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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
summarization | transformers |
# Randeng-Pegasus-523M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理摘要任务的，中文版的PAGASUS-large。
Good at solving text summarization tasks, Chinese PAGASUS-large.
## 模型分类 Model Taxonomy
| 需求 Dem... | {"language": "zh", "tags": ["summarization"], "inference": false} | IDEA-CCNL/Randeng-Pegasus-523M-Chinese | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"zh",
"arxiv:1912.08777",
"arxiv:2209.02970",
"autotrain_compatible",
"region:us"
] | null | 2022-06-09T10:51:35+00:00 | [
"1912.08777",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #region-us
| Randeng-Pegasus-523M-Chinese
============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理摘要任务的，中文版的PAGASUS-large。
Good at solving text summarization tasks, Chinese PAGASUS-large.
模型分类 Model Taxonomy
-------------------
模型信息 Model Informati... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #region-us \n"
] |
summarization | transformers |
# Randeng-Pegasus-238M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理摘要任务的，中文版的PAGASUS-base。
Good at solving text summarization tasks, Chinese PAGASUS-base.
## 模型分类 Model Taxonomy
| 需求 Deman... | {"language": "zh", "tags": ["summarization", "chinese"], "inference": false} | IDEA-CCNL/Randeng-Pegasus-238M-Chinese | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"chinese",
"zh",
"arxiv:1912.08777",
"arxiv:2209.02970",
"autotrain_compatible",
"region:us"
] | null | 2022-06-09T11:01:26+00:00 | [
"1912.08777",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #chinese #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #region-us
| Randeng-Pegasus-238M-Chinese
============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理摘要任务的，中文版的PAGASUS-base。
Good at solving text summarization tasks, Chinese PAGASUS-base.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #chinese #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #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="RalphX1/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"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": ... | RalphX1/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-09T11:02:58+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 |
#Samuel Rodrigues from Metal Gear Rising DialoGPT Model | {"tags": ["conversational"]} | Cirilaron/DialoGPT-medium-jetstreamsam | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T11:07:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Samuel Rodrigues from Metal Gear Rising DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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="RalphX1/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 +/... | RalphX1/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-09T11:21:02+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"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065
## Validation Metrics
- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.707708700088... | {"language": "en", "tags": "autotrain", "datasets": ["qualitydatalab/autotrain-data-car-review-project"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.061185706621337065} | qualitydatalab/autotrain-car-review-project-966432120 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:qualitydatalab/autotrain-data-car-review-project",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T11:30:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065
## Validation Metrics
- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.707708700088... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 966432120\n- CO2 Emissions (in grams): 0.061185706621337065",
"## Validation Metrics\n\n- Loss: 0.6066656112670898\n- Accuracy: 0.724822695035461\n- Macro F1: 0.7077087000886584\n- Micro F1: 0.7248226950354609\n- Weighted ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 966432120\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176
## Validation Metrics
- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939... | {"language": "en", "tags": "autotrain", "datasets": ["qualitydatalab/autotrain-data-car-review-project"], "widget": [{"text": "I love driving this car"}], "co2_eq_emissions": 0.21529888368377176} | qualitydatalab/autotrain-car-review-project-966432121 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:qualitydatalab/autotrain-data-car-review-project",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-09T11:30:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176
## Validation Metrics
- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 966432121\n- CO2 Emissions (in grams): 0.21529888368377176",
"## Validation Metrics\n\n- Loss: 0.6013365983963013\n- Accuracy: 0.737791286727457\n- Macro F1: 0.729171012281939\n- Micro F1: 0.737791286727457\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: ... |
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/1533553176619716608/4klY... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bbclaurakt/1654778894531/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bbclaurakt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T11:47:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Laura Kuenssberg Translator
@bbclaurakt
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.
Trainin... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CTRL-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [sshleifer/tiny-ctrl](https://huggingface.co/sshle... | {"tags": ["generated_from_trainer"], "datasets": "cmotions/Beatles_lyrics", "model-index": [{"name": "CTRL-Beatles-Lyrics-finetuned-newlyrics", "results": []}]} | wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics | null | [
"transformers",
"pytorch",
"tensorboard",
"ctrl",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T11:53:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #ctrl #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #autotrain_compatible #endpoints_compatible #region-us
| CTRL-Beatles-Lyrics-finetuned-newlyrics
=======================================
This model is a fine-tuned version of sshleifer/tiny-ctrl on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #ctrl #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #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\... |
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/1521723273922461696/m8_z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/zaidalyafeai/1654779787447/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/zaidalyafeai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T12:02:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Zaid زيد
@zaidalyafeai
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"
] |
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... | moodlep/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-09T12:23:24+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 | 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="i8pxgd2s/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 +/... | i8pxgd2s/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-09T12:26:40+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"
] |
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. -->
# MIX1_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MIX1_ja-en_helsinki", "results": []}]} | twieland/MIX1_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T12:37:39+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MIX1\_ja-en\_helsinki
=====================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on a combination of Visual Novel, Light Novel, and Subtitle data. A total of ~10MM lines of training data were used.
It achieves the following results on the evaluation set:
* Loss: 1.7947
* Otaku Benchmark ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64... |
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_fine_tuned_sentiment_sst3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on t... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta_fine_tuned_sentiment_sst3", "results": []}]} | RogerKam/roberta_fine_tuned_sentiment_sst3 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T12:42:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta_fine_tuned_sentiment_sst3
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0373
- Accuracy: 0.7729
- F1 Score: 0.7726
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# roberta_fine_tuned_sentiment_sst3\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0373\n- Accuracy: 0.7729\n- F1 Score: 0.7726",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_fine_tuned_sentiment_sst3\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following resul... |
automatic-speech-recognition | transformers |
# xls-r-300m-danish-nst-cv9
This is a version of [alvenir/wav2vec2-base-da](https://huggingface.co/Alvenir/wav2vec2-base-da) finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.
The ... | {"language": "da", "license": "apache-2.0", "tags": ["speech-to-text"], "datasets": ["common-voice-9", "nst"]} | chcaa/alvenir-wav2vec2-base-da-nst-cv9 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech-to-text",
"da",
"dataset:common-voice-9",
"dataset:nst",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T12:42:46+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-danish-nst-cv9
=========================
This is a version of alvenir/wav2vec2-base-da finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.
The model was trained using fair... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
## DistilDNA model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using DistilBERT technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thes... | {"license": "mit", "tags": ["DNA"]} | Peltarion/dnabert-distilbert | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"DNA",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T12:43:06+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #DNA #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## DistilDNA model
This is a distilled version of DNABERT by using DistilBERT technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report.
## How to Use
The model can... | [
"## DistilDNA model\n\nThis is a distilled version of DNABERT by using DistilBERT technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report.",
"## How to Use \n\nTh... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #DNA #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## DistilDNA model\n\nThis is a distilled version of DNABERT by using DistilBERT technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA se... |
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. -->
# GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": "cmotions/Beatles_lyrics", "model-index": [{"name": "GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics", "results": []}]} | wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-09T12:51:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
===============================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.
Model description
-----------------
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: 8\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 #gpt_neo #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
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_copy | null | [
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:07:12+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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-filtered-0609
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) o... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-filtered-0609", "results": []}]} | YeRyeongLee/roberta-base-finetuned-filtered-0609 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:14:27+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-filtered-0609
====================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1343
* Accuracy: 0.9824
* Precision: 0.9824
* Recall: 0.9824
* F1: 0.9824
Model description
-------... | [
"### 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 #roberta #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: 5e-05\n* train\\_batch\\_size: 8\n* eval\... |
text-generation | transformers |
# GPT-Medium-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## Model description
... | {"tags": ["generated_from_trainer"], "datasets": "cmotions/Beatles_lyrics", "model-index": [{"name": "GPT-Medium-Beatles-Lyrics-finetuned-newlyrics", "results": []}]} | wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T13:18:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT-Medium-Beatles-Lyrics-finetuned-newlyrics
=============================================
This model is a fine-tuned version of gpt2-medium on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 4\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 #dataset-cmotions/Beatles_lyrics #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
null | null |
This is a test
| {"license": "cc-by-nc-sa-4.0"} | Tuglat/test | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-09T13:19:14+00:00 | [] | [] | TAGS
#license-cc-by-nc-sa-4.0 #region-us
|
This is a test
| [] | [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
# xls-r-300m-danish-nst-cv9
This is a version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.... | {"language": ["da"], "license": "apache-2.0", "tags": ["speech-to-text", "hf-asr-leaderboard"], "datasets": ["common-voice-9", "nst"], "model-index": [{"name": "xls-r-300m-nst-cv9-da", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voic... | chcaa/xls-r-300m-nst-cv9-da | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech-to-text",
"hf-asr-leaderboard",
"da",
"dataset:common-voice-9",
"dataset:nst",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:20:24+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #hf-asr-leaderboard #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #model-index #endpoints_compatible #region-us
| xls-r-300m-danish-nst-cv9
=========================
This is a version of facebook/wav2vec2-xls-r-300m finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.
The model was trained using ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #hf-asr-leaderboard #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
null | null | # A collection of functions for use with Sieve of Eratosthenes
This is an internal package to use with Sieve of Eratosthenes.
## Example
The example below creates a simple web server based on a `sieve`.
```go
// The Server
func main() {
go func() {
log.Println("Listening...")
server := sieve.New... | {"license": "bsd-3-clause-clear"} | impzdpwer/Sieve_of_Eratosthenes | null | [
"license:bsd-3-clause-clear",
"region:us"
] | null | 2022-06-09T13:23:21+00:00 | [] | [] | TAGS
#license-bsd-3-clause-clear #region-us
| A collection of functions for use with Sieve of Eratosthenes
============================================================
This is an internal package to use with Sieve of Eratosthenes.
Example
-------
The example below creates a simple web server based on a 'sieve'.
Running this program will start a web server ... | [
"### Methods",
"#### Methods of the Session type\n\n\nName: 'Context() \\*Context', Type: 'func(\\*Session)', Description: Returns the 'Context' associated with the session.\nName: 'Close()', Type: 'func()', Description: Closes the session.\nName: 'SetUUID(uuid string)', Type: 'func()', Description: Sets the UUID... | [
"TAGS\n#license-bsd-3-clause-clear #region-us \n",
"### Methods",
"#### Methods of the Session type\n\n\nName: 'Context() \\*Context', Type: 'func(\\*Session)', Description: Returns the 'Context' associated with the session.\nName: 'Close()', Type: 'func()', Description: Closes the session.\nName: 'SetUUID(uuid... |
automatic-speech-recognition | transformers |
# Norwegian Wav2Vec2 Model - 1B Nynorsk
This model is finetuned on top of feature extractor [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-1b) from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the langu... | {"language": ["nn", false], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", false, "nn", "nb-NN"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-1b-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "da... | NbAiLab/nb-wav2vec2-1b-nynorsk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"no",
"nn",
"nb-NN",
"dataset:NbAiLab/NPSC",
"arxiv:2307.01672",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:37:24+00:00 | [
"2307.01672"
] | [
"nn",
"no"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #no #nn #nb-NN #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Norwegian Wav2Vec2 Model - 1B Nynorsk
=====================================
This model is finetuned on top of feature extractor XLS-R from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
* W... | [
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #no #nn #nb-NN #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will ... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras", "tags": ["computer-vision", "classification", "multiple-instance-learning "]} | buio/attention_mil_classification | null | [
"keras",
"tensorboard",
"computer-vision",
"classification",
"multiple-instance-learning ",
"has_space",
"region:us"
] | null | 2022-06-09T13:46:43+00:00 | [] | [] | TAGS
#keras #tensorboard #computer-vision #classification #multiple-instance-learning #has_space #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #computer-vision #classification #multiple-instance-learning #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.... |
sentence-similarity | sentence-transformers |
# S-PubMedBert-MedQuAD
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | TimKond/S-PubMedBert-MedQuAD | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:50:47+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-mit #endpoints_compatible #region-us
|
# S-PubMedBert-MedQuAD
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then y... | [
"# S-PubMedBert-MedQuAD\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-mit #endpoints_compatible #region-us \n",
"# S-PubMedBert-MedQuAD\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks ... |
text-classification | transformers |
## Model description
A question type classification model based on multilingual BERT.
The question type classifier takes as input the question, and returns a label that distinguishes between boolean and short answer extractive questions.
The model was initialized with [bert-base-multilingual-cased](https://hugging... | {"license": "apache-2.0"} | PrimeQA/tydiqa-boolean-question-classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1810.04805",
"arxiv:2206.08441",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T13:54:52+00:00 | [
"1810.04805",
"2206.08441"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #arxiv-2206.08441 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
A question type classification model based on multilingual BERT.
The question type classifier takes as input the question, and returns a label that distinguishes between boolean and short answer extractive questions.
The model was initialized with bert-base-multilingual-cased and fine-tuned on... | [
"## Model description\n\nA question type classification model based on multilingual BERT.\n\nThe question type classifier takes as input the question, and returns a label that distinguishes between boolean and short answer extractive questions. \n\nThe model was initialized with bert-base-multilingual-cased and fin... | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #arxiv-2206.08441 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\nA question type classification model based on multilingual BERT.\n\nThe question type classifier takes as input the ... |
null | transformers |
## MiniDNA model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thesis rep... | {"license": "mit", "tags": ["DNA"]} | Peltarion/dnabert-minilm | null | [
"transformers",
"pytorch",
"bert",
"DNA",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T14:02:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us
|
## MiniDNA model
This is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..
## How to Use
The model can be u... | [
"## MiniDNA model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..",
"## How to Use \n\nThe mod... | [
"TAGS\n#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us \n",
"## MiniDNA model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training... |
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", "tags": ["computer-vision", "generative", "variational-autoencoder", "vq-vae"]} | buio/vq-vae | null | [
"keras",
"computer-vision",
"generative",
"variational-autoencoder",
"vq-vae",
"region:us"
] | null | 2022-06-09T14:04:32+00:00 | [] | [] | TAGS
#keras #computer-vision #generative #variational-autoencoder #vq-vae #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 #computer-vision #generative #variational-autoencoder #vq-vae #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\... |
null | transformers |
## MiniDNA small model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thes... | {"license": "mit", "tags": ["DNA"]} | Peltarion/dnabert-minilm-small | null | [
"transformers",
"pytorch",
"bert",
"DNA",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T14:07:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us
|
## MiniDNA small model
This is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..
## How to Use
The model ca... | [
"## MiniDNA small model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..",
"## How to Use \n\nT... | [
"TAGS\n#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us \n",
"## MiniDNA small model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-tr... |
null | transformers |
## MiniDNA mini model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 3 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thesi... | {"license": "mit", "tags": ["DNA"]} | Peltarion/dnabert-minilm-mini | null | [
"transformers",
"pytorch",
"bert",
"DNA",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T14:13:29+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us
|
## MiniDNA mini model
This is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 3 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..
## How to Use
The model can... | [
"## MiniDNA mini model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 3 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..",
"## How to Use \n\nTh... | [
"TAGS\n#transformers #pytorch #bert #DNA #license-mit #endpoints_compatible #region-us \n",
"## MiniDNA mini model\n\nThis is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 3 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-tra... |
text-classification | transformers |
# CAP_coded_UK_statutory_instruments
This model predicts the CAP code of parliamentary bills/instruments (https://www.comparativeagendas.net/pages/master-codebook)
The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament.
The model is cased (case... | {"tags": ["generated_from_keras_callback"], "widget": [{"text": "The National Health Service (Charges for Drugs and Appliances) (Scotland) Regulations 2007", "example_title": "example 1"}, {"text": "The Inshore Fishing (Prohibited Methods of Fishing) (Luce Bay) Order 2015", "example_title": "example 2"}], "model-index"... | z-dickson/CAP_coded_UK_statutory_instruments | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T14:18:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| CAP\_coded\_UK\_statutory\_instruments
======================================
This model predicts the CAP code of parliamentary bills/instruments (URL
The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament.
The model is cased (case sensitive)
A... | [
"### Training results",
"### Framework versions\n\n\n* Transformers 4.19.2\n* TensorFlow 2.8.2\n* Datasets 2.2.2\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.19.2\n* TensorFlow 2.8.2\n* Datasets 2.2.2\n* Tokenizers 0.12.1"
] |
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/1484686785812832263/Beh-... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elrichmc/1654790629445/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elrichmc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T15:01:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ElRichMC
@elrichmc
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"
] |
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... | XGBooster/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-09T15:03:00+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... |
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/994592419705274369/RLplF... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mrbeast | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T15:11:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
MrBeast
@mrbeast
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/1511431988720414730/A1kq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/sorcehri/1654791699329/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/sorcehri | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T15:20:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ehri
@sorcehri
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"
] |
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... | YaYaB/SpaceInvadersNoFrameskip-v4-1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-09T15:23:40+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... |
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/1401919208133378050/l2MK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/medscape/1654792218439/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/medscape | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T15:29:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Medscape
@medscape
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"
] |
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. -->
# NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned
This model is a fine-tuned version of [ber... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned", "results": []}]} | ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T15:33:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| NLP-CIC-WFU\_Clinical\_Cases\_NER\_Sents\_Tokenized\_bertin\_roberta\_base\_spanish\_fine\_tuned
================================================================================================
This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on the None dataset.
It achieves the follow... | [
"### 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: 7",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-cc-by-4.0 #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\\_batc... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Ts
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ts](https://huggingface.co/Helsinki-NLP/opus-mt-en-ts) on t... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Ts", "results": []}]} | kabelomalapane/En-Ts | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T15:33:13+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Ts
=====
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ts on the None dataset.
It achieves the following results on the evaluation set:
Before training:
* Loss: 3.17
* Bleu: 14.513
After Training
* Loss: 1.3320
* Bleu: 36.7687
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: 32\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: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
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. -->
# TEdetection_distiBERT_NER_V4
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_NER_V4", "results": []}]} | FritzOS/TEdetection_distiBERT_NER_V4 | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T15:36:37+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distiBERT_NER_V4
This model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3 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... | [
"# TEdetection_distiBERT_NER_V4\n\nThis model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3 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 nee... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distiBERT_NER_V4\n\nThis model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3 on an unknown dataset... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# veb/twitch-distilbert-base-uncased-finetuned-sst-2-english
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-ss... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "veb/twitch-distilbert-base-uncased-finetuned-sst-2-english", "results": []}]} | veb/twitch-distilbert-base-uncased-finetuned-sst-2-english | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T15:58:37+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| veb/twitch-distilbert-base-uncased-finetuned-sst-2-english
==========================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3074
* Train Spa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | flood/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:06:01+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1612
* F1: 0.8618
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
## Model description
An answer classification model for boolean questions based on XLM-RoBERTa.
The answer classifier takes as input a boolean question and a passage, and returns a label (yes, no-answer, no).
The model was initialized with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and fine-tun... | {"license": "apache-2.0"} | PrimeQA/tydiqa-boolean-answer-classifier | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:2112.07772",
"arxiv:2206.08441",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:11:51+00:00 | [
"2112.07772",
"2206.08441"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #arxiv-2112.07772 #arxiv-2206.08441 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
An answer classification model for boolean questions based on XLM-RoBERTa.
The answer classifier takes as input a boolean question and a passage, and returns a label (yes, no-answer, no).
The model was initialized with xlm-roberta-large and fine-tuned on the boolean questions from TyDiQA, as ... | [
"## Model description\n\nAn answer classification model for boolean questions based on XLM-RoBERTa.\n\nThe answer classifier takes as input a boolean question and a passage, and returns a label (yes, no-answer, no). \n\nThe model was initialized with xlm-roberta-large and fine-tuned on the boolean questions from T... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #arxiv-2112.07772 #arxiv-2206.08441 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\nAn answer classification model for boolean questions based on XLM-RoBERTa.\n\nThe answer classifier takes as ... |
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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | flood/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:14:32+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2794
* F1: 0.8376
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | flood/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:20:20+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2527
* F1: 0.8086
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# german-poetry-gpt2-large
This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-la... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "german-poetry-gpt2-large", "results": []}]} | Anjoe/german-poetry-gpt2-large | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T16:24:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# german-poetry-gpt2-large
This model is a fine-tuned version of benjamin/gerpt2-large on German poems.
It achieves the following results on the evaluation set:
- eval_loss: 3.5753
- eval_runtime: 100.7173
- eval_samples_per_second: 51.6
- eval_steps_per_second: 25.805
- epoch: 4.0
- step: 95544
## Model descripti... | [
"# german-poetry-gpt2-large\n\nThis model is a fine-tuned version of benjamin/gerpt2-large on German poems.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.5753\n- eval_runtime: 100.7173\n- eval_samples_per_second: 51.6\n- eval_steps_per_second: 25.805\n- epoch: 4.0\n- step: 95544",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# german-poetry-gpt2-large\n\nThis model is a fine-tuned version of benjamin/gerpt2-large on German poems.\nIt... |
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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | flood/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:25:36+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4025
* F1: 0.6778
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | flood/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T16:31:22+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1739
* F1: 0.8525
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-brooks-or-dunn
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-brooks-or-dunn", "results": []}]} | q2-jlbar/segformer-b0-finetuned-brooks-or-dunn | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T17:20:04+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-brooks-or-dunn
=====================================
This model is a fine-tuned version of nvidia/mit-b0 on the q2-jlbar/BrooksOrDunn dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1158
* Mean Iou: nan
* Mean Accuracy: nan
* Overall Accuracy: nan
* Per Category Io... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 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: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #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: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batc... |
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/1526668354609680384/r85f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/midudev/1654800505422/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/midudev | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T17:33:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
EN DIRECTO URL
@midudev
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"
] |
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="6001k1d/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"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": ... | 6001k1d/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-09T17:34:20+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 | 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="6001k1d/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 +/... | 6001k1d/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-09T17:48:35+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"
] |
null | transformers |
## LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
[LingMess](https://arxiv.org/abs/2205.12644) is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly i... | {"language": ["en"], "license": "mit", "tags": ["coreference-resolution"], "datasets": ["ontonotes"], "metrics": ["CoNLL"], "task_categories": ["coreference-resolution"], "model-index": [{"name": "biu-nlp/lingmess-coref", "results": [{"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "datase... | biu-nlp/lingmess-coref | null | [
"transformers",
"pytorch",
"longformer",
"coreference-resolution",
"en",
"dataset:ontonotes",
"arxiv:2205.12644",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-09T18:05:32+00:00 | [
"2205.12644"
] | [
"en"
] | TAGS
#transformers #pytorch #longformer #coreference-resolution #en #dataset-ontonotes #arxiv-2205.12644 #license-mit #model-index #endpoints_compatible #has_space #region-us
| LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
---------------------------------------------------------------------------------
LingMess is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring funct... | [
"#### Training on OntoNotes\n\n\nWe present the test results on OntoNotes 5.0 dataset.\n\n\n\nIf you find LingMess useful for your work, please cite the following paper:"
] | [
"TAGS\n#transformers #pytorch #longformer #coreference-resolution #en #dataset-ontonotes #arxiv-2205.12644 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"#### Training on OntoNotes\n\n\nWe present the test results on OntoNotes 5.0 dataset.\n\n\n\nIf you find LingMess useful for your w... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM)
This repository provides all the necessary... | {"language": "dar", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["Dvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-dvoice-darija | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"dar",
"dataset:Dvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-09T18:09:28+00:00 | [] | [
"dar"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-Dvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a DVoice Darija dataset within
SpeechBrain. For a bet... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-Dvoice #license-apache-2.0 #has_space #region-us \n"
] |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
This repository provides all the necessar... | {"language": "sw", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["Dvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-dvoice-swahili | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"sw",
"dataset:Dvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-09T18:09:42+00:00 | [] | [
"sw"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #sw #dataset-Dvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a DVoice-VoxLingua107 Swahili dataset within
Speech... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #sw #dataset-Dvoice #license-apache-2.0 #has_space #region-us \n"
] |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM)
This repository provides all the necessary ... | {"language": "fon", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["Dvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-dvoice-fongbe | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"fon",
"dataset:Dvoice",
"license:apache-2.0",
"region:us"
] | null | 2022-06-09T18:10:08+00:00 | [] | [
"fon"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #fon #dataset-Dvoice #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Fongbe dataset within
SpeechBrain. For a bett... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #fon #dataset-Dvoice #license-apache-2.0 #region-us \n"
] |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM)
This repository provides all the necessary... | {"language": "dar", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["Dvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-dvoice-amharic | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"dar",
"dataset:Dvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-09T18:10:24+00:00 | [] | [
"dar"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-Dvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM)
================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Amharic dataset within
SpeechBrain. For a b... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #dar #dataset-Dvoice #license-apache-2.0 #has_space #region-us \n"
] |
text-classification | transformers |
This model predicts the issue category of US Congressional bills.
The model is trained on ~250k US Congressional bills from 1950-2015.
The issue coding scheme follows the Comparative Agenda Project: https://www.comparativeagendas.net/pages/master-codebook
The model is cased (case sensitive)
Any questions on the mo... | {"tags": ["generated_from_keras_callback"], "widget": [{"text": "A bill to prohibt discrimination in employment because of race, color, religion, national origin, or ancestry", "example_title": "example 1"}, {"text": "A bill to require the promulgation of regulations to improve aviation safety in adverse weather condit... | z-dickson/CAP_coded_US_Congressional_bills | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T19:06:53+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
This model predicts the issue category of US Congressional bills.
The model is trained on ~250k US Congressional bills from 1950-2015.
The issue coding scheme follows the Comparative Agenda Project: URL
The model is cased (case sensitive)
Any questions on the model and training data feel free to message me on twit... | [
"### Training hyperparameters",
"### Framework versions\n\n- Transformers 4.19.3\n- TensorFlow 2.8.2\n- Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters",
"### Framework versions\n\n- Transformers 4.19.3\n- TensorFlow 2.8.2\n- Tokenizers 0.12.1"
] |
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. -->
# ner_marathi_bert
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multiling... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "model-index": [{"name": "ner_marathi_bert", "results": []}]} | lakshaywadhwa1993/ner_marathi_bert | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T20:00:19+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ner\_marathi\_bert
==================
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3606
* Overall Precision: 0.8939
* Overall Recall: 0.9030
* Overall F1: 0.8984
* Overall Accuracy: 0.9347
* Loc F1: 0.88... | [
"### 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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #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... |
sentence-similarity | sentence-transformers |
# nthakur/contriever-base-msmarco
This is a port of the [Contriever MSMARCO Model](https://huggingface.co/facebook/contriever-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic s... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | nthakur/contriever-base-msmarco | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-09T20:50:15+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# nthakur/contriever-base-msmarco
This is a port of the Contriever MSMARCO Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | [
"# nthakur/contriever-base-msmarco\n\nThis is a port of the Contriever MSMARCO Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes ... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# nthakur/contriever-base-msmarco\n\nThis is a port of the Contriever MSMARCO Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensio... |
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... | pm390/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-09T21:02:36+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... |
null | null |
The pretrained model (pruned_transducer_stateless4) in https://github.com/k2-fsa/icefall/pull/380
### training
```
#!/usr/bin/env bash
set -x
K2_ROOT=/path/to/k2
ICEFALL=/path/to/icefall
export PYTHONPATH=$K2_ROOT/k2/python:$PYTHONPATH
export PYTHONPATH=$K2_ROOT/build/lib:$PYTHONPATH
export PYTHONPATH=$ICEFALL:$... | {"license": "apache-2.0"} | pkufool/icefall-asr-librispeech-pruned-stateless-streaming-conformer-rnnt4-2022-06-10 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-09T21:50:20+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
|
The pretrained model (pruned_transducer_stateless4) in URL
### training
### decoding
#### simulate streaming
#### streaming
### export
for URL
for cpu_jit.pt
| [
"### training",
"### decoding",
"#### simulate streaming",
"#### streaming",
"### export\n\nfor URL\n\nfor cpu_jit.pt"
] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n",
"### training",
"### decoding",
"#### simulate streaming",
"#### streaming",
"### export\n\nfor URL\n\nfor cpu_jit.pt"
] |
null | transformers |
Latent Diffusion
**Paper**: [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)
**Abstract**:
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image da... | {"license": "mit", "tags": ["diffusion"]} | fusing/latent-diffusion-text2im-large | null | [
"transformers",
"pytorch",
"ldmbert",
"diffusion",
"arxiv:2112.10752",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-09T21:56:15+00:00 | [
"2112.10752"
] | [] | TAGS
#transformers #pytorch #ldmbert #diffusion #arxiv-2112.10752 #license-mit #endpoints_compatible #has_space #region-us
|
Latent Diffusion
Paper: High-Resolution Image Synthesis with Latent Diffusion Models
Abstract:
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulati... | [
"## Usage",
"## Samples\n\n1. \"A street sign that reads Huggingface.\"\n!sample_1\n\n\n2.\"A painting of a squirrel eating a burger\"\n!sample_2"
] | [
"TAGS\n#transformers #pytorch #ldmbert #diffusion #arxiv-2112.10752 #license-mit #endpoints_compatible #has_space #region-us \n",
"## Usage",
"## Samples\n\n1. \"A street sign that reads Huggingface.\"\n!sample_1\n\n\n2.\"A painting of a squirrel eating a burger\"\n!sample_2"
] |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)
This repository provides all the necessary t... | {"language": "wo", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["Dvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-dvoice-wolof | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"wo",
"dataset:Dvoice",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-09T22:02:32+00:00 | [] | [
"wo"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #wo #dataset-Dvoice #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)
==============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a ALFFA Wolof dataset within
SpeechBrain. For a better ... | [] | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #wo #dataset-Dvoice #license-apache-2.0 #has_space #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. -->
# NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
This model is a fine-tuned version of [bert-base-multi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned", "results": []}]} | ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T22:02:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| NLP-CIC-WFU\_Clinical\_Cases\_NER\_Paragraph\_Tokenized\_mBERT\_cased\_fine\_tuned
==================================================================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### 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: 7",
"### 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: 5e-05\n* train\\_batch\... |
text-generation | transformers |
# Omar Dialog GPT Model Medium 10
# Trained on discord channels:
# half of Dragalia chat | {"tags": ["conversational"]} | lucataco/DialoGPT-medium-omar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T22:22:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Omar Dialog GPT Model Medium 10
# Trained on discord channels:
# half of Dragalia chat | [
"# Omar Dialog GPT Model Medium 10",
"# Trained on discord channels:",
"# half of Dragalia chat"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Omar Dialog GPT Model Medium 10",
"# Trained on discord channels:",
"# half of Dragalia chat"
] |
text2text-generation | transformers |
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset This model was fine-tuned for capitalisation on text that includes multiple sentences or questions.
Interested in Caribbean Creole? Checkout the library [Caribe](https://pypi.org/project/Caribe/) for more info and future updates... | {"language": "en", "license": "mit", "tags": ["sentence capitalization", "text2text-generation"]} | KES/caribe-capitalise | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"sentence capitalization",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T22:47:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #sentence capitalization #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset This model was fine-tuned for capitalisation on text that includes multiple sentences or questions.
Interested in Caribbean Creole? Checkout the library Caribe for more info and future updates.
___
# Usage with Transformers
_... | [
"# Usage with Transformers\n\n\n___"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #sentence capitalization #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Usage with Transformers\n\n\n___"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [goog... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.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 | 2022-06-09T22:49:44+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\_7.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 results on the ... | [
"### 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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | kjunelee/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T23:03:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1595
* Accuracy: 0.931
* F1: 0.9313
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# Milo Dialog GPT Model Medium 12
# Trained on discord channels:
# half of Dragalia chat | {"tags": ["conversational"]} | lucataco/DialoGPT-medium-milo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-09T23:25:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Milo Dialog GPT Model Medium 12
# Trained on discord channels:
# half of Dragalia chat | [
"# Milo Dialog GPT Model Medium 12",
"# Trained on discord channels:",
"# half of Dragalia chat"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Milo Dialog GPT Model Medium 12",
"# Trained on discord channels:",
"# half of Dragalia chat"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-filtered-0609
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-cased-finetuned-filtered-0609", "results": []}]} | YeRyeongLee/bert-base-cased-finetuned-filtered-0609 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T23:30:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-filtered-0609
=======================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2410
* Accuracy: 0.9748
* Precision: 0.9751
* Recall: 0.9748
* F1: 0.9749
Model descriptio... | [
"### 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... |
null | null |
Super resolution model for anime and illustrations based on vgg11 and waifu2x. This model was trained on around 10k high resolution images (at least HD)
https://github.com/Exusai/SuperResolutionWaifuNN | {"license": "mit"} | ExusAI/SRWNN | null | [
"license:mit",
"region:us"
] | null | 2022-06-09T23:45:58+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Super resolution model for anime and illustrations based on vgg11 and waifu2x. This model was trained on around 10k high resolution images (at least HD)
URL | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | HrayrM/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T23:50:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7771
* Accuracy: 0.9135
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | hossay/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T23:51:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9263
* Recall: 0.9379
* F1: 0.9321
* Accuracy: 0.9838
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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/1485413658351968256/NUVe... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/artificialbuttr/1654825134207/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/artificialbuttr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T00:37:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
artificialbutter
@artificialbuttr
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 |
# vit_test_1_95
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/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | 25khattab/vit_test_1_95 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T00:40:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit_test_1_95
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 | [
"# vit_test_1_95\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"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit_test_1_95\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
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/1381121023567917058/JyYf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/wick_is_tired/1654825353897/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/wick_is_tired | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T00:41:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
IntroWick
@wick\_is\_tired
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"
] |
text2text-generation | transformers | Task:
Given a set of input keywords, generate a corresponding text output for a section in the legal domain.
Dataset:
We used the Contract Understanding Atticus Dataset (CUAD).
It is a corpus of 13,000+ labels in 510 commercial legal contracts.
They have been manually labeled under the supervision of experienced law... | {} | Wikram/Legal-key-to-text | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T00:44:21+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Task:
Given a set of input keywords, generate a corresponding text output for a section in the legal domain.
Dataset:
We used the Contract Understanding Atticus Dataset (CUAD).
It is a corpus of 13,000+ labels in 510 commercial legal contracts.
They have been manually labeled under the supervision of experienced law... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# rule_learning_margin_1mm
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_1mm", "results": []}]} | enoriega/rule_learning_margin_1mm | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T00:52:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us
| rule\_learning\_margin\_1mm
===========================
This model is a fine-tuned version of bert-base-uncased on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3806
* Margin Accuracy: 0.8239
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #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: 5e-05\n* train\\_batch\\_size: ... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln51")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln51")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln51 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T01:03:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #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/1367879964733804547/buUe... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/burkevillemama | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T01:15:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bree
@burkevillemama
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/1353151127026597889/Yarj... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/wickdedaccount/1654827628283/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/wickdedaccount | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T01:17:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
pp
@wickdedaccount
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/1401837042934468611/okzq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/loganpaul/1654828143127/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/loganpaul | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T01:27:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Logan Paul
@loganpaul
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"
] |
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-base-finetuned-wikilingua-ar
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base)... | {"license": "apache-2.0", "tags": ["summarization", "mt5", "ar", "abstractive summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mt5-base-finetuned-wikilingua-ar", "results": []}]} | ahmeddbahaa/mt5-base-finetuned-wikilingua-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"ar",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T01:40:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #ar #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base-finetuned-wikilingua-ar
This model is a fine-tuned version of google/mt5-base on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4936
- Rouge-1: 20.79
- Rouge-2: 7.6
- Rouge-l: 18.81
- Gen Len: 18.73
- Bertscore: 70.87
## Model description
More information ne... | [
"# mt5-base-finetuned-wikilingua-ar\n\nThis model is a fine-tuned version of google/mt5-base on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.4936\n- Rouge-1: 20.79\n- Rouge-2: 7.6\n- Rouge-l: 18.81\n- Gen Len: 18.73\n- Bertscore: 70.87",
"## Model description\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #ar #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-finetuned-wikilingua-ar\n\nThis mo... |
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-wikilingua-ar
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https:/... | {"tags": ["summarization", "mT5_multilingual_XLSum", "mt5", "abstractive summarization", "ar", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-wikilingua-ar", "results": []}]} | ahmeddbahaa/mT5_multilingual_XLSum-finetuned-wikilingua-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"... | null | 2022-06-10T01:47:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5_multilingual_XLSum-finetuned-wikilingua-ar
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5540
- Rouge-1: 27.46
- Rouge-2: 9.0
- Rouge-l: 22.59
- Gen Len: 43.41
- Bertscore: 73.7
## Model d... | [
"# mT5_multilingual_XLSum-finetuned-wikilingua-ar\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5540\n- Rouge-1: 27.46\n- Rouge-2: 9.0\n- Rouge-l: 22.59\n- Gen Len: 43.41\n- Bertscore: 73.7"... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_multilingual_XLSum-finetuned-wikili... |
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="thenewcompany/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | thenewcompany/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-10T02:41:32+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 |
<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/1579916871654117376/Dxd2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mcdonalds/1668324384662/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mcdonalds | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T02:43:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
McDonald's
@mcdonalds
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"
] |
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="thenewcompany/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"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 +/... | thenewcompany/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-10T03:36:13+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"
] |
image-classification | transformers |
# animal-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hu... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ritheshSree/animal-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T04:21:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# animal-classifier
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
#### cat
!cat
#### dog
!dog
#### snake
!snake
#### tiger
!tiger | [
"# animal-classifier\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",
"#### cat\n\n!cat",
"#### dog\n\n!dog",
"#### snake\n\n!snake",
"#### tiger\n\n!tiger"... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# animal-classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any is... |
null | null | This is a [Unigram tokenizer](https://huggingface.co/course/chapter6/7?fw=pt) trained on the [Wikitext dataset](https://huggingface.co/datasets/wikitext). Refer to the `train_unigram.py` script within this repository to know how it was trained. | {} | tf-tpu/unigram-tokenizer-wikitext | null | [
"region:us"
] | null | 2022-06-10T04:30:57+00:00 | [] | [] | TAGS
#region-us
| This is a Unigram tokenizer trained on the Wikitext dataset. Refer to the 'train_unigram.py' script within this repository to know how it was trained. | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Model Card: DALL·E Mini
This model is a reproduction of OpenAI’s DALL·E. Please see [this link](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for project-specific details. Below, we include the original DALL·E model card available on [the OpenAI ... | {} | skiltz/dall-e | null | [
"arxiv:2102.12092",
"region:us"
] | null | 2022-06-10T04:32:10+00:00 | [
"2102.12092"
] | [] | TAGS
#arxiv-2102.12092 #region-us
| # Model Card: DALL·E Mini
This model is a reproduction of OpenAI’s DALL·E. Please see this link for project-specific details. Below, we include the original DALL·E model card available on the OpenAI github.
## Model Details
The dVAE was developed by researchers at OpenAI to reduce the memory footprint of the trans... | [
"# Model Card: DALL·E Mini\n\nThis model is a reproduction of OpenAI’s DALL·E. Please see this link for project-specific details. Below, we include the original DALL·E model card available on the OpenAI github.",
"## Model Details\n\nThe dVAE was developed by researchers at OpenAI to reduce the memory footprint... | [
"TAGS\n#arxiv-2102.12092 #region-us \n",
"# Model Card: DALL·E Mini\n\nThis model is a reproduction of OpenAI’s DALL·E. Please see this link for project-specific details. Below, we include the original DALL·E model card available on the OpenAI github.",
"## Model Details\n\nThe dVAE was developed by researche... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | HrayrM/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T04:50:40+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3209
* Accuracy: 0.9429
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\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 #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
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/1535020786007916545/po7D... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/macarena_olona/1654842717478/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/macarena_olona | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T05:10:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Macarena Olona
@macarena\_olona
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"
] |
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_trained
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "t5-small-finetuned-cnndm_trained", "results": []}]} | bubblecookie/t5-small-finetuned-cnndm_trained | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T05:21:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-cnndm_trained
This model is a fine-tuned version of t5-small on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# t5-small-finetuned-cnndm_trained\n\nThis model is a fine-tuned version of t5-small on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-cnndm_trained\n\nThis model is a fine-tuned version of t5-small on th... |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | flood/pegasus-samsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T05:24:51+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4814
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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: 1\n* e... |
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/964497068424249345/Y6ce6... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/ralee85 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T05:27:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rob Lee
@ralee85
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"
] |
fill-mask | transformers | ## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain
For Chinese natural language processing in specific domains, we provide **Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model)** for the financial domain named **pai-dkplm-financial-base-zh**, fr... | {"language": "zh", "license": "apache-2.0", "tags": ["bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u6839\u636e\u65b0\u95fb\u62a5\u9053\uff0c\u4e09\u5927[MASK]\u6570\u5348\u540e\u96c6\u4f53\u6da8\u8d851\uff05\u3002"}, {"text": "\u7528\u5404\u79cd\u9014\u5f84\u652f\u6301\u4e2d\u5c0f[MASK]\u4f01\u4e1a\u878d\... | alibaba-pai/pai-dkplm-financial-base-zh | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"fill-mask",
"zh",
"arxiv:2205.00258",
"arxiv:2112.01047",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T05:28:43+00:00 | [
"2205.00258",
"2112.01047"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #pretraining #fill-mask #zh #arxiv-2205.00258 #arxiv-2112.01047 #license-apache-2.0 #endpoints_compatible #region-us
| ## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain
For Chinese natural language processing in specific domains, we provide Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain named pai-dkplm-financial-base-zh, from our A... | [
"## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain\nFor Chinese natural language processing in specific domains, we provide Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain named pai-dkplm-financial-base-zh, from... | [
"TAGS\n#transformers #pytorch #bert #pretraining #fill-mask #zh #arxiv-2205.00258 #arxiv-2112.01047 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain\nFor Chinese natural language processing in specific... |
null | null | bob esponja en pañales | {} | perehhh/random | null | [
"region:us"
] | null | 2022-06-10T05:34:48+00:00 | [] | [] | TAGS
#region-us
| bob esponja en pañales | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# adi1494/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": "adi1494/distilbert-base-uncased-finetuned-squad", "results": []}]} | adi1494/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 | 2022-06-10T05:38:11+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| adi1494/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: 1.5671
* Validation Loss: 1.2217
* Epoch: 0
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': 5532, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"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\\... |
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