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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
image-classification | keras |
## Model description
Metric learning aims to measure the similarity among data samples and to learn embedding models. The motivation is to embed inputs in an embedding space such that similar images are close together in that space while dissimilar ones are far away.
The model in this repo is an example which demo... | {"library_name": "keras", "tags": ["image-classification"]} | keras-io/cifar10_metric_learning | null | [
"keras",
"tensorboard",
"image-classification",
"has_space",
"region:us"
] | null | 2022-06-07T16:09:52+00:00 | [] | [] | TAGS
#keras #tensorboard #image-classification #has_space #region-us
| Model description
-----------------
Metric learning aims to measure the similarity among data samples and to learn embedding models. The motivation is to embed inputs in an embedding space such that similar images are close together in that space while dissimilar ones are far away.
The model in this repo is an exam... | [
"### Training hyperparameters\n\n\n\nTraining Metrics\n----------------\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #image-classification #has_space #region-us \n",
"### Training hyperparameters\n\n\n\nTraining Metrics\n----------------\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# risethi/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "risethi/distilbert-base-uncased-finetuned-squad", "results": []}]} | risethi/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T16:18:28+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| risethi/distilbert-base-uncased-finetuned-squad
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9709
* Validation Loss: 1.1167
* Epoch: 1
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11064, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model-facebookptbrlarge
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-portuguese](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "model-facebookptbrlarge", "results": []}]} | Vkt/model-facebookptbrlarge | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T16:48:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| model-facebookptbrlarge
=======================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-portuguese on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2206
* Wer: 0.1322
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
null | null |
<!-- 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. -->
# spanbert-base-cased-prefix-tuning-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-base-cased-prefix-tuning-squad", "results": []}]} | anas-awadalla/spanbert-base-cased-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T16:49:46+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-base-cased-prefix-tuning-squad
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# spanbert-base-cased-prefix-tuning-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-base-cased-prefix-tuning-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... |
text-classification | transformers |
Start from 'DeepPavlov/rubert-base-cased' and finetuning on DUMBOT fake data (http://dumbot.ru/Home/MobileOperatorRate).
100 epoch
on progress...
| {"language": ["ru"], "metrics": [{"loss": 0.704381}, {"accuracy": 1.0}], "widget": [{"text": "[CLS] \u041a\u0430\u043a\u0430\u044f \u0430\u0431\u043e\u043d\u0435\u043d\u0442\u0441\u043a\u0430\u044f \u043f\u043b\u0430\u0442\u0430 \u043d\u0430 \u0442\u0430\u0440\u0438\u0444\u0435 \u041f\u043e\u0437\u0432\u043e\u043d\u043... | Nehc/FakeMobile | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T17:05:08+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #text-classification #ru #autotrain_compatible #endpoints_compatible #region-us
|
Start from 'DeepPavlov/rubert-base-cased' and finetuning on DUMBOT fake data (URL
100 epoch
on progress...
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1448289036171309068/LiGz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jeanswayy/1654627123103/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jeanswayy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T17:21:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
j e a n ️
@jeanswayy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/948537441429803009/NgUot... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/irodori7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T17:27:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
たつき／irodori
@irodori7
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach day. He decides to go to the beach. He gets out on the board. He puts on his swimsuit. He goes to the beach.
Arthur goes to the beach. Arthur is walking on the beach. He notices t... | {} | jppaolim/v60_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T17:32:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach day. He decides to go to the beach. He gets out on the board. He puts on his swimsuit. He goes to the beach.
Arthur goes to the beach. Arthur is walking on the beach. He notices t... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach day. He decides to go to the beach. He gets out on the board. He puts on his swimsuit. He goes to the beach. \nArthur goes to the beach. Arthur is walking on the beach. He n... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach day. He decides to go to the bea... |
null | null |
<!-- 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. -->
# spanbert-large-cased-prefix-tuning-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-large-cased-prefix-tuning-squad", "results": []}]} | anas-awadalla/spanbert-large-cased-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T17:39:20+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-large-cased-prefix-tuning-squad
This model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# spanbert-large-cased-prefix-tuning-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-large-cased-prefix-tuning-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... |
null | null |
<!-- 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-compacter-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-compacter-squad", "results": []}]} | anas-awadalla/bert-base-uncased-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T17:39:24+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-base-uncased-compacter-squad
This model is a fine-tuned version of bert-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
### Training hyp... | [
"# bert-base-uncased-compacter-squad\n\nThis model is a fine-tuned version of bert-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",
"## Training p... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-base-uncased-compacter-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore info... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mariastull/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | mariastull/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T17:44:52+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# kant-gpt2
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on an unknown data... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "kant-gpt2", "results": []}]} | Anjoe/kant-gpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T17:51:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| kant-gpt2
=========
This model is a fine-tuned version of dbmdz/german-gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8022
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n*... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pylemountain/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "pylemountain/distilbert-base-uncased-finetuned-imdb", "results": []}]} | pylemountain/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T17:59:54+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pylemountain/distilbert-base-uncased-finetuned-imdb
===================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8553
* Validation Loss: 2.5640
* Epoch: 0
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | rushic24/neural-machine-translation-model_1 | null | [
"keras",
"region:us"
] | null | 2022-06-07T18:02:00+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | Galeros/dqn-mountaincar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T18:11:49+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | null |
<!-- 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-large-uncased-compacter-squad
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-uncased-compacter-squad", "results": []}]} | anas-awadalla/bert-large-uncased-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T18:12:54+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-large-uncased-compacter-squad
This model is a fine-tuned version of bert-large-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
### Training h... | [
"# bert-large-uncased-compacter-squad\n\nThis model is a fine-tuned version of bert-large-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",
"## Training... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-large-uncased-compacter-squad\n\nThis model is a fine-tuned version of bert-large-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore in... |
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. -->
# test_auto_protocol
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "test_auto_protocol", "results": []}]} | kangaroo927/test_auto_protocol | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T18:24:48+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test_auto_protocol
This model is a fine-tuned version of bert-base-cased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The ... | [
"# test_auto_protocol\n\nThis model is a fine-tuned version of bert-base-cased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test_auto_protocol\n\nThis model is a fine-tuned version of bert-base-cased on the None dataset.",
"## Model description\n\nMore information needed"... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-finetuned-xsum-v3
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/euge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum-v3", "results": []}]} | pranavk/bart-paraphrase-finetuned-xsum-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T18:29:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-finetuned-xsum-v3
=================================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1881
* Rouge1: 99.9251
* Rouge2: 99.9188
* Rougel: 99.9251
* Rougelsum: 99.9251
* Gen Len: 10... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #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\\_batch\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mariastull/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | mariastull/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T18:34:38+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mariastull/q-Taxi-v3-2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | mariastull/q-Taxi-v3-2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T18:37:35+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | Galeros/dqn-mountaincar-v0-opt | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T19:18:53+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | null |
<!-- 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-large-uncased-prefix-tuning-squad
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-la... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-uncased-prefix-tuning-squad", "results": []}]} | anas-awadalla/bert-large-uncased-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T19:30:55+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-large-uncased-prefix-tuning-squad
This model is a fine-tuned version of bert-large-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
### Traini... | [
"# bert-large-uncased-prefix-tuning-squad\n\nThis model is a fine-tuned version of bert-large-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",
"## Trai... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-large-uncased-prefix-tuning-squad\n\nThis model is a fine-tuned version of bert-large-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMor... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510648677995581453/13zo... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jpegmafia/1654634032817/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jpegmafia | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T19:33:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
JPEGMAFIA
@jpegmafia
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1194734625547010048/NB1V... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bladeecity-lil_icebunny/1654634518665/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bladeecity-lil_icebunny | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T19:41:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
JAMES FERRARO & Aim Nothyng
@bladeecity-lil\_icebunny
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B r... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv1-cord-ner
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/lay... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv1-cord-ner", "results": []}]} | renjithks/layoutlmv1-cord-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T19:44:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv1-cord-ner
===================
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1438
* Precision: 0.9336
* Recall: 0.9453
* F1: 0.9394
* Accuracy: 0.9767
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eva... |
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 None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | Anery/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T19:44:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0244
* Precision: 0.7368
* Recall: 0.4
* F1: 0.5185
* Accuracy: 0.9919
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: 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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1331413261070307329/N7du... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/0pn-lil_icebunny/1654634967211/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/0pn-lil_icebunny | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T19:48:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
oneohtrix point never & JAMES FERRARO
@0pn-lil\_icebunny
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-compacter-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sq... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-compacter-squad", "results": []}]} | anas-awadalla/roberta-base-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T20:00:14+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-base-compacter-squad
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# roberta-base-compacter-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-base-compacter-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_review... | ferjeffQ/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T20:31:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2207
* Accuracy: 0.9325
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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\... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-compacter-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-compacter-squad", "results": []}]} | anas-awadalla/roberta-large-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T20:35:02+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-large-compacter-squad
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# roberta-large-compacter-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-large-compacter-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-prefix-tuning-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on th... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-prefix-tuning-squad", "results": []}]} | anas-awadalla/roberta-base-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T21:20:24+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-base-prefix-tuning-squad
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpara... | [
"# roberta-base-prefix-tuning-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedu... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-base-prefix-tuning-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information neede... |
fill-mask | transformers | Modelo para completar tokens en francés | {} | iNceptioN/dummy_model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T21:27:20+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Modelo para completar tokens en francés | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \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. -->
# plncmm/roberta-clinical-wl-es
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://... | {"language": ["es"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "Periodontitis <mask> generalizada severa."}, {"text": "Caries dentinaria <mask>."}, {"text": "Movilidad aumentada en pza <mask>."}, {"text": "Pcte con dm en tto con <mask>."}, {"text": "Pcte con erc en tto con <mask>.... | plncmm/roberta-clinical-wl-es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T21:52:54+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# plncmm/roberta-clinical-wl-es
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the Chilean waiting list dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information need... | [
"# plncmm/roberta-clinical-wl-es\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the Chilean waiting list dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# plncmm/roberta-clinical-wl-es\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the Chilean wait... |
null | null |
<!-- 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. -->
# spanbert-base-cased-compacter-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-base-cased-compacter-squad", "results": []}]} | anas-awadalla/spanbert-base-cased-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T21:59:06+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-base-cased-compacter-squad
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# spanbert-base-cased-compacter-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-base-cased-compacter-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-prefix-tuning-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-prefix-tuning-squad", "results": []}]} | anas-awadalla/roberta-large-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T22:00:31+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-large-prefix-tuning-squad
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
"# roberta-large-prefix-tuning-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-large-prefix-tuning-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... |
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. -->
# plncmm/beto-clinical-wl-es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.c... | {"language": ["es"], "tags": ["generated_from_trainer"], "widget": [{"text": "Periodontitis [MASK] generalizada severa."}, {"text": "Caries dentinaria [MASK]."}, {"text": "Movilidad aumentada en pza [MASK]."}, {"text": "Pcte con dm en tto con [MASK]."}, {"text": "Pcte con erc en tto con [MASK]."}], "base_model": "dccuc... | plncmm/beto-clinical-wl-es | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"es",
"base_model:dccuchile/bert-base-spanish-wwm-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T22:01:54+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #es #base_model-dccuchile/bert-base-spanish-wwm-uncased #autotrain_compatible #endpoints_compatible #region-us
|
# plncmm/beto-clinical-wl-es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the Chilean waiting list dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# plncmm/beto-clinical-wl-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the Chilean waiting list dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #es #base_model-dccuchile/bert-base-spanish-wwm-uncased #autotrain_compatible #endpoints_compatible #region-us \n",
"# plncmm/beto-clinical-wl-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on ... |
null | null |
<!-- 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. -->
# spanbert-large-cased-compacter-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.c... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-large-cased-compacter-squad", "results": []}]} | anas-awadalla/spanbert-large-cased-compacter-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T22:31:21+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-large-cased-compacter-squad
This model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# spanbert-large-cased-compacter-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-large-cased-compacter-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/dwr-elonmusk-maccaw | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T22:37:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Alex MacCaw & Dan Romero
@dwr-elonmusk-maccaw
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach house. He decides to lay out. Arthur wants to lay out on the beach. He puts on his favorite sandals. Arthur lays on the beach.
Arthur goes to the beach. Arthur is walking on a bea... | {} | jppaolim/v61_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T23:20:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach house. He decides to lay out. Arthur wants to lay out on the beach. He puts on his favorite sandals. Arthur lays on the beach.
Arthur goes to the beach. Arthur is walking on a bea... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach house. He decides to lay out. Arthur wants to lay out on the beach. He puts on his favorite sandals. Arthur lays on the beach. \nArthur goes to the beach. Arthur is walking ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach house. He decides to lay out. Ar... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-koquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github... | {"language": "ko", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: 1990\ub144 \uc601\ud654 \u300a <hl> \ub0a... | lmqg/mt5-small-koquad-qg-ae | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"answer extraction",
"ko",
"dataset:lmqg/qg_koquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T23:41:00+00:00 | [
"2210.03992"
] | [
"ko"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-koquad-qg-ae'
===========================================
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language:... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ko\n* Training data: lmqg/qg\\_koquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-smal... |
image-classification | keras |
## Model Description
### Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL)
This repo contains the trained model of [Classification using Attention-based Deep Multiple Instance Learning (MIL)](https://keras.io/examples/vision/attention_mil_classification/).
The full c... | {"library_name": "keras", "tags": ["image-classification"]} | keras-io/attention_mil | null | [
"keras",
"tensorboard",
"image-classification",
"has_space",
"region:us"
] | null | 2022-06-07T23:41:41+00:00 | [] | [] | TAGS
#keras #tensorboard #image-classification #has_space #region-us
|
## Model Description
### Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL)
This repo contains the trained model of Classification using Attention-based Deep Multiple Instance Learning (MIL).
The full credit goes to: Mohamad Jaber
## Intended uses & limitations
- The... | [
"## Model Description",
"### Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL)\n\nThis repo contains the trained model of Classification using Attention-based Deep Multiple Instance Learning (MIL).\n\nThe full credit goes to: Mohamad Jaber",
"## Intended uses & l... | [
"TAGS\n#keras #tensorboard #image-classification #has_space #region-us \n",
"## Model Description",
"### Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL)\n\nThis repo contains the trained model of Classification using Attention-based Deep Multiple Instance Learn... |
feature-extraction | transformers |
# vitB32_bert_ko_small_clip
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) + [lassl/bert-ko-small](https://huggingface.co/lassl/bert-ko-small) CLIP Model
[training code(github)](https://github.com/Bing-su/KoCLIP_training_code)
## Train
SBERT의 [Making Monolingual Sentence Embedd... | {"language": "ko", "license": "mit", "tags": ["clip"]} | Bingsu/vitB32_bert_ko_small_clip | null | [
"transformers",
"pytorch",
"safetensors",
"vision-text-dual-encoder",
"feature-extraction",
"clip",
"ko",
"arxiv:2004.09813",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T23:44:39+00:00 | [
"2004.09813"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #vision-text-dual-encoder #feature-extraction #clip #ko #arxiv-2004.09813 #license-mit #endpoints_compatible #has_space #region-us
|
# vitB32_bert_ko_small_clip
openai/clip-vit-base-patch32 + lassl/bert-ko-small CLIP Model
training code(github)
## Train
SBERT의 Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation를 참고하여, 'openai/clip-vit-base-patch32' 텍스트 모델의 가중치를 'lassl/bert-ko-small'로 복제하였습니다. 논문과는 달리 mean pooling을 사... | [
"# vitB32_bert_ko_small_clip\n\nopenai/clip-vit-base-patch32 + lassl/bert-ko-small CLIP Model\n\ntraining code(github)",
"## Train\n\nSBERT의 Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation를 참고하여, 'openai/clip-vit-base-patch32' 텍스트 모델의 가중치를 'lassl/bert-ko-small'로 복제하였습니다. 논문과는 달리 m... | [
"TAGS\n#transformers #pytorch #safetensors #vision-text-dual-encoder #feature-extraction #clip #ko #arxiv-2004.09813 #license-mit #endpoints_compatible #has_space #region-us \n",
"# vitB32_bert_ko_small_clip\n\nopenai/clip-vit-base-patch32 + lassl/bert-ko-small CLIP Model\n\ntraining code(github)",
"## Train\n\... |
null | null |
<!-- 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-lora-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-lora-squad", "results": []}]} | anas-awadalla/bert-base-uncased-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-08T00:07:11+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-base-uncased-lora-squad
This model is a fine-tuned version of bert-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
### Training hyperpar... | [
"# bert-base-uncased-lora-squad\n\nThis model is a fine-tuned version of bert-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",
"## Training proced... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-base-uncased-lora-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... |
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. -->
# VN_ja-en_byt5_small
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "VN_ja-en_byt5_small", "results": []}]} | twieland/VN_ja-en_byt5_small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T00:50:21+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| VN\_ja-en\_byt5\_small
======================
This model is a fine-tuned version of google/byt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0552
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Train... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
null | null |
<!-- 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-large-uncased-lora-squad
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-uncased-lora-squad", "results": []}]} | anas-awadalla/bert-large-uncased-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-08T01:07:14+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-large-uncased-lora-squad
This model is a fine-tuned version of bert-large-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
### Training hyperp... | [
"# bert-large-uncased-lora-squad\n\nThis model is a fine-tuned version of bert-large-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",
"## Training proc... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-large-uncased-lora-squad\n\nThis model is a fine-tuned version of bert-large-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wa2vec2-5epochs
This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek](https://huggingface.co/lightet... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wa2vec2-5epochs", "results": []}]} | cammy/wa2vec2-5epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T01:23:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wa2vec2-5epochs
===============
This model is a fine-tuned version of lighteternal/wav2vec2-large-xlsr-53-greek on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3049
* Accuracy: 0.9282
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* ... |
null | null | # 这是一个测试模型
language:
- "List of ISO 639-1 code for your language"
- zh
thumbnail: "url to a thumbnail used in social sharing"
tags:
- example
- qbhy
license: "any valid license identifier"
datasets:
- qbhy/dataset-example
metrics:
- metric1 | {} | qbhy/model-example | null | [
"region:us"
] | null | 2022-06-08T01:23:37+00:00 | [] | [] | TAGS
#region-us
| # 这是一个测试模型
language:
- "List of ISO 639-1 code for your language"
- zh
thumbnail: "url to a thumbnail used in social sharing"
tags:
- example
- qbhy
license: "any valid license identifier"
datasets:
- qbhy/dataset-example
metrics:
- metric1 | [
"# 这是一个测试模型\n\nlanguage: \n - \"List of ISO 639-1 code for your language\"\n - zh\nthumbnail: \"url to a thumbnail used in social sharing\"\ntags:\n- example\n- qbhy\nlicense: \"any valid license identifier\"\ndatasets:\n- qbhy/dataset-example\nmetrics:\n- metric1"
] | [
"TAGS\n#region-us \n",
"# 这是一个测试模型\n\nlanguage: \n - \"List of ISO 639-1 code for your language\"\n - zh\nthumbnail: \"url to a thumbnail used in social sharing\"\ntags:\n- example\n- qbhy\nlicense: \"any valid license identifier\"\ndatasets:\n- qbhy/dataset-example\nmetrics:\n- metric1"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1525194520970899457/uqCA... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/_pancagkes/1654655985301/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/_pancagkes | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T01:31:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
carlala
@\_pancagkes
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1328273166599217152/TUO7... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/benny_thejet_11/1654656621512/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/benny_thejet_11 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T01:42:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Benny “The Jet”
@benny\_thejet\_11
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | nice/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T01:44:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5155
* Wer: 0.3388
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
text-generation | transformers |
# Wenzhong2.0-GPT2-3.5B-chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
基于悟道数据集预训练，善于处理NLG任务，目前最大的，中文版的GPT2。
Pretraining on Wudao Corpus, focused on handling NLG tasks, the current largest, Chinese G... | {"language": ["zh"], "license": "apache-2.0", "inference": {"parameters": {"max_new_tokens": 250, "repetition_penalty": 1.1, "top_p": 0.9, "do_sample": true}}} | IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T02:33:50+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Wenzhong2.0-GPT2-3.5B-chinese
=============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
基于悟道数据集预训练，善于处理NLG任务，目前最大的，中文版的GPT2。
Pretraining on Wudao Corpus, focused on handling NLG tasks, the current largest, Chinese GPT2.
模型分类 Model Taxonomy
---... | [
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1350929535454359558/lWAf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/vufewequ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T02:59:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Vu Fewequ
@vufewequ
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# teeth_verify
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"]} | steven123/teeth_verify | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T03:02:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# teeth_verify
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
#### Good Teeth
!Good Teeth
#### Missing Teeth
!Missing Teeth
#### Rotten Teeth
!Rotten Teeth | [
"# teeth_verify\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",
"#### Good Teeth\n\n!Good Teeth",
"#### Missing Teeth\n\n!Missing Teeth",
"#### Rotten Teeth\... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# teeth_verify\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-esquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github... | {"language": "es", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_esquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: del <hl> Ministerio de Desarrollo Urbano ... | lmqg/mt5-small-esquad-qg-ae | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"answer extraction",
"es",
"dataset:lmqg/qg_esquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T03:41:06+00:00 | [
"2210.03992"
] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-esquad-qg-ae'
===========================================
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg\_esquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language:... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: es\n* Training data: lmqg/qg\\_esquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-smal... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-lora-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad d... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-lora-squad", "results": []}]} | anas-awadalla/roberta-base-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-08T03:47:13+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-base-lora-squad
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# roberta-base-lora-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-base-lora-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"##... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-deberta
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Mo... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-deberta", "results": []}]} | ayush1701/my-deberta | null | [
"transformers",
"tf",
"deberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T03:49:00+00:00 | [] | [] | TAGS
#transformers #tf #deberta #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# my-deberta
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# my-deberta\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #tf #deberta #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# my-deberta\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
... |
image-classification | fastai |
# Model card
## Model description
A model trained to classify the material of European plates, as found in the British Museum collection. Initial model was trained using basic fastai workflow with timm integration.
## Intended uses & limitations
Should be able to predict the Material used (as defined by the British ... | {"tags": ["fastai", "image-classification"]} | Kieranm/britishmus_plate_material_classifier | null | [
"fastai",
"image-classification",
"has_space",
"region:us"
] | null | 2022-06-08T04:01:12+00:00 | [] | [] | TAGS
#fastai #image-classification #has_space #region-us
|
# Model card
## Model description
A model trained to classify the material of European plates, as found in the British Museum collection. Initial model was trained using basic fastai workflow with timm integration.
## Intended uses & limitations
Should be able to predict the Material used (as defined by the British ... | [
"# Model card",
"## Model description\nA model trained to classify the material of European plates, as found in the British Museum collection. Initial model was trained using basic fastai workflow with timm integration.",
"## Intended uses & limitations\nShould be able to predict the Material used (as defined b... | [
"TAGS\n#fastai #image-classification #has_space #region-us \n",
"# Model card",
"## Model description\nA model trained to classify the material of European plates, as found in the British Museum collection. Initial model was trained using basic fastai workflow with timm integration.",
"## Intended uses & limi... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-dialogue-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["samsum"], "metrics": ["accuracy"], "pipeline_tag": "summarization", "base_model": "t5-small", "model-index": [{"name": "t5-dialogue-summarization", "results": []}]} | chanifrusydi/t5-dialogue-summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"dataset:samsum",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T04:08:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #dataset-samsum #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-dialogue-summarization
This model is a fine-tuned version of t5-small on the samsum dataset.
dataset:
type: {summarization}
name: {samsum}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# t5-dialogue-summarization\n\nThis model is a fine-tuned version of t5-small on the samsum dataset.\ndataset:\n type: {summarization} \n name: {samsum}",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluat... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #dataset-samsum #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-dialogue-summarization\n\nThis model is a f... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1524432360678342656/TVb2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gnu_amir/1654665822752/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gnu_amir | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T04:21:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ژوپیتر - Amirhossein
@gnu\_amir
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1515664770996715524/UJ44... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/qiamast/1654666925668/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/qiamast | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T04:40:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mahdi
@qiamast
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
Th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-lora-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squa... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-lora-squad", "results": []}]} | anas-awadalla/roberta-large-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-08T04:45:46+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us
|
# roberta-large-lora-squad
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# roberta-large-lora-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-mit #region-us \n",
"# roberta-large-lora-squad\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"... |
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. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_162754.csv___topic_text_google_mt5_base
This model is a fine-tuned version of ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_162754.csv___topic_text_google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_162754.csv___topic_text_google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T04:57:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-data\_prep\_2021\_12\_26\_\_\_t1\_162754.csv\_\_\_topic\_text\_google\_mt5\_base
========================================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following resul... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | epsil/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T05:13:15+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | larryboy825/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T06:26:25+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #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 None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0021
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",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval... |
fill-mask | transformers |
# deberta-base-japanese-unidic
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune `deberta-base-japanese-unidic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic-luw-upos), [dependency-p... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-base-japanese-unidic | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T07:05:32+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-japanese-unidic
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune 'deberta-base-japanese-unidic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
fugashi and unidic-lite are required.
| [
"# deberta-base-japanese-unidic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune 'deberta-base-japanese-unidic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use\n\n\n\nfugashi and unidic-lite ... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-japanese-unidic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fin... |
null | null |
<!-- 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. -->
# spanbert-base-cased-lora-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanB... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-base-cased-lora-squad", "results": []}]} | anas-awadalla/spanbert-base-cased-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-08T07:24:42+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-base-cased-lora-squad
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# spanbert-base-cased-lora-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-base-cased-lora-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information need... |
token-classification | transformers |
# deberta-base-japanese-unidic-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic). Every long-unit-word is tagged by [UPOS](https://u... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/deberta-base-japanese-unidic-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T07:26:25+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-japanese-unidic-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-unidic. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
fugashi and unidic... | [
"# deberta-base-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-unidic. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\n\nor\n\n... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model... |
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. -->
# camembert-base-finetuned-ICDCode_5
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall"], "model-index": [{"name": "camembert-base-finetuned-ICDCode_5", "results": []}]} | louisdeco/camembert-base-finetuned-ICDCode_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T07:47:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-ICDCode\_5
===================================
This model is a fine-tuned version of camembert-base on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict th... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 50\n* eval\\_batch\\_size: 50\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pinku/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | pinku/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-08T07:52:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **CartPole-v1**
This is a trained model of a **DQN** agent playing **CartPole-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | epsil/dqn-s2-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T08:02:06+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing CartPole-v1
This is a trained model of a DQN agent playing CartPole-v1
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 R... | [
"# DQN Agent playing CartPole-v1\nThis is a trained model of a DQN agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Us... | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing CartPole-v1\nThis is a trained model of a DQN agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
text2text-generation | transformers | # Randeng-T5-77M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLT任务，中文版的mT5-small。
Good at handling NLT tasks, Chinese mT5-small.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series ... | {"language": "zh", "license": "apache-2.0", "tags": ["T5", "chinese", "sentencepiece"], "inference": true, "widget": [{"text": "\u5317\u4eac\u6709\u60a0\u4e45\u7684 <extra_id_0>\u548c <extra_id_1>\u3002"}, {"type": "text-generation"}]} | IDEA-CCNL/Randeng-T5-77M | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"T5",
"chinese",
"sentencepiece",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T08:04:03+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #T5 #chinese #sentencepiece #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Randeng-T5-77M
==============
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLT任务，中文版的mT5-small。
Good at handling NLT tasks, Chinese mT5-small.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
----------------------
我们基于mT5-small，训练了它的中文版... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #T5 #chinese #sentencepiece #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# mGENRE
The mGENRE (multilingual Generative ENtity REtrieval) system as presented in [Multilingual Autoregressive Entity Linking](https://arxiv.org/abs/2103.12528) implemented in pytorch.
In a nutshell, mGENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned [mBART](h... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bm", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kg", "kk", "k... | facebook/mgenre-wiki | null | [
"transformers",
"pytorch",
"tf",
"jax",
"mbart",
"text2text-generation",
"retrieval",
"entity-retrieval",
"named-entity-disambiguation",
"entity-disambiguation",
"named-entity-linking",
"entity-linking",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bm",
"bn",
... | null | 2022-06-08T08:25:11+00:00 | [
"2103.12528",
"2001.08210"
] | [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bm",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gn",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"h... | TAGS
#transformers #pytorch #tf #jax #mbart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #multilingual #af #am #ar #as #az #be #bg #bm #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #ff #fi #fr #fy #ga #gd #gl #... |
# mGENRE
The mGENRE (multilingual Generative ENtity REtrieval) system as presented in Multilingual Autoregressive Entity Linking implemented in pytorch.
In a nutshell, mGENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned mBART architecture. GENRE performs retrieval ... | [
"# mGENRE \n\n\nThe mGENRE (multilingual Generative ENtity REtrieval) system as presented in Multilingual Autoregressive Entity Linking implemented in pytorch.\n\nIn a nutshell, mGENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned mBART architecture. GENRE performs re... | [
"TAGS\n#transformers #pytorch #tf #jax #mbart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #multilingual #af #am #ar #as #az #be #bg #bm #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #ff #fi #fr #fy #ga #gd... |
null | null |
<!-- 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. -->
# spanbert-large-cased-lora-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/Spa... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "spanbert-large-cased-lora-squad", "results": []}]} | anas-awadalla/spanbert-large-cased-lora-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-08T08:27:27+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #region-us
|
# spanbert-large-cased-lora-squad
This model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# spanbert-large-cased-lora-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## ... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #region-us \n",
"# spanbert-large-cased-lora-squad\n\nThis model is a fine-tuned version of SpanBERT/spanbert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information ne... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/roberta-base-mlm-electronics-attrs-correction
This model is a fine-tuned version of [ksabeh/roberta-base-mlm-electronics](https... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/roberta-base-mlm-electronics-attrs-correction", "results": []}]} | ksabeh/roberta-base-attribute-correction-mlm | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:38:06+00:00 | [] | [] | TAGS
#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| ksabeh/roberta-base-mlm-electronics-attrs-correction
====================================================
This model is a fine-tuned version of ksabeh/roberta-base-mlm-electronics on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1009
* Validation Loss: 0.0936
* Epoch: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 36848, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDeca... |
text-classification | transformers |
# Quantized-distilbert-banking77
This model is a dynamically quantized version of [optimum/distilbert-base-uncased-finetuned-banking77](https://huggingface.co/optimum/distilbert-base-uncased-finetuned-banking77) on the `banking77` dataset.
The model was created using the [dynamic-quantization](https://github.com/hu... | {"tags": ["optimum"], "datasets": ["banking77"], "metrics": ["accuracy"], "model-index": [{"name": "quantized-distilbert-banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "banking77", "type": "banking77"}, "metrics": [{"type": "accuracy", "value": 0.924... | lewtun/quantized-distilbert-banking77 | null | [
"transformers",
"onnx",
"text-classification",
"optimum",
"dataset:banking77",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:42:56+00:00 | [] | [] | TAGS
#transformers #onnx #text-classification #optimum #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Quantized-distilbert-banking77
==============================
This model is a dynamically quantized version of optimum/distilbert-base-uncased-finetuned-banking77 on the 'banking77' dataset.
The model was created using the dynamic-quantization notebook from a workshop presented at MLOps World 2022.
It achieves th... | [] | [
"TAGS\n#transformers #onnx #text-classification #optimum #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | ## XLS-R-300m-danish
Continued pretraining of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for 120.000 steps on 141.000 hours of speech from Danish radio (DR P1 and Radio24Syv from 2005 to 2021).
The model was pretrained on 16kHz audio using fairseq and should be fine-tuned to ... | {"language": "da", "license": "apache-2.0", "tags": ["speech", "xls_r", "xls_r_pretrained", "danish"]} | chcaa/xls-r-300m-danish | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"xls_r",
"xls_r_pretrained",
"danish",
"da",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:47:32+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #danish #da #license-apache-2.0 #endpoints_compatible #region-us
| ## XLS-R-300m-danish
Continued pretraining of facebook/wav2vec2-xls-r-300m for 120.000 steps on 141.000 hours of speech from Danish radio (DR P1 and Radio24Syv from 2005 to 2021).
The model was pretrained on 16kHz audio using fairseq and should be fine-tuned to perform speech recognition.
A fine-tuned version of ... | [
"## XLS-R-300m-danish\n\nContinued pretraining of facebook/wav2vec2-xls-r-300m for 120.000 steps on 141.000 hours of speech from Danish radio (DR P1 and Radio24Syv from 2005 to 2021). \n\nThe model was pretrained on 16kHz audio using fairseq and should be fine-tuned to perform speech recognition. \n\nA fine-tuned ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #danish #da #license-apache-2.0 #endpoints_compatible #region-us \n",
"## XLS-R-300m-danish\n\nContinued pretraining of facebook/wav2vec2-xls-r-300m for 120.000 steps on 141.000 hours of speech from Danish radio (DR P1 and Radio... |
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. -->
# ff_analysis_3
This model is a fine-tuned version of [zdreiosis/ff_analysis_2](https://huggingface.co/zdreiosis/ff_analysis_2) on... | {"license": "apache-2.0", "tags": ["6th", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "ff_analysis_3", "results": []}]} | zdreiosis/ff_analysis_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"6th",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:49:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #6th #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ff\_analysis\_3
===============
This model is a fine-tuned version of zdreiosis/ff\_analysis\_2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0060
* F1: 1.0
* Roc Auc: 1.0
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #6th #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\\_ba... |
fill-mask | transformers | Domain adaptation is the process of fine-tuning pre-trained language models (PLMs) on domain-specific datasets to produce predictions that are better suited to the new datasets. Here, we re-train the BERT-base-uncased model on an unlabelled COVID-19 fake news dataset (Constraint@AAAI2021) using the masked language mode... | {} | Jawaher/Covid19-fake-news-bert-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:52:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Domain adaptation is the process of fine-tuning pre-trained language models (PLMs) on domain-specific datasets to produce predictions that are better suited to the new datasets. Here, we re-train the BERT-base-uncased model on an unlabelled COVID-19 fake news dataset (Constraint@AAAI2021) using the masked language mode... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# silviacamplani/distilbert-uncase-finetuned-ai-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-uncase-finetuned-ai-ner", "results": []}]} | silviacamplani/distilbert-uncase-finetuned-ai-ner | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:55:39+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-uncase-finetuned-ai-ner
=================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5704
* Validation Loss: 2.5380
* Epoch: 2
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_na... |
text-classification | transformers | Just a simple example
Central definitions
Look at other model cards | {} | Longtong/dummy | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T08:56:03+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Just a simple example
Central definitions
Look at other model cards | [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dai... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm-samsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "ty... | bubblecookie/t5-small-finetuned-cnndm-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T09:21:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm-samsum
===============================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6422
* Rouge1: 24.5996
* Rouge2: 11.817
* Rougel: 20.3346
* Rougelsum: 23.2155
* Gen Len: 18.9999
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4",... | epsil/dqn-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T09:32:02+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BreakoutNoFrameskip-v4
This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach chair. He is walking along the beach when he starts feeling a pain in his back. Arthur rushes to the doctor. The doctor says he needs a special cast. Arthur is so relieved he tears ... | {} | jppaolim/v62_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T09:39:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach chair. He is walking along the beach when he starts feeling a pain in his back. Arthur rushes to the doctor. The doctor says he needs a special cast. Arthur is so relieved he tears ... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach chair. He is walking along the beach when he starts feeling a pain in his back. Arthur rushes to the doctor. The doctor says he needs a special cast. Arthur is so relieved he... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in his beach chair. He is walking along the b... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1447765226376638469/EuvZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/conspiracymill/1654685163989/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/conspiracymill | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T09:44:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Conspiracy Mill
@conspiracymill
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-multilang-cv-ru
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-multilang-cv-ru", "results": []}]} | cutten/wav2vec2-large-multilang-cv-ru | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T10:35:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-multilang-cv-ru
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9734
* Wer: 0.7037
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* t... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | andri/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T10:50:01+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | nikitakapitan/TEST4ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T10:52:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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-evidence-types
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the e... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "BERT-evidence-types", "results": []}]} | marieke93/BERT-evidence-types | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T10:54:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT-evidence-types
===================
This model is a fine-tuned version of bert-base-uncased on the evidence types dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8008
* Macro f1: 0.4227
* Weighted f1: 0.6976
* Accuracy: 0.7154
* Balanced accuracy: 0.3876
Training and evaluation dat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-abc-irish-music-generation
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-abc-irish-music-generation", "results": []}]} | ehcalabres/distilgpt2-abc-irish-music-generation | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T10:55:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# distilgpt2-abc-irish-music-generation
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# distilgpt2-abc-irish-music-generation\n\nThis model is a fine-tuned version of distilgpt2 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",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# distilgpt2-abc-irish-music-generation\n\nThis model is a fine-tuned... |
fill-mask | transformers |
# RoBERTweetTurkCovid (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is a Turkish tweets collection related to COVID-19.
Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenizati... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"]} | ctoraman/RoBERTweetTurkCovid | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T10:59:09+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTweetTurkCovid (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is a Turkish tweets collection related to COVID-19.
Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenizati... | [
"# RoBERTweetTurkCovid (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is a Turkish tweets collection related to COVID-19. \n\nModel architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). T... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTweetTurkCovid (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is a T... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | naveenk903/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-08T11:10:46+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model has apostrophes and hyphens.
The language model is trained on the texts of the Common Voice dataset, which is used during training.
Metri... | {"language": ["uk"], "license": "apache-2.0", "datasets": ["mozilla-foundation/common_voice_10_0"]} | Yehor/wav2vec2-xls-r-300m-uk-with-small-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-08T11:31:06+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model has apostrophes and hyphens.
The language model is trained on the texts of the Common Voice dataset, which is used during training.
Metrics:
Dataset: CV7 (no LM), CER: 0.0432, ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcmc/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | jcmc/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-08T11:41:19+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | transformers |
# Denoising Diffusion Implicit Models (DDIM)
**Paper**: [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502)
**Abstract**:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for man... | {"tags": ["ddim_diffusion"]} | fusing/ddim-celeba-hq | null | [
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T11:42:31+00:00 | [
"2010.02502"
] | [] | TAGS
#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us
|
# Denoising Diffusion Implicit Models (DDIM)
Paper: Denoising Diffusion Implicit Models
Abstract:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate s... | [
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To ac... | [
"TAGS\n#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial... |
null | transformers |
# Denoising Diffusion Implicit Models (DDIM)
**Paper**: [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502)
**Abstract**:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for man... | {"tags": ["ddim_diffusion"]} | fusing/ddim-lsun-bedroom | null | [
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T11:42:50+00:00 | [
"2010.02502"
] | [] | TAGS
#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us
|
# Denoising Diffusion Implicit Models (DDIM)
Paper: Denoising Diffusion Implicit Models
Abstract:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate s... | [
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To ac... | [
"TAGS\n#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial... |
null | transformers |
# Denoising Diffusion Implicit Models (DDIM)
**Paper**: [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502)
**Abstract**:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for man... | {"tags": ["ddim_diffusion"]} | fusing/ddim-lsun-church | null | [
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T11:43:01+00:00 | [
"2010.02502"
] | [] | TAGS
#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us
|
# Denoising Diffusion Implicit Models (DDIM)
Paper: Denoising Diffusion Implicit Models
Abstract:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate s... | [
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To ac... | [
"TAGS\n#transformers #ddim_diffusion #arxiv-2010.02502 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Implicit Models (DDIM)\n\nPaper: Denoising Diffusion Implicit Models\n\nAbstract:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial... |
text-classification | transformers |
#
[Debora Nozza](http://dnozza.github.io/) •
[Federico Bianchi](https://federicobianchi.io/) •
[Giuseppe Attanasio](https://gattanasio.cc/)
# HATE-ITA Base
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hat... | {"language": "it", "license": "gpl-3.0", "tags": ["text classification", "abusive language", "hate speech", "offensive language"], "widget": [{"text": "Ci sono dei bellissimi capibara!", "example_title": "Hate Speech Classification 1"}, {"text": "Sei una testa di cazzo!!", "example_title": "Hate Speech Classification 2... | MilaNLProc/hate-ita | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"text classification",
"abusive language",
"hate speech",
"offensive language",
"it",
"arxiv:2104.12250",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T11:46:39+00:00 | [
"2104.12250"
] | [
"it"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #arxiv-2104.12250 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
Debora Nozza •
Federico Bianchi •
Giuseppe Attanasio
HATE-ITA Base
=============
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="URL width="200">
Abstract
--------
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted proper... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #arxiv-2104.12250 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# Randeng-T5-784M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLT任务，中文版的mT5-large。
Good at handling NLT tasks, Chinese mT5-large.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series ... | {"language": ["zh"], "license": "apache-2.0", "tags": ["T5", "chinese", "sentencepiece"], "inference": true, "widget": [{"text": "\u5317\u4eac\u6709\u60a0\u4e45\u7684 <extra_id_0>\u548c <extra_id_1>\u3002"}, {"type": "text-generation"}]} | IDEA-CCNL/Randeng-T5-784M | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"T5",
"chinese",
"sentencepiece",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T11:49:47+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #T5 #chinese #sentencepiece #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Randeng-T5-784M
===============
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLT任务，中文版的mT5-large。
Good at handling NLT tasks, Chinese mT5-large.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
----------------------
我们基于mT5-large，训练了它的中... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #T5 #chinese #sentencepiece #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | 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. -->
# mT5_multilingual_XLSum-finetuned-en-cnn
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggin... | {"tags": ["summarization", "en", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-en-cnn", "results": []}]} | ahmeddbahaa/mT5_multilingual_XLSum-finetuned-en-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"en",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-08T12:01:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5_multilingual_XLSum-finetuned-en-cnn
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28
## Model des... | [
"# mT5_multilingual_XLSum-finetuned-en-cnn\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0025\n- Rouge-1: 36.87\n- Rouge-2: 15.31\n- Rouge-l: 33.74\n- Gen Len: 77.93\n- Bertscore: 88.28",
... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_multilingual_XLSum-finetuned-en-cnn\n\nThis model is a fi... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-filtered-0608_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-filtered-0608_test", "results": []}]} | YeRyeongLee/bert-base-uncased-finetuned-filtered-0608_test | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T12:05:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-filtered-0608\_test
===============================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1009
* Accuracy: 0.9777
* Precision: 0.9778
* Recall: 0.9777
* F1: 0.9777
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
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