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image-classification | transformers |
# rare-puppers-123
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hug... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/rare-puppers-123 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers-123
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers-123\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
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image-classification | transformers |
# rare-puppers-demo
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hu... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/rare-puppers-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers-demo
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### husky
!husky
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers-demo\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### husky\n\n!husky",
"#### samoyed\n\n!samoyed",
"#### shib... | [
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image-classification | transformers |
# rare-puppers-new-auth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/natera... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/rare-puppers-new-auth | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers-new-auth
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers-new-auth\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
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image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
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"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
image-classification | timm | # Model card for resnet18-random-classifier-123 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/resnet18-random-classifier-123 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for resnet18-random-classifier-123 | [
"# Model card for resnet18-random-classifier-123"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for resnet18-random-classifier-123"
] |
image-classification | timm |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.
It achieves the fo... | {"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["beans"], "library_tag": "timm"} | nateraw/resnet50-beans-dummy-sagemaker | null | [
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us
|
# model
This model is a fine-tuned version of resnet18 on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0219
- Acc1: 56.3910
- Acc5: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
... | [
"# model\n\nThis model is a fine-tuned version of resnet18 on the beans dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0219\n- Acc1: 56.3910\n- Acc5: 100.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Train... | [
"TAGS\n#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us \n",
"# model\n\nThis model is a fine-tuned version of resnet18 on the beans dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0219\n- Acc1: 56.3910\n- Acc5: 100.0",
"## Model d... |
image-classification | timm | # Model card for resnet50-oxford-iiit-pet
 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/resnet50-oxford-iiit-pet | null | [
"timm",
"pytorch",
"image-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #has_space #region-us
| # Model card for resnet50-oxford-iiit-pet
!boxer | [
"# Model card for resnet50-oxford-iiit-pet\n\n!boxer"
] | [
"TAGS\n#timm #pytorch #image-classification #has_space #region-us \n",
"# Model card for resnet50-oxford-iiit-pet\n\n!boxer"
] |
image-classification | transformers |
# Resnet50 Model from Torchvision
## Using the model
```
pip install modelz
```
```python
from modelz import ResnetModel
model = ResnetModel.from_pretrained('nateraw/resnet50')
ex_input = torch.rand(4, 3, 224, 224)
out = model(ex_input)
``` | {"tags": ["image-classification", "pytorch"], "datasets": ["imagenet"]} | nateraw/resnet50 | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"dataset:imagenet",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #resnet #image-classification #dataset-imagenet #endpoints_compatible #has_space #region-us
|
# Resnet50 Model from Torchvision
## Using the model
| [
"# Resnet50 Model from Torchvision",
"## Using the model"
] | [
"TAGS\n#transformers #pytorch #resnet #image-classification #dataset-imagenet #endpoints_compatible #has_space #region-us \n",
"# Resnet50 Model from Torchvision",
"## Using the model"
] |
image-classification | generic |
# Test | {"library_name": "generic", "tags": ["image-classification"]} | nateraw/test-classifier-flash-2 | null | [
"generic",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #image-classification #region-us
|
# Test | [
"# Test"
] | [
"TAGS\n#generic #pytorch #image-classification #region-us \n",
"# Test"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model_a
This model is a fine-tuned version of [lysandre/tiny-vit-random](https://huggingface.co/lysandre/tiny-vit-random) o... | {"tags": ["generated_from_trainer"], "datasets": ["image_folder"], "model_index": [{"name": "test_model_a", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": "default"}}]}]} | nateraw/test_model_a | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #autotrain_compatible #endpoints_compatible #region-us
|
# test_model_a
This model is a fine-tuned version of lysandre/tiny-vit-random on the image_folder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# test_model_a\n\nThis model is a fine-tuned version of lysandre/tiny-vit-random on the image_folder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #autotrain_compatible #endpoints_compatible #region-us \n",
"# test_model_a\n\nThis model is a fine-tuned version of lysandre/tiny-vit-random on the image_folder dataset.",
"## Model description\n\nMore inform... |
text-classification | generic |
# Test | {"library_name": "generic", "tags": ["text-classification"]} | nateraw/text-classification-flash-demo-2 | null | [
"generic",
"pytorch",
"text-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #text-classification #region-us
|
# Test | [
"# Test"
] | [
"TAGS\n#generic #pytorch #text-classification #region-us \n",
"# Test"
] |
image-classification | timm |
# Model card for `timm-resnet50-beans`
**TODO**
**For now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.**
 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/timm-adv_inception_v3 | null | [
"timm",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #image-classification #region-us
|
# Model card for 'timm-resnet50-beans'
TODO
For now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.
!leaf_example | [
"# Model card for 'timm-resnet50-beans'\n\nTODO\n\nFor now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.\n!leaf_example"
] | [
"TAGS\n#timm #image-classification #region-us \n",
"# Model card for 'timm-resnet50-beans'\n\nTODO\n\nFor now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.\n!leaf_example"
] |
image-classification | timm |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# timm-resnet18-beans-test-2
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans datase... | {"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model_index": [{"name": "timm-resnet18-beans-test-2", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}, "dataset": {"name": "beans", "type": "beans", "args": "defaul... | nateraw/timm-resnet18-beans-test-2 | null | [
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us
| timm-resnet18-beans-test-2
==========================
This model is a fine-tuned version of resnet18 on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3225
* Accuracy: 0.5789
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10",
"### Tra... | [
"TAGS\n#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: A... |
image-classification | timm |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# timm-resnet18-beans-test
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.... | {"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model_index": [{"name": "timm-resnet18-beans-test", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}, "dataset": {"name": "beans", "type": "beans", "args": "default"... | nateraw/timm-resnet18-beans-test | null | [
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us
|
# timm-resnet18-beans-test
This model is a fine-tuned version of resnet18 on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2126
- Accuracy: 0.3609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluati... | [
"# timm-resnet18-beans-test\n\nThis model is a fine-tuned version of resnet18 on the beans dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2126\n- Accuracy: 0.3609",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"#... | [
"TAGS\n#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-beans #region-us \n",
"# timm-resnet18-beans-test\n\nThis model is a fine-tuned version of resnet18 on the beans dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2126\n- Accuracy: 0.3609",
"## ... |
image-classification | timm | # Model card for timm-resnet18-imagenette-160px-5-epochs | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/timm-resnet18-imagenette-160px-5-epochs | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for timm-resnet18-imagenette-160px-5-epochs | [
"# Model card for timm-resnet18-imagenette-160px-5-epochs"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for timm-resnet18-imagenette-160px-5-epochs"
] |
image-classification | timm |
# Model card for `timm-resnet50-beans`
**TODO**
**For now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.**
| {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/timm-resnet50-beans | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
|
# Model card for 'timm-resnet50-beans'
TODO
For now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.
!leaf_example
| [
"# Model card for 'timm-resnet50-beans'\n\nTODO\n\nFor now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.\n!leaf_example"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for 'timm-resnet50-beans'\n\nTODO\n\nFor now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.\n!leaf_example"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trainer-rare-puppers
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "trainer-rare-puppers", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}}]}]} | nateraw/trainer-rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| trainer-rare-puppers
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the huggingpics dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation da... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
image-classification | transformers |
A vision transformer finetuned to classify the age of a given person's face.
```python
import requests
from PIL import Image
from io import BytesIO
from transformers import ViTFeatureExtractor, ViTForImageClassification
# Get example image from official fairface repo + read it in as an image
r = requests.get(... | {"tags": ["image-classification", "pytorch"], "datasets": ["fairface"]} | nateraw/vit-age-classifier | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"dataset:fairface",
"doi:10.57967/hf/1259",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #dataset-fairface #doi-10.57967/hf/1259 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
A vision transformer finetuned to classify the age of a given person's face.
| [] | [
"TAGS\n#transformers #pytorch #vit #image-classification #dataset-fairface #doi-10.57967/hf/1259 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | {"license": "apache-2.0", "tags": ["image-classification", "other-image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans-demo-v2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name"... | nateraw/vit-base-beans-demo-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"other-image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans-demo-v2
======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0099
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | {"license": "apache-2.0", "tags": ["image-classification", "other-image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans-demo-v3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name"... | nateraw/vit-base-beans-demo-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"other-image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans-demo-v3
======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0645
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-... | {"license": "apache-2.0", "tags": ["image-classification", "other-image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans-demo", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "... | nateraw/vit-base-beans-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"other-image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans-demo
===================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0853
* Accuracy: 0.9774
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #other-image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer", "image-classification"], "datasets": ["beans"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg", "example_title": "Healthy"}, {"src": "https://huggingface.co/nateraw/vit-b... | nateraw/vit-base-beans | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"en",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-beans
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0942
* Accuracy: 0.9774
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: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-cats-vs-dogs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vi... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "image-classification", "pytorch"], "datasets": ["cats_vs_dogs"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-cats-vs-dogs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cats_vs_do... | nateraw/vit-base-cats-vs-dogs | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cats_vs_dogs",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-cats_vs_dogs #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-cats-vs-dogs
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cats\_vs\_dogs dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0202
* Accuracy: 0.9935
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed\\... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-cats_vs_dogs #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
image-classification | transformers |
# Vision Transformer Fine Tuned on CIFAR10
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and **fine-tuned on CIFAR10** at resolution 224x224.
Check out the code at my [my Github repo](https://github.com/nateraw/huggingface-vit-finetune).
## Usage
```python
from tran... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "pytorch"], "datasets": ["cifar10"], "metrics": ["accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | nateraw/vit-base-patch16-224-cifar10 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"vision",
"dataset:cifar10",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #vision #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer Fine Tuned on CIFAR10
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and fine-tuned on CIFAR10 at resolution 224x224.
Check out the code at my my Github repo.
## Usage
## Model description
The Vision Transformer (ViT) is a transformer encoder ... | [
"# Vision Transformer Fine Tuned on CIFAR10\n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and fine-tuned on CIFAR10 at resolution 224x224.\n\nCheck out the code at my my Github repo.",
"## Usage",
"## Model description\n\nThe Vision Transformer (ViT) is a tran... | [
"TAGS\n#transformers #pytorch #vit #image-classification #vision #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer Fine Tuned on CIFAR10\n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes)... |
image-classification | timm | # Model card for cait_m48_448 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_m48_448 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| # Model card for cait_m48_448 | [
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"# Model card for cait_m48_448"
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image-classification | timm | # Model card for cait_s24_224 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_s24_224 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_s24_224 | [
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"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for cait_s24_224"
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image-classification | timm | # Model card for cait_s24_384 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_s24_384 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_s24_384 | [
"# Model card for cait_s24_384"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for cait_s24_384"
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image-classification | timm | # Model card for cait_s36_384 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_s36_384 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
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image-classification | timm | # Model card for cait_xs24_384 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_xs24_384 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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"TAGS\n#timm #pytorch #image-classification #region-us \n",
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image-classification | timm | # Model card for cait_xxs24_224 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_xxs24_224 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_xxs24_224 | [
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image-classification | timm | # Model card for cait_xxs24_384 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_xxs24_384 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_xxs24_384 | [
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] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
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image-classification | timm | # Model card for cait_xxs36_224 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_xxs36_224 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_xxs36_224 | [
"# Model card for cait_xxs36_224"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for cait_xxs36_224"
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image-classification | timm | # Model card for cait_xxs36_384 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cait_xxs36_384 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cait_xxs36_384 | [
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] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for cait_xxs36_384"
] |
image-classification | timm | # Model card for coat_lite_mini | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/coat_lite_mini | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for coat_lite_mini | [
"# Model card for coat_lite_mini"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for coat_lite_mini"
] |
image-classification | timm | # Model card for coat_lite_small | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/coat_lite_small | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for coat_lite_small | [
"# Model card for coat_lite_small"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for coat_lite_small"
] |
image-classification | timm | # Model card for coat_lite_tiny | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/coat_lite_tiny | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for coat_lite_tiny | [
"# Model card for coat_lite_tiny"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for coat_lite_tiny"
] |
image-classification | timm | # Model card for coat_mini | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/coat_mini | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for coat_mini | [
"# Model card for coat_mini"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for coat_mini"
] |
image-classification | timm | # Model card for coat_tiny | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/coat_tiny | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for coat_tiny | [
"# Model card for coat_tiny"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for coat_tiny"
] |
image-classification | timm | # Model card for convit_base | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/convit_base | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for convit_base | [
"# Model card for convit_base"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for convit_base"
] |
image-classification | timm | # Model card for convit_small | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/convit_small | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for convit_small | [
"# Model card for convit_small"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for convit_small"
] |
image-classification | timm | # Model card for convit_tiny | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/convit_tiny | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for convit_tiny | [
"# Model card for convit_tiny"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for convit_tiny"
] |
image-classification | timm | # Model card for cspdarknet53 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nates-test-org/cspdarknet53 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for cspdarknet53 | [
"# Model card for cspdarknet53"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for cspdarknet53"
] |
feature-extraction | transformers | # BROS
GitHub: https://github.com/clovaai/bros
## Introduction
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br>
Given the OCR results of the document image, which are text and bounding box pairs, it can perform var... | {} | naver-clova-ocr/bros-base-uncased | null | [
"transformers",
"pytorch",
"bros",
"feature-extraction",
"arxiv:2108.04539",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.04539"
] | [] | TAGS
#transformers #pytorch #bros #feature-extraction #arxiv-2108.04539 #endpoints_compatible #has_space #region-us
| BROS
====
GitHub: URL
Introduction
------------
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.
Given the OCR results of the document image, which are text and bounding box pairs, it can perform various key info... | [] | [
"TAGS\n#transformers #pytorch #bros #feature-extraction #arxiv-2108.04539 #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | # BROS
GitHub: https://github.com/clovaai/bros
## Introduction
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br>
Given the OCR results of the document image, which are text and bounding box pairs, it can perform var... | {} | naver-clova-ocr/bros-large-uncased | null | [
"transformers",
"pytorch",
"bros",
"feature-extraction",
"arxiv:2108.04539",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.04539"
] | [] | TAGS
#transformers #pytorch #bros #feature-extraction #arxiv-2108.04539 #endpoints_compatible #region-us
| BROS
====
GitHub: URL
Introduction
------------
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.
Given the OCR results of the document image, which are text and bounding box pairs, it can perform various key info... | [] | [
"TAGS\n#transformers #pytorch #bros #feature-extraction #arxiv-2108.04539 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ## Welcome | {} | navid-rekabsaz/advbert_ranker_l2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| ## Welcome | [
"## Welcome"
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n",
"## Welcome"
] |
feature-extraction | transformers | ## Welcome | {} | navid-rekabsaz/advbert_ranker_l4 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| ## Welcome | [
"## Welcome"
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n",
"## Welcome"
] |
text-generation | transformers | 3 epoch på norsk oscar corpus.
warmup_steps = 1000
learning_rate = 5e-3
block_size =512
per_device_train_batch_size = 64
cirka 1,5 time på TPU v3-8 per epoch | {} | navjordj/gpt2_no | null | [
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"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 3 epoch på norsk oscar corpus.
warmup_steps = 1000
learning_rate = 5e-3
block_size =512
per_device_train_batch_size = 64
cirka 1,5 time på TPU v3-8 per epoch | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# navid_test_bert
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "navid_test_bert", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics"... | navsad/navid_test_bert | null | [
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| navid\_test\_bert
=================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8149
* Matthews Correlation: 0.5834
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",
"### Training... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
sentence-similarity | sentence-transformers |
# All MPNet base model (v2) for Semantic Search
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you ... | {"language": "en", "license": "mit", "tags": ["feature-extraction", "sentence-similarity", "sentence-transformers"], "pipeline_tag": "sentence-similarity"} | navteca/all-mpnet-base-v2 | null | [
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"arxiv:1704.05179",
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"license:mit",
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"region:us"
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| All MPNet base model (v2) for Semantic Search
=============================================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
----------------------... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sent... | [
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"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. Please ... |
zero-shot-classification | transformers | # Bart large model for NLI-based Zero Shot Text Classification
This model uses [bart-large](https://huggingface.co/facebook/bart-large).
## Training Data
This model was trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset in the manner originally described in [Yin et al. 2019](https://a... | {"language": "en", "license": "mit", "tags": ["bart", "zero-shot-classification"], "datasets": ["multi_nli"], "pipeline_tag": "zero-shot-classification"} | navteca/bart-large-mnli | null | [
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"en",
"dataset:multi_nli",
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.00161"
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"en"
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#transformers #pytorch #jax #bart #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Bart large model for NLI-based Zero Shot Text Classification
This model uses bart-large.
## Training Data
This model was trained on the MultiNLI (MNLI) dataset in the manner originally described in Yin et al. 2019.
It can be used to predict whether a topic label can be assigned to a given sequence, whether or not ... | [
"# Bart large model for NLI-based Zero Shot Text Classification\n\nThis model uses bart-large.",
"## Training Data\nThis model was trained on the MultiNLI (MNLI) dataset in the manner originally described in Yin et al. 2019.\n\nIt can be used to predict whether a topic label can be assigned to a given sequence, w... | [
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"# Bart large model for NLI-based Zero Shot Text Classification\n\nThis model uses bart-large.",
"## Training ... |
question-answering | transformers | # Electra base model for QA (SQuAD 2.0)
This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator).
## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
It can be used for question answering task.
## Usage and Performanc... | {"language": "en", "license": "mit", "tags": ["electra", "question-answering"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering"} | navteca/electra-base-squad2 | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #electra #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| # Electra base model for QA (SQuAD 2.0)
This model uses electra-base.
## Training Data
The models have been trained on the SQuAD 2.0 dataset.
It can be used for question answering task.
## Usage and Performance
The trained model can be used like this:
| [
"# Electra base model for QA (SQuAD 2.0)\n\nThis model uses electra-base.",
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sentence-similarity | sentence-transformers |
# Multi QA MPNet base model for Semantic Search
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.
This model uses [... | {"language": "en", "license": "mit", "tags": ["feature-extraction", "sentence-similarity", "sentence-transformers"], "pipeline_tag": "sentence-similarity"} | navteca/multi-qa-mpnet-base-cos-v1 | null | [
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"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #license-mit #endpoints_compatible #region-us
| Multi QA MPNet base model for Semantic Search
=============================================
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.
T... | [] | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #license-mit #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
This model uses [roberta-base](https://huggingface.co/roberta-base).
## Training Data
This mode... | {"language": "en", "license": "mit", "tags": ["roberta", "text-classification"], "datasets": ["quora"], "pipeline_tag": "text-classification"} | navteca/quora-roberta-base | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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#transformers #pytorch #jax #roberta #text-classification #en #dataset-quora #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
This model uses roberta-base.
## Training Data
This model was trained on the Quora Duplicate Questions dataset.
The model will predict a score between 0 and 1: How likely the two given que... | [
"# Cross-Encoder for Quora Duplicate Questions Detection\n\nThis model was trained using SentenceTransformers Cross-Encoder class.\n\nThis model uses roberta-base.",
"## Training Data\n\nThis model was trained on the Quora Duplicate Questions dataset.\n\nThe model will predict a score between 0 and 1: How likely ... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
This model uses [roberta-large](https://huggingface.co/roberta-large).
## Training Data
This mo... | {"language": "en", "license": "mit", "tags": ["roberta", "text-classification"], "datasets": ["quora"], "pipeline_tag": "text-classification"} | navteca/quora-roberta-large | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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#transformers #pytorch #jax #roberta #text-classification #en #dataset-quora #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
This model uses roberta-large.
## Training Data
This model was trained on the Quora Duplicate Questions dataset.
The model will predict a score between 0 and 1: How likely the two given qu... | [
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question-answering | transformers | # Roberta base model for QA (SQuAD 2.0)
This model uses [roberta-base](https://huggingface.co/roberta-base).
## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
It can be used for question answering task.
## Usage and Performance
The trained model c... | {"language": "en", "license": "mit", "tags": ["roberta", "question-answering"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering"} | navteca/roberta-base-squad2 | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| # Roberta base model for QA (SQuAD 2.0)
This model uses roberta-base.
## Training Data
The models have been trained on the SQuAD 2.0 dataset.
It can be used for question answering task.
## Usage and Performance
The trained model can be used like this:
| [
"# Roberta base model for QA (SQuAD 2.0)\n\nThis model uses roberta-base.",
"## Training Data\nThe models have been trained on the SQuAD 2.0 dataset.\n\nIt can be used for question answering task.",
"## Usage and Performance\nThe trained model can be used like this:"
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"## Training Data\nThe models have been trained on the SQuAD 2.0 dataset.\n\nIt can be used for que... |
question-answering | transformers | # Roberta large model for QA (SQuAD 2.0)
This model uses [roberta-large](https://huggingface.co/roberta-large).
## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
It can be used for question answering task.
## Usage and Performance
The trained mode... | {"language": "en", "license": "mit", "tags": ["roberta", "question-answering"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering"} | navteca/roberta-large-squad2 | null | [
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#transformers #pytorch #jax #roberta #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us
| # Roberta large model for QA (SQuAD 2.0)
This model uses roberta-large.
## Training Data
The models have been trained on the SQuAD 2.0 dataset.
It can be used for question answering task.
## Usage and Performance
The trained model can be used like this:
| [
"# Roberta large model for QA (SQuAD 2.0)\n\nThis model uses roberta-large.",
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"## Training Data\nThe models have been trained on the SQuAD 2.0 dataset.\n\nIt can be... |
table-question-answering | transformers |
# TAPAS large model fine-tuned on WikiTable Questions (WTQ)
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why... | {"language": "en", "license": "mit", "tags": ["tapas", "table-question-answering"], "datasets": ["sqa", "wikisql", "wtq"], "pipeline_tag": "table-question-answering"} | navteca/tapas-large-finetuned-wtq | null | [
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"license:mit",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tapas #table-question-answering #en #dataset-sqa #dataset-wikisql #dataset-wtq #arxiv-2004.02349 #license-mit #endpoints_compatible #has_space #region-us
|
# TAPAS large model fine-tuned on WikiTable Questions (WTQ)
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why... | [
"# TAPAS large model fine-tuned on WikiTable Questions (WTQ)\n\nTAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which ... | [
"TAGS\n#transformers #pytorch #tapas #table-question-answering #en #dataset-sqa #dataset-wikisql #dataset-wtq #arxiv-2004.02349 #license-mit #endpoints_compatible #has_space #region-us \n",
"# TAPAS large model fine-tuned on WikiTable Questions (WTQ)\n\nTAPAS is a BERT-like transformers model pretrained on a larg... |
text-classification | transformers | # FlauBERT finetuned on French cooking recipes
This model is finetuned on a sequence classification task that associates each sequence with the appropriate recipe category.
### How to use it?
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassifica... | {"language": "fr", "tags": ["text-classification", "flaubert", "french", "flaubert-base-uncased"], "widget": [{"text": "Lasagnes \u00e0 la bolognaise"}]} | nbouali/flaubert-base-uncased-finetuned-cooking | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #text-classification #french #flaubert-base-uncased #fr #autotrain_compatible #endpoints_compatible #region-us
| FlauBERT finetuned on French cooking recipes
============================================
This model is finetuned on a sequence classification task that associates each sequence with the appropriate recipe category.
### How to use it?
### Label encoding:
>
> If you would like to know more about this ... | [
"### How to use it?",
"### Label encoding:\n\n\n\n \n\n \n\n\n> \n> If you would like to know more about this model you can refer to our blog post\n> \n> \n>"
] | [
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"### How to use it?",
"### Label encoding:\n\n\n\n \n\n \n\n\n> \n> If you would like to know more about this model you can refer to our blog post\n> \n> ... |
text-classification | transformers | # Model Card for ESG-BERT
Domain Specific BERT Model for Text Mining in Sustainable Investing
# Model Details
## Model Description
- **Developed by:** [Mukut Mukherjee](https://www.linkedin.com/in/mukutm/), [Charan Pothireddi](https://www.linkedin.com/in/sree-charan-pothireddi-6a0a3587/) and [Parabole.ai]... | {"language": ["en"], "widget": [{"text": "In fiscal year 2019, we reduced our comprehensive carbon footprint for the fourth consecutive year\u2014down 35 percent compared to 2015, when Apple\u2019s carbon emissions peaked, even as net revenue increased by 11 percent over that same period. In the past year, we avoided o... | nbroad/ESG-BERT | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Model Card for ESG-BERT
Domain Specific BERT Model for Text Mining in Sustainable Investing
# Model Details
## Model Description
- Developed by: Mukut Mukherjee, Charan Pothireddi and URL
- Shared by [Optional]: HuggingFace
- Model type: Language model
- Language(s) (NLP): en
- License: More information... | [
"# Model Card for ESG-BERT\nDomain Specific BERT Model for Text Mining in Sustainable Investing",
"# Model Details",
"## Model Description\n \n \n \n- Developed by: Mukut Mukherjee, Charan Pothireddi and URL\n- Shared by [Optional]: HuggingFace\n- Model type: Language model\n- Language(s) (NLP): en\n- License: ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for ESG-BERT\nDomain Specific BERT Model for Text Mining in Sustainable Investing",
"# Model Details",
"## Model Description\n \n \n ... |
question-answering | transformers |
# DeBERTa v3 xsmall SQuAD 2.0
[Microsoft reports that this model can get 84.8/82.0](https://huggingface.co/microsoft/deberta-v3-xsmall#fine-tuning-on-nlu-tasks) on f1/em on the dev set.
I got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show... | {"language": "en", "license": "cc-by-4.0", "tags": ["question-answering"], "datasets": ["squad_v2"], "metrics": ["f1", "exact"], "widget": [{"context": "DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majo... | nbroad/deberta-v3-xsmall-squad2 | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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#transformers #pytorch #tensorboard #deberta-v2 #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us
|
# DeBERTa v3 xsmall SQuAD 2.0
Microsoft reports that this model can get 84.8/82.0 on f1/em on the dev set.
I got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show the results on the official script soon.
| [
"# DeBERTa v3 xsmall SQuAD 2.0\n\nMicrosoft reports that this model can get 84.8/82.0 on f1/em on the dev set. \n\nI got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show the results on the official script soon."
] | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us \n",
"# DeBERTa v3 xsmall SQuAD 2.0\n\nMicrosoft reports that this model can get 84.8/82.0 on f1/em on the dev set. \n\nI got 81.5/78.3 but I only did o... |
text2text-generation | transformers | Model not mine.
Taken from here https://github.com/PANXiao1994/mRASP2 | {} | nbroad/mrasp2-6e6d-no-mono | null | [
"transformers",
"fsmt",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #fsmt #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Model not mine.
Taken from here URL | [] | [
"TAGS\n#transformers #fsmt #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | # Multi-lingual Question Generating Model (mt5-base)
Give the model a passage and it will generate a question about the passage.
## Trained on the following datasets:
- [SQuAD (English)](https://rajpurkar.github.io/SQuAD-explorer/)
- [TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, ... | {"language": ["en", "hi", "de", "ar", "bn", "fi", "ja", "zh", "id", "sw", "ta", "gr", "ru", "es", "th", "tr", "vi", "multilingual"], "datasets": ["squad_v2", "tydiqa", "mlqa", "xquad", "germanquad"], "widget": [{"text": "Hugging Face has seen rapid growth in its popularity since the get-go. It is definitely doing the r... | nbroad/mt5-base-qgen | null | [
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Give the model a passage and it will generate a question about the passage.
## Trained on the following datasets:
- SQuAD (English)
- TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, Russian, Telugu, Thai)
- MLQA (Arabic, Chinese, ... | [
"# Multi-lingual Question Generating Model (mt5-base)\nGive the model a passage and it will generate a question about the passage.",
"## Trained on the following datasets:\n\n- SQuAD (English)\n- TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, Russian, Telugu, Thai)\n- MLQA (Arabi... | [
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text2text-generation | transformers | # Multi-lingual Question Generating Model (mt5-small)
Give the model a passage and it will generate a question about the passage.
## Trained on the following datasets:
- [SQuAD (English)](https://rajpurkar.github.io/SQuAD-explorer/)
- [TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean,... | {"language": ["en", "hi", "de", "ar", "bn", "fi", "ja", "zh", "id", "sw", "ta", "gr", "ru", "es", "th", "tr", "vi", "multilingual"], "datasets": ["squad_v2", "tydiqa", "mlqa", "xquad", "germanquad"], "widget": [{"text": "Hugging Face has seen rapid growth in its popularity since the get-go. It is definitely doing the r... | nbroad/mt5-small-qgen | null | [
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"da... | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
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Give the model a passage and it will generate a question about the passage.
## Trained on the following datasets:
- SQuAD (English)
- TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, Russian, Telugu, Thai)
- MLQA (Arabic, Chinese,... | [
"# Multi-lingual Question Generating Model (mt5-small)\nGive the model a passage and it will generate a question about the passage.",
"## Trained on the following datasets:\n\n- SQuAD (English)\n- TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, Russian, Telugu, Thai)\n- MLQA (Arab... | [
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fill-mask | transformers | I put MuRIL base in BigBird.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | {} | nbroad/muril-bigbird-base | null | [
"transformers",
"jax",
"tensorboard",
"big_bird",
"fill-mask",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| I put MuRIL base in BigBird.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | [] | [
"TAGS\n#transformers #jax #tensorboard #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | MAKE SURE TO PULL THE MODEL AT CHECKPOINT 90000 UNTIL I FIGURE OUT HOW TO FIX THE GIT HISTORY
git checkout 57f0ef792a759c022b83433c7a26df52f3da3608
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | {} | nbroad/muril-bigbird-large-1k | null | [
"transformers",
"jax",
"tensorboard",
"big_bird",
"fill-mask",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| MAKE SURE TO PULL THE MODEL AT CHECKPOINT 90000 UNTIL I FIGURE OUT HOW TO FIX THE GIT HISTORY
git checkout 57f0ef792a759c022b83433c7a26df52f3da3608
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | [] | [
"TAGS\n#transformers #jax #tensorboard #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
xtremedistil-l12-h384 trained on SQuAD 2.0
"eval_exact": 75.45691906005221
"eval_f1": 79.32502968532793 | {"tags": ["question-answering"], "datasets": ["squad_v2"], "metrics": ["f1", "exact"], "widget": [{"context": "While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings.... | nbroad/xdistil-l12-h384-squad2 | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #tensorboard #bert #question-answering #dataset-squad_v2 #model-index #endpoints_compatible #has_space #region-us
|
xtremedistil-l12-h384 trained on SQuAD 2.0
"eval_exact": 75.45691906005221
"eval_f1": 79.32502968532793 | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #bert #question-answering #dataset-squad_v2 #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. -->
# results
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-ca... | {"tags": ["generated_from_trainer"], "datasets": ["epi_classify4_gard"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "epi_classify4_gard", "type": "epi_classify4_gard", ... | ncats/EpiClassify4GARD | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-epi_classify4_gard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# results
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986
## Model description
More information needed
## Intended u... | [
"# results\n\nThis model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0541\n- Precision: 0.875\n- Recall: 0.9032\n- F1: 0.8889\n- Accuracy: 0.986",
"## Model description\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-epi_classify4_gard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# results\n\nThis model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.\n... |
token-classification | transformers | ## Model description
**EpiExtract4GARD** is a fine-tuned [BioBERT-base-cased](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) model that is ready to use for **Named Entity Recognition** of locations (LOC), epidemiologic types (EPI), and epidemiologic rates (STAT). This model was fine-tuned on [EpiSet4NER](http... | {} | ncats/EpiExtract4GARD-v1 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Model description
-----------------
EpiExtract4GARD is a fine-tuned BioBERT-base-cased model that is ready to use for Named Entity Recognition of locations (LOC), epidemiologic types (EPI), and epidemiologic rates (STAT). This model was fine-tuned on EpiSet4NER for epidemiological information from rare disease abstra... | [
"#### How to use\n\n\nYou can use this model with the Hosted inference API to the right with this test sentence: \"27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births.\"\n\n\nSee code below for use with Transformers *pipeline* for NER.:\n\n\n\n```\nfrom transf... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with the Hosted inference API to the right with this test sentence: \"27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-20... |
token-classification | transformers |
## DOCUMENTATION UPDATES IN PROGRESS
## Model description
**EpiExtract4GARD-v2** is a fine-tuned [BioBERT-base-cased](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) model that is ready to use for **Named Entity Recognition** of locations (LOC), epidemiologic types (EPI), and epidemiologic rates ... | {"language": ["en"], "license": "other", "tags": ["token-classification", "ncats"], "datasets": ["ncats/EpiSet4NER"], "widget": [{"text": "27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births.", "example_title": "Named Entity Recognition Ex. 1"}, {"text": "A retros... | ncats/EpiExtract4GARD-v2 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #ncats #en #dataset-ncats/EpiSet4NER #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| DOCUMENTATION UPDATES IN PROGRESS
---------------------------------
Model description
-----------------
EpiExtract4GARD-v2 is a fine-tuned BioBERT-base-cased model that is ready to use for Named Entity Recognition of locations (LOC), epidemiologic types (EPI), and epidemiologic rates (STAT). This model was fine-tun... | [
"#### How to use\n\n\nYou can use this model with the Hosted inference API to the right with this test sentence: \"27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births.\"\n\n\nSee code below for use with Transformers *pipeline* for NER.:\n\n\n\n```\nfrom transf... | [
"TAGS\n#transformers #pytorch #bert #token-classification #ncats #en #dataset-ncats/EpiSet4NER #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with the Hosted inference API to the right with this test sentence: \"27 pa... |
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-cased-finetuned-emotion
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["f1"], "model-index": [{"name": "bert-base-cased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"... | ncduy/bert-base-cased-finetuned-emotion | null | [
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-cased-finetuned-emotion
=================================
This model is a fine-tuned version of bert-base-cased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1342
* F1: 0.9365
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "bert-base-cased-wikitext2", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | ncduy/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8565
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### 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 #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "model_index": [{"name": "bert-base-uncased-finetuned-swag", "results": [{"dataset": {"name": "swag", "type": "swag", "args": "regular"}}]}]} | ncduy/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6189
- eval_accuracy: 0.7647
- eval_runtime: 274.5502
- eval_samples_per_second: 72.868
- eval_steps_per_second: 4.557
- epoch: 1.0
... | [
"# bert-base-uncased-finetuned-swag\n\nThis model is a fine-tuned version of bert-base-uncased on the swag dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6189\n- eval_accuracy: 0.7647\n- eval_runtime: 274.5502\n- eval_samples_per_second: 72.868\n- eval_steps_per_second: 4.557\n- ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-swag\n\nThis model is a fine-tuned version of bert-base-uncased on the swag dataset.\nIt achieves the following results o... |
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | ncduy/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0590
* Precision: 0.9311
* Recall: 0.9500
* F1: 0.9404
* Accuracy: 0.9865
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-distilled-squad-finetuned-squad-small
This model is a fine-tuned version of [distilbert-base-cased-distill... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-cased-distilled-squad-finetuned-squad-small", "results": []}]} | ncduy/distilbert-base-cased-distilled-squad-finetuned-squad-small | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-cased-distilled-squad-finetuned-squad-small
This model is a fine-tuned version of distilbert-base-cased-distilled-squad on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information n... | [
"# distilbert-base-cased-distilled-squad-finetuned-squad-small\n\nThis model is a fine-tuned version of distilbert-base-cased-distilled-squad on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-cased-distilled-squad-finetuned-squad-small\n\nThis model is a fine-tuned version of distilbert-base-cased-distilled-squad ... |
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": []}]} | ncduy/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 | 2022-03-02T23:29:05+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: 2.4718
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... |
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... | ncduy/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: 0.0612
* Precision: 0.9270
* Recall: 0.9377
* F1: 0.9323
* Accuracy: 0.9840
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... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following ... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "gpt2-wikitext2", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ncduy/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.3114
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | ncduy/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
- Bleu: 52.8691
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8558\n- Bleu: 52.8691",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model_index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}}]}]} | ncduy/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and ev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #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\\_s... |
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. -->
# opus-mt-en-vi-full-finetuned-en-to-vi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-vi-full-finetuned-en-to-vi", "results": []}]} | ncduy/opus-mt-en-vi-full-finetuned-en-to-vi | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-vi-full-finetuned-en-to-vi
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# opus-mt-en-vi-full-finetuned-en-to-vi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-vi-full-finetuned-en-to-vi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset.",
"## Model desc... |
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. -->
# opus-mt-en-vi-own-finetuned-en-to-vi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/H... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-vi-own-finetuned-en-to-vi", "results": []}]} | ncduy/opus-mt-en-vi-own-finetuned-en-to-vi | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-vi-own-finetuned-en-to-vi
====================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.4416
* Bleu: 2.1189
* Gen Len: 25.153
Model description
-----------------
More info... | [
"### 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
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. -->
# phobert-large-finetuned-vietnamese_students_feedback
This model is a fine-tuned version of [vinai/phobert-large](https://hugging... | {"tags": ["generated_from_trainer"], "datasets": ["vietnamese_students_feedback"], "metrics": ["accuracy"], "model-index": [{"name": "phobert-large-finetuned-vietnamese_students_feedback", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "vietnamese_students_feedb... | ncduy/phobert-large-finetuned-vietnamese_students_feedback | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:vietnamese_students_feedback",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-vietnamese_students_feedback #model-index #autotrain_compatible #endpoints_compatible #region-us
| phobert-large-finetuned-vietnamese\_students\_feedback
======================================================
This model is a fine-tuned version of vinai/phobert-large on the vietnamese\_students\_feedback dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2285
* Accuracy: 0.9463
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-vietnamese_students_feedback #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-... |
null | transformers | #@title
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 290.76 +/- 18.71
name: mean_reward
task:
type: reinforcement-learning
... | {} | ncduy/ppo-LunarLander-v2 | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| #@title
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 290.76 +/- 18.71
name: mean_reward
task:
type: reinforcement-learning
... | [
"# {name_of_your_repo}\n\nThis is a pre-trained model of a {algo} agent playing {environment} using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like this:",
"... | [
"TAGS\n#transformers #endpoints_compatible #region-us \n",
"# {name_of_your_repo}\n\nThis is a pre-trained model of a {algo} agent playing {environment} using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 i... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-squad2-distilled-finetuned-chaii-small
This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2-d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-squad2-distilled-finetuned-chaii-small", "results": []}]} | ncduy/xlm-roberta-base-squad2-distilled-finetuned-chaii-small | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
|
# xlm-roberta-base-squad2-distilled-finetuned-chaii-small
This model is a fine-tuned version of deepset/xlm-roberta-base-squad2-distilled on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ne... | [
"# xlm-roberta-base-squad2-distilled-finetuned-chaii-small\n\nThis model is a fine-tuned version of deepset/xlm-roberta-base-squad2-distilled on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"# xlm-roberta-base-squad2-distilled-finetuned-chaii-small\n\nThis model is a fine-tuned version of deepset/xlm-roberta-base-squad2-distilled on the None dataset."... |
text-classification | transformers | hello | {} | nchervyakov/super-model | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
## Finetuned DialoGPT model on Spanish Conversations
This model was finetuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model on subtitles from Spanish movies and telenovelas from the awesome [OpenSubtitle dataset](https://github.com/PolyAI-LDN/conversational-datasets/tree/... | {"license": "mit", "tags": ["conversational"]} | ncoop57/DiGPTame-medium | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Finetuned DialoGPT model on Spanish Conversations
-------------------------------------------------
This model was finetuned from the original DialoGPT-medium model on subtitles from Spanish movies and telenovelas from the awesome OpenSubtitle dataset.
DialoGPT paper: URL
Some example dialog from the model:
Us... | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# ncoop57/athena
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ncoop57/athena | null | [
"sentence-transformers",
"pytorch",
"longformer",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #longformer #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ncoop57/athena
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can... | [
"# ncoop57/athena\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #longformer #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ncoop57/athena\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic... |
summarization | transformers |
## ncoop57/bart-base-code-summarizer-java-v0
| {"license": "mit", "tags": ["summarization"]} | ncoop57/bart-base-code-summarizer-java-v0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## ncoop57/bart-base-code-summarizer-java-v0
| [
"## ncoop57/bart-base-code-summarizer-java-v0"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## ncoop57/bart-base-code-summarizer-java-v0"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ncoop57/codeformer-java | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic sear... |
text-generation | transformers |
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 125M was trained on the Pil... | {"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["The Pile"]} | ncoop57/codeparrot-neo-125M-py | null | [
"transformers",
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"text generation",
"causal-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #gpt_neo #text-generation #text generation #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 125M was trained on the Pil... | [
"# GPT-Neo 125M",
"## Model Description\n\nGPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.",
"## Training data\n\nGPT-Neo 125M was tr... | [
"TAGS\n#transformers #pytorch #jax #rust #gpt_neo #text-generation #text generation #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT-Neo 125M",
"## Model Description\n\nGPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 a... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | negfir/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2200
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | nehamj/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | nepalprabin/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0442
* Mae: 0.5385
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ChemBERTa_drug_state_classification
This model is a fine-tuned version of [nepp1d0/ChemBERTa_drug_state_classification](https://... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "ChemBERTa_drug_state_classification", "results": []}]} | nepp1d0/ChemBERTa_drug_state_classification | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| ChemBERTa\_drug\_state\_classification
======================================
This model is a fine-tuned version of nepp1d0/ChemBERTa\_drug\_state\_classification on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0463
* Accuracy: 0.9870
Model description
-----------------
M... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eva... |
null | null | Tokenizer trained on BindingDB SMILES encodings.
Trained on 1008081 samples with one blank space after each character in the SMILES string | {} | nepp1d0/SMILES_tokenizer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Tokenizer trained on BindingDB SMILES encodings.
Trained on 1008081 samples with one blank space after each character in the SMILES string | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# BERTimbau Base (aka "bert-base-portuguese-cased")
## Introduction
BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Simil... | {"language": "pt", "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["brWaC"]} | neuralmind/bert-base-portuguese-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #pt #dataset-brWaC #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERTimbau Base (aka "bert-base-portuguese-cased")
=================================================
!Bert holding a berimbau
Introduction
------------
BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognit... | [
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use our work, please cite:"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #pt #dataset-brWaC #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use our work, please cite:"
] |
fill-mask | transformers |
# BERTimbau Large (aka "bert-large-portuguese-cased")
## Introduction
BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Si... | {"language": "pt", "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["brWaC"]} | neuralmind/bert-large-portuguese-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #pt #dataset-brWaC #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERTimbau Large (aka "bert-large-portuguese-cased")
===================================================
!Bert holding a berimbau
Introduction
------------
BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Rec... | [
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use our work, please cite:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #pt #dataset-brWaC #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use our work, please cite:"
] |
text-classification | transformers |
# 🤗 + neuraly - Italian BERT Sentiment model
## Model description
This model performs sentiment analysis on Italian sentences. It was trained starting from an instance of [bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased), and fine-tuned on an Italian dataset of tweets, reaching 8... | {"language": "it", "license": "mit", "tags": ["sentiment", "Italian"], "thumbnail": "https://neuraly.ai/static/assets/images/huggingface/thumbnail.png", "widget": [{"text": "Huggingface \u00e8 un team fantastico!"}]} | neuraly/bert-base-italian-cased-sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"sentiment",
"Italian",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #sentiment #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# + neuraly - Italian BERT Sentiment model
## Model description
This model performs sentiment analysis on Italian sentences. It was trained starting from an instance of bert-base-italian-cased, and fine-tuned on an Italian dataset of tweets, reaching 82% of accuracy on the latter one.
## Intended uses & li... | [
"# + neuraly - Italian BERT Sentiment model",
"## Model description \n \nThis model performs sentiment analysis on Italian sentences. It was trained starting from an instance of bert-base-italian-cased, and fine-tuned on an Italian dataset of tweets, reaching 82% of accuracy on the latter one.",
"## Intended... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #sentiment #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# + neuraly - Italian BERT Sentiment model",
"## Model description \n \nThis model performs sentiment analysis on Italian sentences. It was train... |
fill-mask | transformers |
# IsRoBERTa a RoBERTa-like masked language model
Probably the first icelandic transformer language model!
## Overview
**Language:** Icelandic
**Downstream-task:** masked-lm
**Training data:** OSCAR corpus
**Code:** See [here](https://github.com/neurocode-io/icelandic-language-model)
**Infrastructure**: 1x Nvidi... | {"language": "is", "datasets": ["Icelandic portion of the OSCAR corpus from INRIA", "oscar"]} | neurocode/IsRoBERTa | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"is",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #is #autotrain_compatible #endpoints_compatible #region-us
|
# IsRoBERTa a RoBERTa-like masked language model
Probably the first icelandic transformer language model!
## Overview
Language: Icelandic
Downstream-task: masked-lm
Training data: OSCAR corpus
Code: See here
Infrastructure: 1x Nvidia K80
## Hyperparameters
## Usage
### In Transformers
## Authors
Bobby... | [
"# IsRoBERTa a RoBERTa-like masked language model\n\nProbably the first icelandic transformer language model!",
"## Overview\nLanguage: Icelandic \nDownstream-task: masked-lm \nTraining data: OSCAR corpus \nCode: See here\nInfrastructure: 1x Nvidia K80",
"## Hyperparameters",
"## Usage",
"### In Transform... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #is #autotrain_compatible #endpoints_compatible #region-us \n",
"# IsRoBERTa a RoBERTa-like masked language model\n\nProbably the first icelandic transformer language model!",
"## Overview\nLanguage: Icelandic \nDownstream-task: masked-lm \nTraining data: ... |
text-classification | transformers |
# sahajBERT News Article Classification
## Model description
[sahajBERT](https://huggingface.co/neuropark/sahajBERT) fine-tuned for news article classification using the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue).
The model is trained for classifying articles into 5 different classes... | {"language": "bn", "license": "apache-2.0", "tags": ["collaborative", "bengali", "SequenceClassification"], "datasets": "IndicGlue", "metrics": ["Loss", "Accuracy", "Precision", "Recall"], "widget": [{"text": "\u098f\u09b6\u09bf\u09df\u09be\u09df \u09aa\u09cd\u09b0\u09a5\u09ae \u09a6\u09c3\u09b7\u09cd\u099f\u09bf\u09b9... | neuropark/sahajBERT-NCC | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"collaborative",
"bengali",
"SequenceClassification",
"bn",
"dataset:IndicGlue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #albert #text-classification #collaborative #bengali #SequenceClassification #bn #dataset-IndicGlue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sahajBERT News Article Classification
=====================================
Model description
-----------------
sahajBERT fine-tuned for news article classification using the 'URL' split of IndicGlue.
The model is trained for classifying articles into 5 different classes:
Intended uses & limitations
----------... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for Sequence Classification:",
"#### Limitations and bias\n\n\nWIP\n\n\nTraining data\n-------------\n\n\nThe model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the 'URL' split of IndicGlue.\n\n\nTraining p... | [
"TAGS\n#transformers #pytorch #albert #text-classification #collaborative #bengali #SequenceClassification #bn #dataset-IndicGlue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly with a pipeline for Sequence Classification:",
"... |
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