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null | null | # PDNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and lim... | {} | zaccharieramzi/PDNet-fastmri | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| PDNet-fastmri
=============
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
Model description
-----------------
For more details, see URL
This section is WIP.
Intended uses and ... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # UNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limitation... | {} | zaccharieramzi/UNet-OASIS | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # UNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see URL
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruc... | [
"# UNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses and limitations\nThis model... | [
"TAGS\n#region-us \n",
"# UNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses an... |
null | null | # UNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limi... | {} | zaccharieramzi/UNet-fastmri | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| UNet-fastmri
============
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
Model description
-----------------
For more details, see URL
This section is WIP.
Intended uses and li... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # UPDNet-knee-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 4.
The mode... | {} | zaccharieramzi/UPDNet-knee-af4 | null | [
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.07290"
] | [] | TAGS
#arxiv-2010.07290 #region-us
| # UPDNet-knee-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medi... | [
"# UPDNet-knee-af4\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).\nIt is a base model for acceleration factor 4.\nThe model uses 25 iter... | [
"TAGS\n#arxiv-2010.07290 #region-us \n",
"# UPDNet-knee-af4\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).\nIt is a base model for acc... |
null | null | # UPDNet-knee-af8
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 8.
The mode... | {} | zaccharieramzi/UPDNet-knee-af8 | null | [
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.07290"
] | [] | TAGS
#arxiv-2010.07290 #region-us
| # UPDNet-knee-af8
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 8.
The model uses 25 iterations and a medi... | [
"# UPDNet-knee-af8\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).\nIt is a base model for acceleration factor 8.\nThe model uses 25 iter... | [
"TAGS\n#arxiv-2010.07290 #region-us \n",
"# UPDNet-knee-af8\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see URL (0.2dB behind the 2nd submission).\nIt is a base model for acc... |
null | null | # UPDNet-knee-singlecoil-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.
## Intended uses an... | {} | zaccharieramzi/UPDNet-knee-singlecoil-af4 | null | [
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.07290"
] | [] | TAGS
#arxiv-2010.07290 #region-us
| # UPDNet-knee-singlecoil-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see URL
This section is WIP.
## Intended uses and limitations
This model can b... | [
"# UPDNet-knee-singlecoil-af4\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses and lim... | [
"TAGS\n#arxiv-2010.07290 #region-us \n",
"# UPDNet-knee-singlecoil-af4\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis se... |
null | null | # XPDNet-brain-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 3rd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium... | {} | zaccharieramzi/XPDNet-brain-af4 | null | [
"arxiv:2010.07290",
"arxiv:2106.00753",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.07290",
"2106.00753"
] | [] | TAGS
#arxiv-2010.07290 #arxiv-2106.00753 #region-us
| XPDNet-brain-af4
================
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model was used to achieve the 3rd highest submission in terms of PSNR on the fastMRI dataset (see URL
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium MWC... | [] | [
"TAGS\n#arxiv-2010.07290 #arxiv-2106.00753 #region-us \n"
] |
null | null | # XPDNet-brain-af8
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 2nd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
It is a base model for acceleration factor 8.
The model uses 25 iterations and a medium... | {} | zaccharieramzi/XPDNet-brain-af8 | null | [
"arxiv:2010.07290",
"arxiv:2106.00753",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.07290",
"2106.00753"
] | [] | TAGS
#arxiv-2010.07290 #arxiv-2106.00753 #region-us
| XPDNet-brain-af8
================
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model was used to achieve the 2nd highest submission in terms of PSNR on the fastMRI dataset (see URL
It is a base model for acceleration factor 8.
The model uses 25 iterations and a medium MWC... | [] | [
"TAGS\n#arxiv-2010.07290 #arxiv-2106.00753 #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | zald/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0607
* Precision: 0.9253
* Recall: 0.9350
* F1: 0.9301
* Accuracy: 0.9836
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
null | null | # Redcaps Demo
**CURRENTLY UNDER DEVELOPMENT**
| {} | zamborg/redcaps | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # Redcaps Demo
CURRENTLY UNDER DEVELOPMENT
| [
"# Redcaps Demo\n\nCURRENTLY UNDER DEVELOPMENT"
] | [
"TAGS\n#has_space #region-us \n",
"# Redcaps Demo\n\nCURRENTLY UNDER DEVELOPMENT"
] |
null | transformers |
# Model name
SingBert Large - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pr... | {"language": "en", "license": "mit", "tags": ["singapore", "sg", "singlish", "malaysia", "ms", "manglish", "bert-large-uncased"], "datasets": ["reddit singapore, malaysia", "hardwarezone"], "widget": [{"text": "kopi c siew [MASK]"}, {"text": "die [MASK] must try"}]} | zanelim/singbert-large-sg | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-large-uncased",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #bert-large-uncased #en #license-mit #endpoints_compatible #region-us
|
# Model name
SingBert Large - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to SingBert but the large version, which was initialized from BERT large uncased (whole word masking), with pre-training finetuned on
singlish and manglish data.
## Intended uses & limitations
#### How to use
H... | [
"# Model name\n\nSingBert Large - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nSimilar to SingBert but the large version, which was initialized from BERT large uncased (whole word masking), with pre-training finetuned on\nsinglish and manglish data.",
"## Intended uses & limitations",
... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #bert-large-uncased #en #license-mit #endpoints_compatible #region-us \n",
"# Model name\n\nSingBert Large - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nSimilar to SingBert but the... |
null | transformers |
# Model name
SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the lite-version, which was initialized from [Albert base v2](https://github.com/google-research/albert#albert), with pre-training finetuned on
[singlish](ht... | {"language": "en", "license": "mit", "tags": ["singapore", "sg", "singlish", "malaysia", "ms", "manglish", "albert-base-v2"], "datasets": ["reddit singapore, malaysia", "hardwarezone"], "widget": [{"text": "dont play [MASK] leh"}, {"text": "die [MASK] must try"}]} | zanelim/singbert-lite-sg | null | [
"transformers",
"pytorch",
"tf",
"albert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"albert-base-v2",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #albert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #albert-base-v2 #en #license-mit #endpoints_compatible #region-us
|
# Model name
SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to SingBert but the lite-version, which was initialized from Albert base v2, with pre-training finetuned on
singlish and manglish data.
## Intended uses & limitations
#### How to use
Here is how to use this mode... | [
"# Model name\n\nSingBert Lite - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nSimilar to SingBert but the lite-version, which was initialized from Albert base v2, with pre-training finetuned on\nsinglish and manglish data.",
"## Intended uses & limitations",
"#### How to use\n\n\n\nHer... | [
"TAGS\n#transformers #pytorch #tf #albert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #albert-base-v2 #en #license-mit #endpoints_compatible #region-us \n",
"# Model name\n\nSingBert Lite - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nSimilar to SingBert but the lite-ve... |
null | transformers |
# Model name
SingBert - Bert for Singlish (SG) and Manglish (MY).
## Model description
[BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## I... | {"language": "en", "license": "mit", "tags": ["singapore", "sg", "singlish", "malaysia", "ms", "manglish", "bert-base-uncased"], "datasets": ["reddit singapore, malaysia", "hardwarezone"], "widget": [{"text": "kopi c siew [MASK]"}, {"text": "die [MASK] must try"}]} | zanelim/singbert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-base-uncased",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #bert-base-uncased #en #license-mit #endpoints_compatible #region-us
|
# Model name
SingBert - Bert for Singlish (SG) and Manglish (MY).
## Model description
BERT base uncased, with pre-training finetuned on
singlish and manglish data.
## Intended uses & limitations
#### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
... | [
"# Model name\n\nSingBert - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nBERT base uncased, with pre-training finetuned on\nsinglish and manglish data.",
"## Intended uses & limitations",
"#### How to use\n\n\n\nHere is how to use this model to get the features of a given text in PyTor... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #pretraining #singapore #sg #singlish #malaysia #ms #manglish #bert-base-uncased #en #license-mit #endpoints_compatible #region-us \n",
"# Model name\n\nSingBert - Bert for Singlish (SG) and Manglish (MY).",
"## Model description\n\nBERT base uncased, with pre-traini... |
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. -->
# my-awesome-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It a... | {"license": "apache-2.0", "datasets": [], "model_index": [{"name": "my-awesome-model", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | zari/my-awesome-model | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my-awesome-model
================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4356
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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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* ev... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | zaydzuhri/lelouch-medium | 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
|
# My Awesome Model | [
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"# My Awesome Model"
] |
null | null | 123
| {} | zbmain/test | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| 123
| [] | [
"TAGS\n#pytorch #region-us \n"
] |
null | null | I am a README, masquerading as a "model card" | {} | zeke/hello-world | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| I am a README, masquerading as a "model card" | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Jake Peralta | {"tags": ["conversational"]} | zemi/jakebot | 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
|
# Jake Peralta | [
"# Jake Peralta"
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta"
] |
text-generation | transformers | # Harry Potter Bot GPT Model | {"tags": "conversational"} | zen-satvik/BotGPT-medium-HP | 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
| # Harry Potter Bot GPT Model | [
"# Harry Potter Bot GPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter Bot GPT Model"
] |
text-generation | transformers |
#Sponge Bob DialoGPT Model | {"tags": ["conversational"]} | zentos/DialoGPT-small-spongebob | 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
|
#Sponge Bob DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers |
# multi-qa-mpnet-base-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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": ["feature-extraction"]} | zeonai/query_embedding | null | [
"transformers",
"pytorch",
"mpnet",
"fill-mask",
"feature-extraction",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mpnet #fill-mask #feature-extraction #autotrain_compatible #endpoints_compatible #region-us
| multi-qa-mpnet-base-dot-v1
==========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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, h... | [
"### Pre-training\n\n\nWe use the pretrained 'mpnet-base' 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, answer) pairs.\nWe sa... | [
"TAGS\n#transformers #pytorch #mpnet #fill-mask #feature-extraction #autotrain_compatible #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the kl... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model-index": [{"name": "bert-base-finetuned-ynat", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metrics": [{"type": "f1", "value": 0.86691... | zgotter/bert-base-finetuned-ynat | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-ynat
========================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3710
* F1: 0.8669
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: 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* num\\_epochs: 5",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | zhangle/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8374
* Matthews Correlation: 0.5573
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | 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_finetunning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_finetunning", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args"... | zhaoyang/BertFinetuning | null | [
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# bert_finetunning
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Accuracy: 0.8260
- F1: 0.8786
- Combined Score: 0.8523
## Model description
More information needed
## Intended uses & limitations
More i... | [
"# bert_finetunning\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4018\n- Accuracy: 0.8260\n- F1: 0.8786\n- Combined Score: 0.8523",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #tensorboard #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# bert_finetunning\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | zhc/distilbert-base-uncased-finetuned-mrpc-test | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mrpc
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5708
* Accuracy: 0.7034
* F1: 0.8207
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-xls-r-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-common_voice-tr-ft", "results": []}]} | zhichao158/wav2vec2-xls-r-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-common\_voice-tr-ft
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3736
* Wer: 0.2930
* Cer: 0.0708
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 64\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | zhihao/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0615
* Precision: 0.9251
* Recall: 0.9363
* F1: 0.9307
* Accuracy: 0.9841
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
fill-mask | transformers |
## 🧐 About <a name = "about"></a>
tunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.
The model was trained for over 600 000 phrases written in the tunisian dialect.
## 🏁 Getting Started <a name = "getting_started"></a>
... | {} | ziedsb19/tunbert_zied | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
## About <a name = "about"></a>
tunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.
The model was trained for over 600 000 phrases written in the tunisian dialect.
## Getting Started <a name = "getting_started"></a>
Load... | [
"## About <a name = \"about\"></a>\r\n\r\ntunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.\r\n\r\nThe model was trained for over 600 000 phrases written in the tunisian dialect.",
"## Getting Started <a name = \"getting_started\... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## About <a name = \"about\"></a>\r\n\r\ntunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.\r\n\r\nThe model was trained... |
text-generation | transformers |
#Rick and Morty DialoGPT
| {"tags": ["conversational"]} | zinary/DialoGPT-small-rick-new | 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 and Morty DialoGPT
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# OntoProtein model
Pretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introduced in [this paper](https://openreview.net/pdf?id=yfe1VMYAXa4) and first released in [this repository](https://github.com/zjunlp/OntoProtein). This model is tr... | {"tags": ["protein language model"], "datasets": ["ProteinKG25"], "widget": [{"text": "D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T"}]} | zjunlp/OntoProtein | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"protein language model",
"dataset:ProteinKG25",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #protein language model #dataset-ProteinKG25 #autotrain_compatible #endpoints_compatible #region-us
|
# OntoProtein model
Pretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.
## Mod... | [
"# OntoProtein model\nPretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.",
... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #protein language model #dataset-ProteinKG25 #autotrain_compatible #endpoints_compatible #region-us \n",
"# OntoProtein model\nPretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introduc... |
fill-mask | transformers |
### BERT (double)
Model and tokenizer files for BERT (double) model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://arxiv.org/abs/2104.08671).
### Training Data
BERT (double) is pretrained using the same English Wikipedia corpus that the base BERT model (... | {"language": "en", "pipeline_tag": "fill-mask"} | casehold/bert-double | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"fill-mask",
"en",
"arxiv:2104.08671",
"arxiv:1810.04805",
"arxiv:1903.10676",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08671",
"1810.04805",
"1903.10676"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #fill-mask #en #arxiv-2104.08671 #arxiv-1810.04805 #arxiv-1903.10676 #endpoints_compatible #region-us
|
### BERT (double)
Model and tokenizer files for BERT (double) model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.
### Training Data
BERT (double) is pretrained using the same English Wikipedia corpus that the base BERT model (uncased, 110M parameters), bert-base... | [
"### BERT (double)\nModel and tokenizer files for BERT (double) model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.",
"### Training Data\nBERT (double) is pretrained using the same English Wikipedia corpus that the base BERT model (uncased, 110M parameters),... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #pretraining #fill-mask #en #arxiv-2104.08671 #arxiv-1810.04805 #arxiv-1903.10676 #endpoints_compatible #region-us \n",
"### BERT (double)\nModel and tokenizer files for BERT (double) model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and... |
fill-mask | transformers |
### Custom Legal-BERT
Model and tokenizer files for Custom Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://arxiv.org/abs/2104.08671).
### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus... | {"language": "en", "tags": ["legal"], "pipeline_tag": "fill-mask"} | casehold/custom-legalbert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"arxiv:2104.08671",
"arxiv:1808.06226",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08671",
"1808.06226"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #arxiv-1808.06226 #endpoints_compatible #has_space #region-us
|
### Custom Legal-BERT
Model and tokenizer files for Custom Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.
### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (URL The s... | [
"### Custom Legal-BERT\nModel and tokenizer files for Custom Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.",
"### Training Data\nThe pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #arxiv-1808.06226 #endpoints_compatible #has_space #region-us \n",
"### Custom Legal-BERT\nModel and tokenizer files for Custom Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the ... |
fill-mask | transformers |
### Legal-BERT
Model and tokenizer files for Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings](https://arxiv.org/abs/2104.08671).
### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard La... | {"language": "en", "tags": ["legal"], "pipeline_tag": "fill-mask"} | casehold/legalbert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"arxiv:2104.08671",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08671"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #endpoints_compatible #region-us
|
### Legal-BERT
Model and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings.
### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the prese... | [
"### Legal-BERT\nModel and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings.",
"### Training Data\nThe pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to... | [
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text-generation | transformers |
# DialoGPT Model | {"tags": ["conversational"]} | zuto37/DialoGPT-small-sadao | 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
|
# DialoGPT Model | [
"# DialoGPT Model"
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"# DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 451311592
- CO2 Emissions (in grams): 1.8697144296865242
## Validation Metrics
- Loss: 0.4544260799884796
- Accuracy: 0.8042452830188679
- Precision: 0.8331288343558282
- Recall: 0.8573232323232324
- AUC: 0.8759811658249159
- F1: 0.8450... | {"language": "en", "tags": "autonlp", "datasets": ["zwang199/autonlp-data-traffic-nlp"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.8697144296865242} | zwang199/autonlp-traffic-nlp-451311592 | null | [
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"text-classification",
"autonlp",
"en",
"dataset:zwang199/autonlp-data-traffic-nlp",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-zwang199/autonlp-data-traffic-nlp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 451311592
- CO2 Emissions (in grams): 1.8697144296865242
## Validation Metrics
- Loss: 0.4544260799884796
- Accuracy: 0.8042452830188679
- Precision: 0.8331288343558282
- Recall: 0.8573232323232324
- AUC: 0.8759811658249159
- F1: 0.8450... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445
## Validation Metrics
- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.87600... | {"language": "en", "tags": "autonlp", "datasets": ["zwang199/autonlp-data-traffic_nlp_binary"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.171798205242445} | zwang199/autonlp-traffic_nlp_binary-537215209 | null | [
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"dataset:zwang199/autonlp-data-traffic_nlp_binary",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445
## Validation Metrics
- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.87600... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 537215209\n- CO2 Emissions (... |
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-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-1", "results": []}]} | jiobiala24/wav2vec2-base-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:56:08+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-base-1
===============
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9254
* Wer: 0.3216
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* t... |
text-generation | transformers |
# Max DiabloGPT Model | {"tags": ["conversational"]} | Maxwere/DiabloGPT-medium-maxbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T00:29:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Max DiabloGPT Model | [
"# Max DiabloGPT Model"
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"# Max DiabloGPT Model"
] |
null | null | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/233
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
The model was trained on full [TEDLium3](https://... | {} | luomingshuang/icefall_asr_tedlium3_transducer_stateless | null | [
"region:us"
] | null | 2022-03-03T02:56:02+00:00 | [] | [] | TAGS
#region-us
| Note: This recipe is trained with the codes from this PR URL
And the SpecAugment codes from this PR URL
Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
==============================================================================
The model was trained on full TEDLium3 with the script... | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electricidad-base-muchocine-finetuned
This model fine-tunes [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm... | {"language": ["es"], "tags": ["spanish", "sentiment"], "datasets": ["muchocine"], "widget": ["Incre\u00edble pelicula. \u00a1Altamente recomendado!", "Extremadamente malo. Baja calidad"]} | shahp7575/electricidad-base-muchocine-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"spanish",
"sentiment",
"es",
"dataset:muchocine",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T03:46:13+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #spanish #sentiment #es #dataset-muchocine #autotrain_compatible #endpoints_compatible #region-us
|
# electricidad-base-muchocine-finetuned
This model fine-tunes mrm8488/electricidad-base-discriminator on muchocine dataset for sentiment classification to predict *star_rating*.
### How to use
The model can be used directly with the HuggingFace 'pipeline'.
### Examples
| [
"# electricidad-base-muchocine-finetuned\n\nThis model fine-tunes mrm8488/electricidad-base-discriminator on muchocine dataset for sentiment classification to predict *star_rating*.",
"### How to use\nThe model can be used directly with the HuggingFace 'pipeline'.",
"### Examples"
] | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 606217261
- CO2 Emissions (in grams): 7.968891750522204
## Validation Metrics
- Loss: 0.989620566368103
- Accuracy: 0.6777163904235728
- Macro F1: 0.6817448899563519
- Micro F1: 0.6777163904235728
- Weighted F1: 0.6590820060806175
... | {"language": "en", "tags": "autonlp", "datasets": ["Kamuuung/autonlp-data-lessons_tagging"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 7.968891750522204} | Kamuuung/autonlp-lessons_tagging-606217261 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:Kamuuung/autonlp-data-lessons_tagging",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T04:19:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-Kamuuung/autonlp-data-lessons_tagging #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 606217261
- CO2 Emissions (in grams): 7.968891750522204
## Validation Metrics
- Loss: 0.989620566368103
- Accuracy: 0.6777163904235728
- Macro F1: 0.6817448899563519
- Micro F1: 0.6777163904235728
- Weighted F1: 0.6590820060806175
... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 606217261\n- CO2 Emissions... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1188911868863221772/fpcy... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/xqc/1646281436978/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/xqc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T04:21:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
xQc
@xqc
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The mode... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
# BigScience Large Language Model Training
Training a multilingual 176 billion parameters model in the open
[BigScience](https://bigscience.huggingface.co) is a open and collaborative works... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"]} | bigscience/tr11-176B-logs | null | [
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... | null | 2022-03-03T04:38:09+00:00 | [] | [
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... | TAGS
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|
# BigScience Large Language Model Training
Training a multilingual 176 billion parameters model in the open
!BigScience Logo
BigScience is a open and collaborative workshop around the study and creation of very large language models gathering more than 1000 researchers around the worlds. You can find more information... | [
"# BigScience Large Language Model Training\nTraining a multilingual 176 billion parameters model in the open\n!BigScience Logo\n\nBigScience is a open and collaborative workshop around the study and creation of very large language models gathering more than 1000 researchers around the worlds. You can find more inf... | [
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"# BigScience Large Language Model Training\nTraining a multilingual 176 billio... |
text-generation | transformers |
# japanese-soseki-gpt2-1b
This repository provides a 1.3B-parameter finetuned Japanese GPT2 model.
The model was finetuned by [jweb](https://jweb.asia/) based on trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
Both pytorch(pytorch_model.bin) and Rust(rust_model.ot) models are provide... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp", "rust", "rust-bert"], "datasets": ["cc100", "wikipedia", "AozoraBunko"], "thumbnail": "https://github.com/ycat3/japanese-pretrained-models/blob/master/jweb.png", "widget": [{"text": "\u590f\u76ee\u6f31\u77f3\u306f\u3... | jweb/japanese-soseki-gpt2-1b | null | [
"transformers",
"pytorch",
"rust",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"rust-bert",
"dataset:cc100",
"dataset:wikipedia",
"dataset:AozoraBunko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T04:53:15+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #rust #gpt2 #text-generation #ja #japanese #lm #nlp #rust-bert #dataset-cc100 #dataset-wikipedia #dataset-AozoraBunko #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# japanese-soseki-gpt2-1b
!jweb-icon
This repository provides a 1.3B-parameter finetuned Japanese GPT2 model.
The model was finetuned by jweb based on trained by rinna Co., Ltd.
Both pytorch(pytorch_model.bin) and Rust(rust_model.ot) models are provided
# How to use the model
*NOTE:* Use 'T5Tokenizer' to initiate ... | [
"# japanese-soseki-gpt2-1b\n\n!jweb-icon\n\nThis repository provides a 1.3B-parameter finetuned Japanese GPT2 model.\nThe model was finetuned by jweb based on trained by rinna Co., Ltd.\nBoth pytorch(pytorch_model.bin) and Rust(rust_model.ot) models are provided",
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"# japanese-soseki-gpt2-1b\n\n!jweb-icon\n\nThis repository provid... |
null | null | https://sattaking-sattaking.com | {} | sattaguru/game | null | [
"region:us"
] | null | 2022-03-03T05:30:04+00:00 | [] | [] | TAGS
#region-us
| URL | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-weaksup-100-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-100-pad-early", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-100-pad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T06:28:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-weaksup-100-pad-early
==============================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0714
* Rouge1: 26.6767
* Rouge2: 8.6321
* Rougel: 17.4235
* Rougelsum:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
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. -->
# ernie_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail da... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "ernie_bert_summarization_cnn_dailymail", "results": []}]} | Ayham/ernie_bert_summarization_cnn_dailymail | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T08:14:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# ernie_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# ernie_bert_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# ernie_bert_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw`
This model was trained by Shaoxiong Lin using dns_ins20 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4538462eb7dc6a6b858adcbd3a526fb8173d6f73
pip inst... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["dns_ins20"]} | Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:dns_ins20",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-03T08:35:14+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-dns_ins20 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'Johnson-Lsx/Shaoxiong\_Lin\_dns\_ins20\_enh\_enh\_train\_enh\_dccrn\_raw'
This model was trained by Shaoxiong Lin using dns\_ins20 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Feb 10 23:11:40 CST 2022'
* pyt... | [
"### 'Johnson-Lsx/Shaoxiong\\_Lin\\_dns\\_ins20\\_enh\\_enh\\_train\\_enh\\_dccrn\\_raw'\n\n\nThis model was trained by Shaoxiong Lin using dns\\_ins20 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Feb 10 23:11:40 CST 2022'\n* python v... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-dns_ins20 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'Johnson-Lsx/Shaoxiong\\_Lin\\_dns\\_ins20\\_enh\\_enh\\_train\\_enh\\_dccrn\\_raw'\n\n\nThis model was trained by Shaoxiong Lin using dns\\_ins20 recipe in espnet.",
"### Demo: How to use in E... |
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | carolEileen/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T08:55:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4725
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-generation | transformers |
#technoai model | {"tags": ["conversational"]} | sadkat/technoai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T09:00:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#technoai model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Sean Diaz Model
| {"tags": ["conversational"]} | kookyklavicle/sean-diaz-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T10:12:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sean Diaz Model
| [
"# Sean Diaz Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sean Diaz Model"
] |
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-large-cnn-finetuned-new-100-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-new-100-pad-early", "results": []}]} | cammy/bart-large-cnn-finetuned-new-100-pad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T10:22:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-new-100-pad-early
==========================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9543
* Rouge1: 21.8858
* Rouge2: 8.1444
* Rougel: 16.5751
* Rougelsum: 19.163
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
text-generation | transformers |
# Sean Diaz (Life is Strange 2) Chat Model
| {"tags": ["conversational"]} | kookyklavicle/sean-diaz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T11:24:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sean Diaz (Life is Strange 2) Chat Model
| [
"# Sean Diaz (Life is Strange 2) Chat Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sean Diaz (Life is Strange 2) Chat Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# beto_amazon_final_pnn
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "beto_amazon_final_pnn", "results": []}]} | MarioPenguin/beto_amazon_final_pnn | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T12:50:27+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| beto\_amazon\_final\_pnn
========================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5242
* Train Accuracy: 0.7838
* Validation Loss: 0.6507
* Validation Accuracy: 0.7284
* Epoc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'bet... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-pubmed
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the pub_med_summariz... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_su... | Kevincp560/t5-base-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T13:28:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-pubmed
========================
This model is a fine-tuned version of t5-base on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6311
* Rouge1: 9.3771
* Rouge2: 3.7042
* Rougel: 8.4912
* Rougelsum: 9.0013
* Gen Len: 19.0
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamete... |
text-classification | transformers | # roberta_sentiments_es , a Sentiment Analysis model for Spanish sentences
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from... | {} | Manauu17/roberta_sentiments_es | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T13:50:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # roberta_sentiments_es , a Sentiment Analysis model for Spanish sentences
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
## Example of classification
Output:
| [
"# roberta_sentiments_es , a Sentiment Analysis model for Spanish sentences\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences",
"## Example of classification\n\n\nOutput:"
] | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_sentiments_es , a Sentiment Analysis model for Spanish sentences\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# beto_amazon_final_posneg
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuch... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "beto_amazon_final_posneg", "results": []}]} | MarioPenguin/beto_amazon_final_posneg | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T13:55:40+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| beto\_amazon\_final\_posneg
===========================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1429
* Train Accuracy: 0.9510
* Validation Loss: 0.2942
* Validation Accuracy: 0.8913
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-07, 'decay': 0.0, 'bet... |
translation | transformers | ERROR: type should be string, got "\nhttps://github.com/mt-empty/assyrian-translation-model\n\nThis is an English to Assyrian/Eastern Syriac machine translation model, it uses [English to Arabic](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) model as the base model.\n\nAlthough the project aim is to Build a English to Assyrian - the ones that fall under [Northeastern Neo-Aramaic](https://en.wikipedia.org/wiki/Northeastern_Neo-Aramaic) - the current model mostly provides translation for Classical Syriac. This model is a good initial step, but I hope future work will make it more inline with Assyrian dialects.\n" | {"language": ["en", "as"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu"]} | mt-empty/english-assyrian | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"as",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T14:05:11+00:00 | [] | [
"en",
"as"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #as #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
URL
This is an English to Assyrian/Eastern Syriac machine translation model, it uses English to Arabic model as the base model.
Although the project aim is to Build a English to Assyrian - the ones that fall under Northeastern Neo-Aramaic - the current model mostly provides translation for Classical Syriac. This mo... | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #as #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wsj0-5percent-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wsj0-5percent-supervised", "results": []}]} | Kuray107/wsj0-5percent-supervised | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T14:31:38+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wsj0-5percent-supervised
========================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3883
* Wer: 0.1555
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 12\n* eval\\_b... |
question-answering | transformers |
# AWS Neuron Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad)
# DistilBERT base cased distilled SQuAD
This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) k... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "metrics": ["squad"]} | philschmid/distilbert-neuron | null | [
"transformers",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T15:25:21+00:00 | [] | [
"en"
] | TAGS
#transformers #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# AWS Neuron Conversion of distilbert-base-cased-distilled-squad
# DistilBERT base cased distilled SQuAD
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT ... | [
"# AWS Neuron Conversion of distilbert-base-cased-distilled-squad",
"# DistilBERT base cased distilled SQuAD\n\nThis model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.\nThis model reaches a F1 score of 87.1 on the dev set (for compar... | [
"TAGS\n#transformers #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# AWS Neuron Conversion of distilbert-base-cased-distilled-squad",
"# DistilBERT base cased distilled SQuAD\n\nThis model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tu... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2`
This model was trained by YushiUeda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout cf73065ba66cf6efb94af4415f0facaaef86abf6
pip instal... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iemocap"]} | espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-03T15:29:59+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/YushiUeda\_iemocap\_sentiment\_asr\_train\_asr\_conformer\_wav2vec2'
This model was trained by YushiUeda using iemocap recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Mar 3 00:09:55 EST 2022'
* python ve... | [
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer\\_wav2vec2'\n\n\nThis model was trained by YushiUeda using iemocap recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Mar 3 00:09:55 EST 2022'\n* python version: '... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer\\_wav2vec2'\n\n\nThis model was trained by YushiUeda using iemocap recipe in espnet.",
"### Demo: How to use ... |
fill-mask | transformers |
# BERT-i-base-cased (gnBERT-base-cased)
A pre-trained BERT model for **Guarani** (12 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{aguero-et-al2023multi-affect-low-langs-grn,
title={Multidimensional Affective Analysis for Low-resource Languages: A Use Case with Guarani... | {"language": "gn", "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["f1", "accuracy"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me"}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/gn-bert-base-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0356",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-03T15:40:20+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0356 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-i-base-cased (gnBERT-base-cased)
A pre-trained BERT model for Guarani (12 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# BERT-i-base-cased (gnBERT-base-cased)\n\nA pre-trained BERT model for Guarani (12 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0356 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-i-base-cased (gnBERT-base-cased)\n\nA pre-trained BERT model for Guarani (12 layers, cased). Trained on Wikipe... |
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-2 | null | [
"keras",
"region:us"
] | null | 2022-03-03T15:50:54+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/autoencoder-keras-mnist-demo-with-card-2 | null | [
"keras",
"region:us"
] | null | 2022-03-03T15:53:14+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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pub_med_summa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_s... | Kevincp560/t5-small-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T16:24:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-pubmed
=========================
This model is a fine-tuned version of t5-small on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2635
* Rouge1: 8.8295
* Rouge2: 3.2594
* Rougel: 7.9975
* Rougelsum: 8.4483
* Gen Len: 19.0
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamete... |
image-segmentation | transformers | # SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic.
SegFormer model fine-tuned on [Segments.ai](https://segments.ai) [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation w... | {"tags": ["vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"], "widget": [{"src": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg", "example_title": "Brugge"}]} | segments-tobias/segformer-b0-finetuned-segments-sidewalk | null | [
"transformers",
"pytorch",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"arxiv:2105.15203",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-03T16:41:19+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #endpoints_compatible #has_space #region-us
| # SegFormer (b0-sized) model fine-tuned on URL sidewalk-semantic.
SegFormer model fine-tuned on URL 'sidewalk-semantic'. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
## Model description
SegFormer ... | [
"# SegFormer (b0-sized) model fine-tuned on URL sidewalk-semantic.\nSegFormer model fine-tuned on URL 'sidewalk-semantic'. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.",
"## Model description\... | [
"TAGS\n#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b0-sized) model fine-tuned on URL sidewalk-semantic.\nSegFormer model fine-tuned on URL 'sidewalk-semantic'. It was... |
null | null | this is example | {} | tam321/model123321 | null | [
"region:us"
] | null | 2022-03-03T16:48:14+00:00 | [] | [] | TAGS
#region-us
| this is example | [] | [
"TAGS\n#region-us \n"
] |
null | transformers |
# BETito (Spanish TinyBERT for BETO) | {"language": ["es"], "tags": ["spanish", "tinybert"], "datasets": ["large_spanish_corpus"]} | mrm8488/spanish-TinyBERT-betito | null | [
"transformers",
"pytorch",
"bert",
"spanish",
"tinybert",
"es",
"dataset:large_spanish_corpus",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T17:15:17+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #spanish #tinybert #es #dataset-large_spanish_corpus #endpoints_compatible #region-us
|
# BETito (Spanish TinyBERT for BETO) | [
"# BETito (Spanish TinyBERT for BETO)"
] | [
"TAGS\n#transformers #pytorch #bert #spanish #tinybert #es #dataset-large_spanish_corpus #endpoints_compatible #region-us \n",
"# BETito (Spanish TinyBERT for BETO)"
] |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b3-finetuned-segments-sidewalk
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"], "widget": [{"src": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg", "example_title": "Brugge"}], "base_model": "nvid... | segments-tobias/segformer-b3-finetuned-segments-sidewalk | null | [
"transformers",
"pytorch",
"safetensors",
"segformer",
"generated_from_trainer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"base_model:nvidia/mit-b3",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-03T17:19:57+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #segformer #generated_from_trainer #vision #image-segmentation #dataset-segments/sidewalk-semantic #base_model-nvidia/mit-b3 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| segformer-b3-finetuned-segments-sidewalk
========================================
This model is a fine-tuned version of nvidia/mit-b3 on the 'sidewalk-semantic' dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8527
* Miou: 0.4345
* Macc: 0.5079
* Overall Accuracy: 0.8871
* Per Category Io... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 200",
"### Trai... | [
"TAGS\n#transformers #pytorch #safetensors #segformer #generated_from_trainer #vision #image-segmentation #dataset-segments/sidewalk-semantic #base_model-nvidia/mit-b3 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used du... |
text-classification | transformers | # **Q-Cat**
A pre-trained Distilbert model for classifying question types. For use in QA systems.
Dataset contains ~800 labeled examples. Classifier uses a taxonomy of 27 question types. | {"license": "mit"} | ekohrt/qcat | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T17:22:24+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Q-Cat
A pre-trained Distilbert model for classifying question types. For use in QA systems.
Dataset contains ~800 labeled examples. Classifier uses a taxonomy of 27 question types. | [
"# Q-Cat \r\nA pre-trained Distilbert model for classifying question types. For use in QA systems.\r\n\r\nDataset contains ~800 labeled examples. Classifier uses a taxonomy of 27 question types."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Q-Cat \r\nA pre-trained Distilbert model for classifying question types. For use in QA systems.\r\n\r\nDataset contains ~800 labeled examples. Classifier uses a taxonomy of 27... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu", "results": []}]} | kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T17:46:12+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
===================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.5907
* Validation Loss: 1.6321
* Epoch: 2
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 783, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text2text-generation | transformers |
<!-- 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. -->
# ernie_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "ernie_roberta_summarization_cnn_dailymail", "results": []}]} | Ayham/ernie_roberta_summarization_cnn_dailymail | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T18:05:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# ernie_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# ernie_roberta_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# ernie_roberta_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
... |
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. -->
# wikihow-t5-small-finetuned-pubmed
This model is a fine-tuned version of [deep-learning-analytics/wikihow-t5-small](https://huggi... | {"tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "wikihow-t5-small-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_summarization_data... | Kevincp560/wikihow-t5-small-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T19:09:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| wikihow-t5-small-finetuned-pubmed
=================================
This model is a fine-tuned version of deep-learning-analytics/wikihow-t5-small on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2702
* Rouge1: 8.9619
* Rouge2: 3.2719
* Rougel: 8.15... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
fill-mask | transformers |
# BatteryOnlyBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is uncased: it does not make a diff... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batteryonlybert-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T19:09:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryOnlyBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
## Model description... | [
"# BatteryOnlyBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.",
"## Mode... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It w... |
fill-mask | transformers |
# BatteryOnlyBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it
makes a differe... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batteryonlybert-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T19:09:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryOnlyBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
## Model description
B... | [
"# BatteryOnlyBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.",
"## Model d... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-NER-finetuned-ner-ISU
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-NER-finetuned-ner-ISU", "results": []}]} | mcdzwil/bert-base-NER-finetuned-ner-ISU | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-03T20:12:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-NER-finetuned-ner-ISU
===============================
This model is a fine-tuned version of dslim/bert-base-NER on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1090
* Precision: 0.9408
* Recall: 0.8223
* F1: 0.8776
* Accuracy: 0.9644
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-generation | transformers |
# Aqua Model | {"tags": ["conversational", "lm-head", "casual-lm"]} | Aquasp34/DialoGPT-small-aqua1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"lm-head",
"casual-lm",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-03T20:26:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #lm-head #casual-lm #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Aqua Model | [
"# Aqua Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #lm-head #casual-lm #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Aqua 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-base-2
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-1](https://huggingface.co/jiobiala24/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-2", "results": []}]} | jiobiala24/wav2vec2-base-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T04:00:58+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-base-2
===============
This model is a fine-tuned version of jiobiala24/wav2vec2-base-1 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9415
* Wer: 0.3076
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* t... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail da... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_ernie_summarization_cnn_dailymail", "results": []}]} | Ayham/bert_ernie_summarization_cnn_dailymail | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T05:25:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# bert_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# bert_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"... |
null | transformers |
# Erlangshen-Longformer-110M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理长文本，采用旋转位置编码的中文版1.1亿参数的Longformer-base
The Chinese Longformer-base (110M), which uses rotating positional encoding, is adept a... | {"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | IDEA-CCNL/Erlangshen-Longformer-110M | null | [
"transformers",
"pytorch",
"longformer",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T06:08:07+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #longformer #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us
| Erlangshen-Longformer-110M
==========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理长文本，采用旋转位置编码的中文版1.1亿参数的Longformer-base
The Chinese Longformer-base (110M), which uses rotating positional encoding, is adept at handling lengthy text.
模型分类 M... | [
"### 加载模型 Loading Models\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #longformer #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### 加载模型 Loading Models\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou ... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install -e... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-04T07:00:57+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'Yulinfeng/wsj0\_2mix\_enh\_train\_enh\_dpcl\_raw\_valid.si\_snr.ave'
This model was trained by earthmanylf using wsj0\_2mix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Feb 24 16:26:21 CST 2022'
* python ver... | [
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_dpcl\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Feb 24 16:26:21 CST 2022'\n* python version: ... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_dpcl\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-04T07:16:14+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'Yulinfeng/wsj0\_2mix\_enh\_train\_enh\_dan\_tf\_raw\_valid.si\_snr.ave'
This model was trained by earthmanylf using wsj0\_2mix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Mar 3 14:33:32 CST 2022'
* python v... | [
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_dan\\_tf\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Mar 3 14:33:32 CST 2022'\n* python versio... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_dan\\_tf\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPne... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install -e ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-04T07:19:31+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'Yulinfeng/wsj0\_2mix\_enh\_train\_enh\_mdc\_raw\_valid.si\_snr.ave'
This model was trained by earthmanylf using wsj0\_2mix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Mar 3 17:10:03 CST 2022'
* python versi... | [
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_mdc\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Mar 3 17:10:03 CST 2022'\n* python version: '3... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'Yulinfeng/wsj0\\_2mix\\_enh\\_train\\_enh\\_mdc\\_raw\\_valid.si\\_snr.ave'\n\n\nThis model was trained by earthmanylf using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\... |
text-classification | transformers | # Model Description
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) and fine-tuning it on the
[github ticket tagger dataset](https://tickettagger.blob.core.windows.net/datasets/dataset-labels-top3-30k-real.txt). It classifies issue into 3 common categorie... | {"datasets": ["ticket-tagger"], "metrics": ["accuracy"], "model-index": [{"name": "distil-bert-uncased-finetuned-github-issues", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ticket tagger", "type": "ticket tagger", "args": "full"}, "metrics": [{"type": "accur... | ivanlau/distil-bert-uncased-finetuned-github-issues | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"dataset:ticket-tagger",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T08:48:29+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #dataset-ticket-tagger #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Model Description
This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the
github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.
It achieves the following results on the evaluation set:
- Accuracy: 0.7862
### Training hyperparameters... | [
"# Model Description\n\nThis model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the\ngithub ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.\n\nIt achieves the following results on the evaluation set:\n- Accuracy: 0.7862",
"### Training ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #dataset-ticket-tagger #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Description\n\nThis model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the\ngithub ticket tagger dataset. It classifies ... |
text2text-generation | transformers |
# ByT5-Korean - large
ByT5-Korean is a Korean specific extension of Google's [ByT5](https://github.com/google-research/byt5).
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ... | {"license": "apache-2.0", "datasets": ["mc4"]} | everdoubling/byt5-Korean-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-04T09:03:25+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5-Korean - large
ByT5-Korean is a Korean specific extension of Google's ByT5.
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ByT5's utf-8 encoding allows generic encodi... | [
"# ByT5-Korean - large\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.\r\nWhile the ByT5's utf-8 encoding allows g... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5-Korean - large\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (... |
null | null | # yu4u/age-estimation-pytorch
- Repo: https://github.com/yu4u/age-estimation-pytorch
- Model: https://github.com/yu4u/age-estimation-pytorch/releases/download/v1.0/epoch044_0.02343_3.9984.pth
| {} | public-data/yu4u-age-estimation-pytorch | null | [
"has_space",
"region:us"
] | null | 2022-03-04T09:37:49+00:00 | [] | [] | TAGS
#has_space #region-us
| # yu4u/age-estimation-pytorch
- Repo: URL
- Model: URL
| [
"# yu4u/age-estimation-pytorch\n\n- Repo: URL\n- Model: URL"
] | [
"TAGS\n#has_space #region-us \n",
"# yu4u/age-estimation-pytorch\n\n- Repo: URL\n- Model: URL"
] |
text-generation | transformers |
#khemx DialoGPT Model | {"tags": ["conversational"]} | zenham/khemx | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-04T11:28:47+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#khemx DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is *google/byt5-small* transformer model trained on Lithuanian text for ~100 hours.
It was created during the work [**Towards Lithuanian Grammatical Error Correction**](https://link.springer.com/chapter/10.1007/978-3-031-09076-9_44), which was presented at [11th Computer Science On-line Conference 2022](https://cs... | {"language": "lt", "license": "apache-2.0", "tags": ["byt5", "Lithuanian", "grammatical error correction"], "widget": [{"text": "Sveiki pardodu tvarkyng\u0105 \"Audi\" firmos automobyl\u012f. K\u0105tik i\u0161 Amerik\u0117s. Viena savininka pri\u017eiurietas ir mylietas Automobylis. Dar turu patobulint\u0105 \u201eMer... | LukasStankevicius/ByT5-Lithuanian-gec-100h | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"byt5",
"Lithuanian",
"grammatical error correction",
"lt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-04T11:33:39+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #byt5 #Lithuanian #grammatical error correction #lt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is *google/byt5-small* transformer model trained on Lithuanian text for ~100 hours.
It was created during the work Towards Lithuanian Grammatical Error Correction, which was presented at 11th Computer Science On-line Conference 2022.
The model is yet in its infancy (we are planning to train 100x longer in the fut... | [
"## Usage\nGiven the following corrupted text obtained from [URL\n\nThe correction can be obtained by:\n\nOutput from the above would be:\n\nSveiki parduodu tvarkingą „Audi“ firmos automobilį. Ką tik iš Amerikės. Viena savininkas prižiūrintas ir mylimas automobilis. Dar turiu patobulintą „Mersedes“ su automatine gr... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #byt5 #Lithuanian #grammatical error correction #lt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Usage\nGiven the following corrupted text obtained from [URL\n\nThe correction can be obtained ... |
fill-mask | transformers |
# BERT-i-large-cased (gnBERT-large-cased)
A pre-trained BERT model for **Guarani** (24 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{aguero-et-al2023multi-affect-low-langs-grn,
title={Multidimensional Affective Analysis for Low-resource Languages: A Use Case with Guara... | {"language": "gn", "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["accuracy", "f1"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me "}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/gn-bert-large-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0359",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T12:16:40+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0359 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-i-large-cased (gnBERT-large-cased)
A pre-trained BERT model for Guarani (24 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# BERT-i-large-cased (gnBERT-large-cased)\n\nA pre-trained BERT model for Guarani (24 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0359 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-i-large-cased (gnBERT-large-cased)\n\nA pre-trained BERT model for Guarani (24 layers, cased). Trained on Wiki... |
fill-mask | transformers |
# BERT-i-tiny-cased (gnBERT-tiny-cased)
A pre-trained BERT model for **Guarani** (2 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{aguero-et-al2023multi-affect-low-langs-grn,
title={Multidimensional Affective Analysis for Low-resource Languages: A Use Case with Guarani-... | {"language": "gn", "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["f1", "accuracy"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me "}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/gn-bert-tiny-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0358",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T12:24:57+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0358 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-i-tiny-cased (gnBERT-tiny-cased)
A pre-trained BERT model for Guarani (2 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# BERT-i-tiny-cased (gnBERT-tiny-cased)\n\nA pre-trained BERT model for Guarani (2 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0358 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-i-tiny-cased (gnBERT-tiny-cased)\n\nA pre-trained BERT model for Guarani (2 layers, cased). Trained on Wikiped... |
fill-mask | transformers |
# BERT-i-small-cased (gnBERT-small-cased)
A pre-trained BERT model for **Guarani** (6 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{aguero-et-al2023multi-affect-low-langs-grn,
title={Multidimensional Affective Analysis for Low-resource Languages: A Use Case with Guaran... | {"language": "gn", "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["f1", "accuracy"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me "}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/gn-bert-small-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0357",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T12:28:57+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0357 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-i-small-cased (gnBERT-small-cased)
A pre-trained BERT model for Guarani (6 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# BERT-i-small-cased (gnBERT-small-cased)\n\nA pre-trained BERT model for Guarani (6 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0357 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-i-small-cased (gnBERT-small-cased)\n\nA pre-trained BERT model for Guarani (6 layers, cased). Trained on Wikip... |
fill-mask | transformers |
# BETO+gn-base-cased
[BETO-base-cased (pre-trained Spanish BERT model)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) fine-tuned for **Guarani** language modeling (Spanish + Guarani). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{aguero-et-al2023multi-affect-low-langs-gr... | {"language": ["gn", "es"], "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["f1", "accuracy"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me "}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/beto-gn-base-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"es",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0354",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T12:37:20+00:00 | [] | [
"gn",
"es"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #es #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0354 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BETO+gn-base-cased
BETO-base-cased (pre-trained Spanish BERT model) fine-tuned for Guarani language modeling (Spanish + Guarani). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# BETO+gn-base-cased\n\nBETO-base-cased (pre-trained Spanish BERT model) fine-tuned for Guarani language modeling (Spanish + Guarani). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #es #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0354 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BETO+gn-base-cased\n\nBETO-base-cased (pre-trained Spanish BERT model) fine-tuned for Guarani language modeling... |
fill-mask | transformers |
# mBERT+gn-base-cased (multilingual-BERT+gn-base-cased)
[BERT multilingual base model (cased, pre-trained BERT model)](https://huggingface.co/bert-base-multilingual-cased) fine-tuned for **Guarani** language modeling (104 languages + gn). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
```
@article{ag... | {"language": ["gn", "multilingual"], "license": "mit", "datasets": ["wikipedia", "wiktionary"], "metrics": ["f1", "accuracy"], "widget": [{"text": "Paraguay ha'e pete\u0129 t\u00e1va o\u0129va [MASK] ret\u00e3me "}, {"text": "Augusto Roa Bastos ha'e pete\u0129 [MASK] arandu"}]} | mmaguero/multilingual-bert-gn-base-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"gn",
"multilingual",
"dataset:wikipedia",
"dataset:wiktionary",
"doi:10.57967/hf/0355",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T12:47:14+00:00 | [] | [
"gn",
"multilingual"
] | TAGS
#transformers #pytorch #bert #fill-mask #gn #multilingual #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0355 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mBERT+gn-base-cased (multilingual-BERT+gn-base-cased)
BERT multilingual base model (cased, pre-trained BERT model) fine-tuned for Guarani language modeling (104 languages + gn). Trained on Wikipedia + Wiktionary (~800K tokens).
# How cite?
| [
"# mBERT+gn-base-cased (multilingual-BERT+gn-base-cased)\n\nBERT multilingual base model (cased, pre-trained BERT model) fine-tuned for Guarani language modeling (104 languages + gn). Trained on Wikipedia + Wiktionary (~800K tokens).",
"# How cite?"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #gn #multilingual #dataset-wikipedia #dataset-wiktionary #doi-10.57967/hf/0355 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mBERT+gn-base-cased (multilingual-BERT+gn-base-cased)\n\nBERT multilingual base model (cased, pre-tra... |
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. -->
# distilbart-cnn-6-6-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-6-6-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": ... | Kevincp560/distilbart-cnn-6-6-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T12:49:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbart-cnn-6-6-finetuned-pubmed
===================================
This model is a fine-tuned version of sshleifer/distilbart-cnn-6-6 on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0648
* Rouge1: 39.2769
* Rouge2: 15.876
* Rougel: 24.2306
* R... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi_home-colab-11
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi_home-colab-11", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-hindi_home-colab-11 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T13:45:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-hindi\_home-colab-11
==============================================
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: 3.7649
* Wer: 1.0
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.03\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=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.03\n* tra... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-8-10-0.01
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-8-10-0.01", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"... | daisyxie21/bert-base-uncased-8-10-0.01 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T14:27:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-8-10-0.01
===========================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8324
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.01\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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": []}]} | jish/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 | 2022-03-04T14:44:11+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: 3.6423
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# ernie_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail d... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "ernie_ernie_summarization_cnn_dailymail", "results": []}]} | Ayham/ernie_ernie_summarization_cnn_dailymail | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T14:48:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# ernie_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training ... | [
"# ernie_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# ernie_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
... |
automatic-speech-recognition | transformers |
# XLS-R-300m-FTSpeech
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [FTSpeech dataset](https://ftspeech.github.io/), being a dataset of 1,800 hours of transcribed speeches from the Danish parliament.
## Performa... | {"language": ["da"], "license": "other", "datasets": ["ftspeech"], "metrics": ["wer"], "tasks": ["automatic-speech-recognition"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-ftspeech", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "Dan... | saattrupdan/wav2vec2-xls-r-300m-ftspeech | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"da",
"dataset:ftspeech",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:other",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-04T14:53:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #da #dataset-ftspeech #base_model-facebook/wav2vec2-xls-r-300m #license-other #model-index #endpoints_compatible #has_space #region-us
| XLS-R-300m-FTSpeech
===================
Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the FTSpeech dataset, being a dataset of 1,800 hours of transcribed speeches from the Danish parliament.
## Performance
The model achieves the following WER scores (l... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #da #dataset-ftspeech #base_model-facebook/wav2vec2-xls-r-300m #license-other #model-index #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PubMedBert-PubMed200kRCT
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](h... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in paraffin and tested for the presence of abnormal prion protein (PrP)."}], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-ful... | pritamdeka/PubMedBert-PubMed200kRCT | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-04T14:59:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext #license-mit #autotrain_compatible #endpoints_compatible #region-us
| PubMedBert-PubMed200kRCT
========================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the PubMed200kRCT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2833
* Accuracy: 0.8942
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparame... |
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