language": "id", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "xlsr-fine-tuning-week"], "datasets": ["common_voice"]} | acul3/xlsr_indonesia | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| ## Evaluation on Common Voice ID Test
Result: 25.7 % | [
"## Evaluation on Common Voice ID Test\n\nResult: 25.7 %"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Evaluation on Common Voice ID Test\n\nResult: 25.7 %"
] |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 5691253
## Validation Metrics
- Loss: 1.4430530071258545
- Rouge1: 23.9565
- Rouge2: 19.1897
- RougeL: 23.1191
- RougeLsum: 23.3308
- Gen Len: 20.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer ... | {"language": "en", "tags": "autonlp", "datasets": ["mohsenalam/autonlp-data-billsum-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | murali-admin/bart-billsum-1 | null | [
"transformers",
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"text2text-generation",
"autonlp",
"en",
"dataset:mohsenalam/autonlp-data-billsum-summarization",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autonlp #en #dataset-mohsenalam/autonlp-data-billsum-summarization #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 5691253
## Validation Metrics
- Loss: 1.4430530071258545
- Rouge1: 23.9565
- Rouge2: 19.1897
- RougeL: 23.1191
- RougeLsum: 23.3308
- Gen Len: 20.0
## Usage
You can use cURL to access this model:
| [
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"## Validation Metrics\n\n- Loss: 1.4430530071258545\n- Rouge1: 23.9565\n- Rouge2: 19.1897\n- RougeL: 23.1191\n- RougeLsum: 23.3308\n- Gen Len: 20.0",
"## Usage\n\nYou can use cURL to access this model:"
] | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 5691253",
"## Validation Metrics\n\n- Loss: 1.443... |
feature-extraction | transformers | `bert-base-cased` trained for spelling correction. See [neuspell](https://github.com/neuspell/neuspell) repository for more details about training and evaluating the model. | {} | murali1996/bert-base-cased-spell-correction | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us
| 'bert-base-cased' trained for spelling correction. See neuspell repository for more details about training and evaluating the model. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | mustapha/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 | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | mutamuta/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
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"# Rick DialoGPT Model"
] |
text-generation | transformers |
# SpongeBob DialoGPT Model | {"tags": ["conversational"]} | mutamuta/DialoGPT-spongebob-small | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SpongeBob DialoGPT Model | [
"# SpongeBob DialoGPT Model"
] | [
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"# SpongeBob DialoGPT Model"
] |
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-tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tr", "results": []}]} | mvip/wav2vec2-large-xls-r-300m-tr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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-tr
============================
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: 0.4074
* Wer: 0.4227
Model description
-----------------
More information needed
Intended ... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-squad2", "results": []}]} | mvonwyl/distilbert-base-uncased-finetuned-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-squad2
========================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4218
Model description
-----------------
More information needed
Intended... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
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. -->
# roberta-base-finetuned-squad2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the s... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-finetuned-squad2", "results": []}]} | mvonwyl/roberta-base-finetuned-squad2 | null | [
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| roberta-base-finetuned-squad2
=============================
This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9325
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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",
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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. -->
# bart-mlm
This model is a fine-tuned version of [mwesner/bart-mlm](https://huggingface.co/mwesner/bart-mlm) on the CNN/Dailymail ... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bart-mlm", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]} | mwesner/bart-mlm | null | [
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"bart",
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"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm
========
This model is a fine-tuned version of mwesner/bart-mlm on the CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 7.5338
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: 0.001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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fill-mask | transformers |
# bert-base-uncased
This model was trained on a dataset of issues from github.
It achieves the following results on the evaluation set:
- Loss: 1.2437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Masked language model traine... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased", "results": []}]} | mwesner/bert-base-uncased | null | [
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"bert",
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"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased
=================
This model was trained on a dataset of issues from github.
It achieves the following results on the evaluation set:
* Loss: 1.2437
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\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",
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text-generation | transformers | ## reformer-clm
This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informatio... | {} | mwesner/reformer-clm | null | [
"transformers",
"pytorch",
"reformer",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #region-us
| reformer-clm
------------
This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7783
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: cosine\\_with\\_restarts\n* lr\\_scheduler\... | [
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text-generation | transformers |
# EasternFantasyNoval
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
| {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/AIXX | null | [
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"zh"
] | TAGS
#transformers #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# EasternFantasyNoval
# Overview
- Language model: GPT2-Medium
- Model size: 1.2GiB
- Language: Chinese
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text-generation | transformers |
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate)
The One Thing You Need to Know i... | {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/CPM-GPT2-FP16 | null | [
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"zh"
] | TAGS
#transformers #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
repo: CPM-Generate
The One Thing You Need to Know is this model is not uploaded by official, the ... | [
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text-generation | transformers |
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate)
The One Thing You Need to Know i... | {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/CPM-GPT2 | null | [
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"zh"
] | TAGS
#transformers #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
repo: CPM-Generate
The One Thing You Need to Know is this model is not uploaded by official, the ... | [
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text-generation | transformers |
<h1 align="center">
CPM-Generate-distill
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate)
The One Thing You... | {"language": "zh", "widget": [{"text": "\u5929\u4e0b\u7199\u7199\uff0c"}, {"text": "\u6e05\u534e\u5927\u5b66\u662f"}]} | mymusise/CPM-Generate-distill | null | [
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"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<h1 align="center">
CPM-Generate-distill
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
repo: CPM-Generate
The One Thing You Need to Know is this model is not uploaded by... | [
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"# How to use\n\nHow to use this model directly from the /transformers library:\n\n\n\n\n!avatar"
] |
text-generation | transformers |
# EasternFantasyNoval
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
| {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/EasternFantasyNoval-small | null | [
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"tf",
"gpt2",
"text-generation",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# EasternFantasyNoval
# Overview
- Language model: GPT2-Medium
- Model size: 1.2GiB
- Language: Chinese
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text-generation | transformers |
# EasternFantasyNoval
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
| {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/EasternFantasyNoval | null | [
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"gpt2",
"text-generation",
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"has_space",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# EasternFantasyNoval
# Overview
- Language model: GPT2-Medium
- Model size: 1.2GiB
- Language: Chinese
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text-generation | transformers |
# gpt2-medium-chinese
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
- **Training data**: [wiki2019zh_corpus](https://github.com/brightmart/nlp_chinese_corpus)
- **Source code**: [gpt2-quickly](https://github.com/mymusise/gpt2-quickly)
# Example
```python
from tran... | {"language": "zh", "widget": [{"text": "\u4eca\u5929\u662f\u4e0b\u96e8\u5929"}, {"text": "\u8d70\u5411\u68ee\u6797"}]} | mymusise/gpt2-medium-chinese | null | [
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"tf",
"gpt2",
"text-generation",
"zh",
"autotrain_compatible",
"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-medium-chinese
# Overview
- Language model: GPT2-Medium
- Model size: 1.2GiB
- Language: Chinese
- Training data: wiki2019zh_corpus
- Source code: gpt2-quickly
# Example
输出
Try it on colab
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feature-extraction | transformers | # {MODEL_NAME}
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## Model
This is a finetuned version of [mys/bert-base-turkish-cased-nli-mean](https://huggingface.co/) for FAQ retrieval, which is itself a finetuned version of [dbmdz/bert-base-turkish-cas... | {} | mys/bert-base-turkish-cased-nli-mean-faq-mnr | null | [
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"pytorch",
"tf",
"bert",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #endpoints_compatible #region-us
| # {MODEL_NAME}
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!
## Model
This is a finetuned version of mys/bert-base-turkish-cased-nli-mean for FAQ retrieval, which is itself a finetuned version of dbmdz/bert-base-turkish-cased for NLI. It maps questions ... | [
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"## Model\nThis is a finetuned version of mys/bert-base-turkish-cased-nli-mean for ... |
feature-extraction | transformers | ## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## What is this?
This model is a finetuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) to be used in zero-shot tasks in Turkish. It is... | {} | mys/bert-base-turkish-cased-nli-mean | null | [
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"2004.14963"
] | [] | TAGS
#transformers #tf #bert #feature-extraction #arxiv-2004.14963 #endpoints_compatible #region-us
| ## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!
## What is this?
This model is a finetuned version of dbmdz/bert-base-turkish-cased to be used in zero-shot tasks in Turkish. It is finetuned with an NLI task by using 'sentence-transformer... | [
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feature-extraction | transformers | ## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## What is this?
This model is a finetuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) to be used as a text encoder in Tur... | {} | mys/distilbert-base-turkish-cased-clip | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #feature-extraction #endpoints_compatible #region-us
| ## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!
## What is this?
This model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should be used wit... | [
"## Acknowledgement\nGoogle supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!",
"## What is this?\nThis model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should b... | [
"TAGS\n#transformers #tf #distilbert #feature-extraction #endpoints_compatible #region-us \n",
"## Acknowledgement\nGoogle supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!",
"## What is this?\nThis model is a finetuned version of dbmdz/distilbert-base-turkis... |
token-classification | transformers |
## What is this
A NER model for Turkish with 48 categories trained on the dataset [Shrinked TWNERTC Turkish NER Data](https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar) by Behçet Şentürk, which is itself a filtered and cleaned version of the following automatically labeled dataset:
... | {"language": "tr"} | mys/electra-base-turkish-cased-ner | null | [
"transformers",
"pytorch",
"tf",
"electra",
"token-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #token-classification #tr #autotrain_compatible #endpoints_compatible #region-us
|
## What is this
A NER model for Turkish with 48 categories trained on the dataset Shrinked TWNERTC Turkish NER Data by Behçet Şentürk, which is itself a filtered and cleaned version of the following automatically labeled dataset:
> Sahin, H. Bahadir; Eren, Mustafa Tolga; Tirkaz, Caglar; Sonmez, Ozan; Yildiz, Eray (2... | [
"## What is this\n\nA NER model for Turkish with 48 categories trained on the dataset Shrinked TWNERTC Turkish NER Data by Behçet Şentürk, which is itself a filtered and cleaned version of the following automatically labeled dataset:\n\n> Sahin, H. Bahadir; Eren, Mustafa Tolga; Tirkaz, Caglar; Sonmez, Ozan; Yildiz,... | [
"TAGS\n#transformers #pytorch #tf #electra #token-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"## What is this\n\nA NER model for Turkish with 48 categories trained on the dataset Shrinked TWNERTC Turkish NER Data by Behçet Şentürk, which is itself a filtered and cleaned version... |
text2text-generation | transformers | ## Overview
This model is a finetuned version of [mt5-small](https://huggingface.co/google/mt5-small) for question paraphrasing task in Turkish. As a generator model, its capabilities are currently investigated and there is an ongoing effort to further improve it. You can raise an issue [in this GitHub repo](https://gi... | {} | mys/mt5-small-turkish-question-paraphrasing | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Overview
This model is a finetuned version of mt5-small for question paraphrasing task in Turkish. As a generator model, its capabilities are currently investigated and there is an ongoing effort to further improve it. You can raise an issue in this GitHub repo for any comments, suggestions or interesting findings w... | [
"## Overview\nThis model is a finetuned version of mt5-small for question paraphrasing task in Turkish. As a generator model, its capabilities are currently investigated and there is an ongoing effort to further improve it. You can raise an issue in this GitHub repo for any comments, suggestions or interesting find... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Overview\nThis model is a finetuned version of mt5-small for question paraphrasing task in Turkish. As a generator model, its capabilities are currently inves... |
text-generation | transformers |
This pre-trained model is work in progress! Model weight download will be available in the future.
A 6.8 billion parameter pre-trained model for Japanese language, based on EleutherAI's Mesh Transformer JAX, that has a similar model structure to their GPT-J-6B pre-trained model.
EleutherAIによるMesh Transformer JAXをコード... | {"language": ["ja"], "license": "apache-2.0", "tags": ["japanese", "text-generation", "gptj", "pytorch", "transformers", "t5tokenizer", "sentencepiece"]} | naclbit/gpt-j-japanese-6.8b | null | [
"transformers",
"gptj",
"text-generation",
"japanese",
"pytorch",
"t5tokenizer",
"sentencepiece",
"ja",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"ja"
] | TAGS
#transformers #gptj #text-generation #japanese #pytorch #t5tokenizer #sentencepiece #ja #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This pre-trained model is work in progress! Model weight download will be available in the future.
A 6.8 billion parameter pre-trained model for Japanese language, based on EleutherAI's Mesh Transformer JAX, that has a similar model structure to their GPT-J-6B pre-trained model.
EleutherAIによるMesh Transformer JAXをコー... | [] | [
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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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [anas/wav2vec2-large-xlsr-arabic](https://huggingface.co/an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | nadaAlnada/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of anas/wav2vec2-large-xlsr-arabic on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of anas/wav2vec2-large-xlsr-arabic on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of anas/wav2vec2-large-xlsr-arabic on the common_voice dataset.",
... |
null | null | # Portuguese Legislative Sentence Embedding:
- We trained the Legal Sentence Bert for Brasilian Portuguese using a contrastive learning framework
- Contrastive learning is a machine learning technique used to learn the general features of a dataset without labels by teaching the model which data points are similar o... | {} | nadiafelix/LegalSentenceBertPT-BR | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Portuguese Legislative Sentence Embedding:
- We trained the Legal Sentence Bert for Brasilian Portuguese using a contrastive learning framework
- Contrastive learning is a machine learning technique used to learn the general features of a dataset without labels by teaching the model which data points are similar o... | [
"# Portuguese Legislative Sentence Embedding: \n- We trained the Legal Sentence Bert for Brasilian Portuguese using a contrastive learning framework \n- Contrastive learning is a machine learning technique used to learn the general features of a dataset without labels by teaching the model which data points are si... | [
"TAGS\n#region-us \n",
"# Portuguese Legislative Sentence Embedding: \n- We trained the Legal Sentence Bert for Brasilian Portuguese using a contrastive learning framework \n- Contrastive learning is a machine learning technique used to learn the general features of a dataset without labels by teaching the model... |
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. -->
# rasr_sample
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "rasr_sample", "results": []}]} | naleraphael/rasr_sample | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| rasr\_sample
============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3147
* Wer: 0.2676
Model description
-----------------
More information needed
Intended... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
automatic-speech-recognition | transformers |
# Hindi-Speech to Text Model </br>
The primary objective of this project is to develop a speech recognition system for the Hindi language. As there are very few systems available for speech to text in the Hindi language. Therefore it is an attempt to develop a system where a language model is developed using Machine l... | {"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["Interspeech 2021"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Hindi by Nalini Kumari", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | nalini2799/CDAC_hindispeechrecognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Hindi-Speech to Text Model </br>
The primary objective of this project is to develop a speech recognition system for the Hindi language. As there are very few systems available for speech to text in the Hindi language. Therefore it is an attempt to develop a system where a language model is developed using Machine l... | [
"# Hindi-Speech to Text Model </br>\nThe primary objective of this project is to develop a speech recognition system for the Hindi language. As there are very few systems available for speech to text in the Hindi language. Therefore it is an attempt to develop a system where a language model is developed using Mach... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Hindi-Speech to Text Model </br>\nThe primary objective of this project is to develop a speech recognition system for the Hindi ... |
text-generation | transformers | #House BOT | {"tags": ["huggingtweets"], "widget": [{"text": ""}]} | namanrana16/DialoGPT-small-House | null | [
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"gpt2",
"text-generation",
"huggingtweets",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #House BOT | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#TRUUMP BOT | {"tags": ["conversational"]} | namanrana16/DialoGPT-small-TrumpBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#TRUUMP BOT | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Quotes Generator
## Model description
This is a GPT2 model fine-tuned on the Quotes-500K dataset.
## Intended uses & limitations
For a given user prompt, it can generate motivational quotes starting with it.
#### How to use
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = Au... | {"language": ["en"], "license": "cc", "tags": ["text generation"], "datasets": ["quotes-500K"], "metrics": ["perplexity"]} | nandinib1999/quote-generator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"text generation",
"en",
"dataset:quotes-500K",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #text generation #en #dataset-quotes-500K #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Quotes Generator
## Model description
This is a GPT2 model fine-tuned on the Quotes-500K dataset.
## Intended uses & limitations
For a given user prompt, it can generate motivational quotes starting with it.
#### How to use
## Training data
This is the distribution of the total dataset into training, vali... | [
"# Quotes Generator",
"## Model description\n\nThis is a GPT2 model fine-tuned on the Quotes-500K dataset.",
"## Intended uses & limitations\n\nFor a given user prompt, it can generate motivational quotes starting with it.",
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"# Quotes Generator",
"## Model description\n\nThis is a GPT2 model fine-tuned on the Quotes-500K dataset.",
"#... |
text-generation | transformers |
#LilHalMediumModel | {"tags": "conversational"} | nanometeres/DialoGPT-medium-halbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#LilHalMediumModel | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#lilhalbot DialoGPT Model | {"tags": ["conversational"]} | nanometeres/DialoGPT-small-halbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#lilhalbot DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | sentence-transformers |
# multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "feature-extraction"} | nanopass/test-model-fe | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
| multi-qa-MiniLM-L6-cos-v1
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, hav... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, ... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"####... |
text2text-generation | transformers | # 使用
这个模型是在uer/bart-chinese-6-960-cluecorpussmall基础上训练的，数据量不是很大，但是修改了默认分词。
使用pkuseg分词，禁用BertTokenizer的do_basic_tokenize分词，不禁用do_basic_tokenize的话会把正常词汇按照逐字分词，禁用后可以导入自己的分词方案。
pip install git+https://github.com/napoler/tkit-AutoTokenizerPosition
```python
import pkuseg
from tkitAutoTokenizerPosition.AutoPos import Auto... | {} | napoler/bart-chinese-6-960-words-pkuseg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # 使用
这个模型是在uer/bart-chinese-6-960-cluecorpussmall基础上训练的，数据量不是很大，但是修改了默认分词。
使用pkuseg分词，禁用BertTokenizer的do_basic_tokenize分词，不禁用do_basic_tokenize的话会把正常词汇按照逐字分词，禁用后可以导入自己的分词方案。
pip install git+URL
分词结果如下
URL
URL
URL
URL
| [
"# 使用\n\n这个模型是在uer/bart-chinese-6-960-cluecorpussmall基础上训练的，数据量不是很大，但是修改了默认分词。\n\n使用pkuseg分词，禁用BertTokenizer的do_basic_tokenize分词，不禁用do_basic_tokenize的话会把正常词汇按照逐字分词，禁用后可以导入自己的分词方案。\n\npip install git+URL\n\n分词结果如下\n\n\nURL\n\n\n\nURL\n\nURL\nURL"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# 使用\n\n这个模型是在uer/bart-chinese-6-960-cluecorpussmall基础上训练的，数据量不是很大，但是修改了默认分词。\n\n使用pkuseg分词，禁用BertTokenizer的do_basic_tokenize分词，不禁用do_basic_tokenize的话会把正常词汇按照逐字分词，禁用后可以导入自己的分词方案。\n\npip install g... |
feature-extraction | transformers |
chinese_roberta_L-4_H-512_rdrop | {"language": "Chinese", "license": "apache-2.0", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | napoler/chinese_roberta_L-4_H-512_rdrop | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Chinese"
] | TAGS
#transformers #pytorch #bert #feature-extraction #license-apache-2.0 #endpoints_compatible #region-us
|
chinese_roberta_L-4_H-512_rdrop | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
token-classification | transformers | scibert_scivocab_cased submission for SDU21 Task 1 AI
| {} | napsternxg/scibert_scivocab_cased_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_cased submission for SDU21 Task 1 AI
| [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | scibert_scivocab_uncased submission for SDU21 Task 1 AI
| {} | napsternxg/scibert_scivocab_uncased_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_uncased submission for SDU21 Task 1 AI
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | scibert_scivocab_uncased_ft MLM pretrained on SDU21 Task 1 + 2
| {} | napsternxg/scibert_scivocab_uncased_ft_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_uncased_ft MLM pretrained on SDU21 Task 1 + 2
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | scibert_scivocab_uncased_ft_mlm MLM pretrained on SDU21 Task 1 + 2
| {} | napsternxg/scibert_scivocab_uncased_ft_mlm_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_uncased_ft_mlm MLM pretrained on SDU21 Task 1 + 2
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | scibert_scivocab_uncased_ft_tv MLM pretrained on SDU21 Task 1 + 2
| {} | napsternxg/scibert_scivocab_uncased_ft_tv_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_uncased_ft_tv MLM pretrained on SDU21 Task 1 + 2
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | scibert_scivocab_uncased_tv submission for SDU21 Task 1 AI
| {} | napsternxg/scibert_scivocab_uncased_tv_SDU21_AI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| scibert_scivocab_uncased_tv submission for SDU21 Task 1 AI
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly abstracts from 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model... | {} | nasa-impact/bert-e-base-mlm | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly abstracts from 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | hello
| {} | nastyatrvl/gpt2_emotion_fine_tuned | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello
| [] | [
"TAGS\n#region-us \n"
] |
audio-to-audio | null |
# Audio-to-Audio Test
Nothing to see here! | {"tags": ["audio", "audio-to-audio"], "library_tag": "generic"} | nateraw/audio-to-audio-test2 | null | [
"audio",
"audio-to-audio",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audio #audio-to-audio #region-us
|
# Audio-to-Audio Test
Nothing to see here! | [
"# Audio-to-Audio Test\n\nNothing to see here!"
] | [
"TAGS\n#audio #audio-to-audio #region-us \n",
"# Audio-to-Audio Test\n\nNothing to see here!"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/autoencoder-keras-mnist-demo-with-card | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTrainin... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/autoencoder-keras-mnist-demo-with-card2 | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
image-classification | transformers |
# baked-goods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](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/huggingpics).
## Examp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/baked-goods | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# baked-goods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### cake
!cake
#### cookie
!cookie
#### pie
!pie | [
"# baked-goods\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### cake\n\n!cake",
"#### cookie\n\n!cookie",
"#### pie\n\n!pie"
] | [
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"# baked-goods\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at ... |
image-classification | transformers |
# baseball-stadium-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](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/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/baseball-stadium-foods | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# baseball-stadium-foods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### cotton candy
!cotton candy
#### hamburger
!hamburger
#### hot dog
!hot dog
#### nachos
!nachos
#### popcor... | [
"# baseball-stadium-foods\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\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",
... | [
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"# baseball-stadium-foods\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with t... |
text-classification | transformers |
# bert-base-uncased-ag-news
## Model description
`bert-base-uncased` finetuned on the AG News dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 4 T4 GPUs, 4 epochs. [The code can be found here](https://github.com/nateraw/hf-text-classification)
#### Limitations and bias
- Not... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "ag_news", "pytorch"], "datasets": ["ag_news"], "metrics": ["accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | nateraw/bert-base-uncased-ag-news | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"ag_news",
"en",
"dataset:ag_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #ag_news #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-uncased-ag-news
## Model description
'bert-base-uncased' finetuned on the AG News dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 4 T4 GPUs, 4 epochs. The code can be found here
#### Limitations and bias
- Not the best model...
## Training data
Data came from ... | [
"# bert-base-uncased-ag-news",
"## Model description\n\n'bert-base-uncased' finetuned on the AG News dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 4 T4 GPUs, 4 epochs. The code can be found here",
"#### Limitations and bias\n\n- Not the best model...",
"## Training d... | [
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"# bert-base-uncased-ag-news",
"## Model description\n\n'bert-base-uncased' finetuned on the AG News dataset using PyTorch Lightning. Se... |
text-classification | transformers |
# bert-base-uncased-emotion
## Model description
`bert-base-uncased` finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.
For more details, please see, [the emotion dataset on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotio... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | nateraw/bert-base-uncased-emotion | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-uncased-emotion
## Model description
'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.
For more details, please see, the emotion dataset on nlp viewer.
#### Limitations and bias
- Not the best model,... | [
"# bert-base-uncased-emotion",
"## Model description\n\n'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.\n\nFor more details, please see, the emotion dataset on nlp viewer.",
"#### Limitations and bias\n\n- No... | [
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"# bert-base-uncased-emotion",
"## Model description\n\n'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightn... |
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. -->
# codecarbon-text-classification
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "codecarbon-text-classification", "results": []}]} | nateraw/codecarbon-text-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# codecarbon-text-classification
This model is a fine-tuned version of bert-base-cased on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpara... | [
"# codecarbon-text-classification\n\nThis model is a fine-tuned version of bert-base-cased on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedu... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# codecarbon-text-classification\n\nThis model is a fine-tuned version of bert-base-cased on the imdb dataset.",
"## Model ... |
null | pytorch | # cryptopunks-gan
A DCGAN trained to generate novel Cryptopunks.
Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopunks-gan).
## Generated Punks
Here are some punks generated by this model:
## Usage
You can try it out yourself, or you can play with the ... | {"library_name": "pytorch", "tags": ["dcgan"]} | nateraw/cryptopunks-gan | null | [
"pytorch",
"tensorboard",
"dcgan",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #tensorboard #dcgan #has_space #region-us
| # cryptopunks-gan
A DCGAN trained to generate novel Cryptopunks.
Check out the code by Teddy Koker here.
## Generated Punks
Here are some punks generated by this model:
## Usage
You can try it out yourself, or you can play with the demo.
To use it yourself - make sure you have 't... | [
"# cryptopunks-gan\n\nA DCGAN trained to generate novel Cryptopunks.\n\nCheck out the code by Teddy Koker here.",
"## Generated Punks\n\nHere are some punks generated by this model:\n\n",
"## Usage\n\nYou can try it out yourself, or you can play with the demo.\n\nTo use it yoursel... | [
"TAGS\n#pytorch #tensorboard #dcgan #has_space #region-us \n",
"# cryptopunks-gan\n\nA DCGAN trained to generate novel Cryptopunks.\n\nCheck out the code by Teddy Koker here.",
"## Generated Punks\n\nHere are some punks generated by this model:\n\n",
"## Usage\n\nYou can try it ... |
image-classification | transformers |
# denver-nyc-paris
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](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/huggingpics).
## ... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/denver-nyc-paris | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# denver-nyc-paris
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### denver
!denver
#### new york city
!new york city
#### paris
!paris | [
"# denver-nyc-paris\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### denver\n\n!denver",
"#### new york city\n\n!new york city",
"#### paris\n\n!paris"
] | [
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"# denver-nyc-paris\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues w... |
image-classification | transformers |
# doggos-lol
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"]} | nateraw/doggos-lol | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# doggos-lol
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
#### bernese mountain dog
!bernese mountain dog
#### husky
!husky
#### saint bernard
!saint bernard | [
"# doggos-lol\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",
"#### bernese mountain dog\n\n!bernese mountain dog",
"#### husky\n\n!husky",
"#### saint berna... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# doggos-lol\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... |
image-classification | transformers |
# donut-or-bagel
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/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/donut-or-bagel | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# donut-or-bagel
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
#### bagel
!bagel
#### donut
!donut | [
"# donut-or-bagel\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",
"#### bagel\n\n!bagel",
"#### donut\n\n!donut"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# donut-or-bagel\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
image-classification | transformers |
# ex-for-evan
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/huggingp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/ex-for-evan | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ex-for-evan
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
#### cheetah
!cheetah
#### elephant
!elephant
#### giraffe
!giraffe
#### rhino
!rhino | [
"# ex-for-evan\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",
"#### cheetah\n\n!cheetah",
"#### elephant\n\n!elephant",
"#### giraffe\n\n!giraffe",
"#### ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# ex-for-evan\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues w... |
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. -->
# nateraw/food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "image-classification", "pytorch"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "food101_outputs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food-101", "type": "f... | nateraw/food | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| nateraw/food
============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the nateraw/food101 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4501
* Accuracy: 0.8913
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-classification | generic |
# Model Card for `guesslang`
| {"library_name": "generic", "tags": ["text-classification"]} | nateraw/guesslang | null | [
"generic",
"text-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #text-classification #region-us
|
# Model Card for 'guesslang'
| [
"# Model Card for 'guesslang'"
] | [
"TAGS\n#generic #text-classification #region-us \n",
"# Model Card for 'guesslang'"
] |
object-detection | transformers |
# hot-dog
Ignore me...I'm broken. | {"tags": ["object-detection", "pytorch"]} | nateraw/hot-dog | null | [
"transformers",
"pytorch",
"detr",
"object-detection",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #detr #object-detection #endpoints_compatible #has_space #region-us
|
# hot-dog
Ignore me...I'm broken. | [
"# hot-dog\n\nIgnore me...I'm broken."
] | [
"TAGS\n#transformers #pytorch #detr #object-detection #endpoints_compatible #has_space #region-us \n",
"# hot-dog\n\nIgnore me...I'm broken."
] |
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. -->
# huggingpics-package-demo-2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/goog... | {"license": "apache-2.0", "tags": ["image-classification", "huggingpics", "generated_from_trainer"]} | nateraw/huggingpics-package-demo-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| huggingpics-package-demo-2
==========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3761
* Acc: 0.9403
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #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* tr... |
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. -->
# huggingpics-package-demo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["image-classification", "huggingpics", "generated_from_trainer"], "model-index": [{"name": "huggingpics-package-demo", "results": []}]} | nateraw/huggingpics-package-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| huggingpics-package-demo
========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3761
* Acc: 0.9403
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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: 4.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #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* tr... |
image-classification | keras |
# Test | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification", "keras"]} | nateraw/keras-cats-vs-dogs | null | [
"keras",
"image-classification",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #license-apache-2.0 #has_space #region-us
|
# Test | [
"# Test"
] | [
"TAGS\n#keras #image-classification #license-apache-2.0 #has_space #region-us \n",
"# Test"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/keras-dummy-functional-demo-w-card | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| na... | {"library_name": "keras"} | nateraw/keras-dummy-functional-demo | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | nateraw/keras-dummy-model-mixin-demo-w-card | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | nateraw/keras-dummy-model-mixin-demo | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/keras-dummy-sequential-demo-with-card | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/keras-dummy-sequential-demo | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
image-classification | keras |
Keras Dog vs Cat based on the [official Keras documentation](https://keras.io/examples/vision/image_classification_from_scratch/)
| {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification", "keras"]} | nateraw/keras-mnist-convnet-demo | null | [
"keras",
"image-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #license-apache-2.0 #region-us
|
Keras Dog vs Cat based on the official Keras documentation
| [] | [
"TAGS\n#keras #image-classification #license-apache-2.0 #region-us \n"
] |
image-classification | keras |
# Model Card for nateraw/keras-mobilevit-xxs-flowers
| {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification", "keras"]} | nateraw/keras-mobilevit-xxs-flowers | null | [
"keras",
"image-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #license-apache-2.0 #region-us
|
# Model Card for nateraw/keras-mobilevit-xxs-flowers
| [
"# Model Card for nateraw/keras-mobilevit-xxs-flowers"
] | [
"TAGS\n#keras #image-classification #license-apache-2.0 #region-us \n",
"# Model Card for nateraw/keras-mobilevit-xxs-flowers"
] |
null | null | # Flowers GAN
<a href="https://colab.research.google.com/github/nateraw/huggingface-hub-examples/blob/main/pytorch_lightweight_gan.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Give the [Github Repo](https://github.com/nateraw/huggingface-hub-exa... | {} | nateraw/lightweight-gan-flowers-64 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Flowers GAN
<a href="URL target="_parent"><img src="URL alt="Open In Colab"/></a>
Give the Github Repo a ⭐️
### Generated Images
<video width="320" height="240" controls>
<source src="URL type="video/mp4">
</video>
### EMA
<video width="320" height="240" controls>
<source src="URL type="video/mp4">
</video... | [
"# Flowers GAN\n\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>\n\nGive the Github Repo a ⭐️",
"### Generated Images\n\n<video width=\"320\" height=\"240\" controls>\n <source src=\"URL type=\"video/mp4\">\n</video>",
"### EMA\n\n<video width=\"320\" height=\"240\" controls>\n <s... | [
"TAGS\n#region-us \n",
"# Flowers GAN\n\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>\n\nGive the Github Repo a ⭐️",
"### Generated Images\n\n<video width=\"320\" height=\"240\" controls>\n <source src=\"URL type=\"video/mp4\">\n</video>",
"### EMA\n\n<video width=\"320\" heigh... |
image-classification | timm |
<!-- 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. -->
# my-cool-timm-model-2
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the cats_vs_dogs datas... | {"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["cats_vs_dogs"], "library_tag": "timm"} | nateraw/my-cool-timm-model-2 | null | [
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:cats_vs_dogs",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #region-us
| my-cool-timm-model-2
====================
This model is a fine-tuned version of resnet18 on the cats\_vs\_dogs dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2510
* Acc1: 95.2150
* Acc5: 100.0
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10\n* mixed... | [
"TAGS\n#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #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: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* o... |
image-classification | timm |
<!-- 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. -->
# my-cool-timm-model-3
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the cats_vs_dogs datas... | {"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["cats_vs_dogs"], "model-index": [{"name": "my-cool-timm-model-3", "results": []}]} | nateraw/my-cool-timm-model-3 | null | [
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:cats_vs_dogs",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #region-us
| my-cool-timm-model-3
====================
This model is a fine-tuned version of resnet18 on the cats\_vs\_dogs dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2455
* Acc1: 94.4175
* Acc5: 100.0
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10\n* mixed... | [
"TAGS\n#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #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: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* o... |
image-classification | timm | # Model card for my-cool-timm-model | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/my-cool-timm-model | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for my-cool-timm-model | [
"# Model card for my-cool-timm-model"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for my-cool-timm-model"
] |
image-classification | transformers |
# pasta-pizza-ravioli
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](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/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/pasta-pizza-ravioli | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pasta-pizza-ravioli
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### pasta
!pasta
#### pizza
!pizza
#### ravioli
!ravioli | [
"# pasta-pizza-ravioli\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### pasta\n\n!pasta",
"#### pizza\n\n!pizza",
"#### ravioli\n\n!ravioli"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pasta-pizza-ravioli\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the ... |
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. -->
# pasta-shapes
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | {"license": "apache-2.0", "tags": ["image-classification", "huggingpics", "generated_from_trainer"], "model-index": [{"name": "pasta-shapes", "results": []}]} | nateraw/pasta-shapes | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| pasta-shapes
============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3761
* Acc: 0.9403
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 4.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
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. -->
# planes-trains-automobiles
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/googl... | {"license": "apache-2.0", "tags": ["huggingpics", "image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model_index": [{"name": "planes-trains-automobiles", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "v... | nateraw/planes-trains-automobiles | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| planes-trains-automobiles
=========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the huggingpics dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0534
* Accuracy: 0.9851
Model description
-----------------
Autogenerated by HuggingPics️
C... | [
"#### automobiles\n\n\n!automobiles",
"#### planes\n\n\n!planes",
"#### trains\n\n\n!trains\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### automobiles\n\n\n!automobiles",
"#### planes\n\n\n!planes",
"#### trains\n\n\n!trains\n\n\nIntended uses & limitation... |
image-classification | keras |
# Quickdraw Model | {"library_name": "keras", "tags": ["image-classification", "keras"]} | nateraw/quickdraw-model | null | [
"keras",
"image-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #has_space #region-us
|
# Quickdraw Model | [
"# Quickdraw Model"
] | [
"TAGS\n#keras #image-classification #has_space #region-us \n",
"# Quickdraw Model"
] |
image-classification | transformers |
# rare-puppers-09-04-2021
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/nate... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/rare-puppers-09-04-2021 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers-09-04-2021
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers-09-04-2021\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers-09-04-2021\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport ... |
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