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text2text-generation | transformers | # t5-qa_webnlg_synth-en
## Model description
This model is a *Data Question Answering* model based on T5-small, that answers questions given a structured table as input.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
#... | {"language": "en", "license": "mit", "tags": ["qa", "question", "answering", "SQuAD", "data2text", "metric", "nlg", "t5-small"], "datasets": ["squad_v2"], "widget": [{"text": "What is the food type at The Eagle? </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ \u00c2\u00a3 2 0 - 2 5 ]"... | ThomasNLG/t5-qa_webnlg_synth-en | null | [
"transformers",
"pytorch",
"jax",
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"text2text-generation",
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"question",
"answering",
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"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2104.07555",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-infere... | null | 2022-03-02T23:29:05+00:00 | [
"2104.07555"
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"en"
] | TAGS
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| # t5-qa_webnlg_synth-en
## Model description
This model is a *Data Question Answering* model based on T5-small, that answers questions given a structured table as input.
It is actually a component of QuestEval metric but can be used independently as it is, for QA only.
## How to use
You can play with the model usi... | [
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"## Model description\nThis model is a *Data Question Answering* model based on T5-small, that answers questions given a structured table as input.\nIt is actually a component of QuestEval metric but can be used independently as it is, for QA only.",
"## How to use\n\n\nYou can play w... | [
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text2text-generation | transformers | # t5-qg_squad1-en
## Model description
This model is a *Question Generation* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.
## How to use
```python
from transformers import T5Tokenizer, T5ForCo... | {"language": "en", "license": "mit", "tags": ["qg", "question", "generation", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad"], "widget": [{"text": "sv1 </s> Louis 14 </s> Louis 14 was a French King."}]} | ThomasNLG/t5-qg_squad1-en | null | [
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"pytorch",
"jax",
"t5",
"text2text-generation",
"qg",
"question",
"generation",
"SQuAD",
"metric",
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"en",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
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"region:us"
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| # t5-qg_squad1-en
## Model description
This model is a *Question Generation* model based on T5-small.
It is actually a component of QuestEval metric but can be used independently as it is, for QG only.
## How to use
You can play with the model using the inference API, the text input format should follow this templ... | [
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"## Model description\nThis model is a *Question Generation* model based on T5-small.\nIt is actually a component of QuestEval metric but can be used independently as it is, for QG only.",
"## How to use\n\n\nYou can play with the model using the inference API, the text input format should ... | [
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text2text-generation | transformers | # t5-qg_webnlg_synth-en
## Model description
This model is a *Data Question Generation* model based on T5-small, that generates questions, given a structured table as input and the conditioned answer.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independ... | {"language": "en", "license": "mit", "tags": ["qa", "question", "generation", "SQuAD", "data2text", "metric", "nlg", "t5-small"], "datasets": ["squad_v2"], "widget": [{"text": "The Eagle </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ \u00c2\u00a3 2 0 - 2 5 ]"}]} | ThomasNLG/t5-qg_webnlg_synth-en | null | [
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"text-generation-infer... | null | 2022-03-02T23:29:05+00:00 | [
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| # t5-qg_webnlg_synth-en
## Model description
This model is a *Data Question Generation* model based on T5-small, that generates questions, given a structured table as input and the conditioned answer.
It is actually a component of QuestEval metric but can be used independently as it is, for QG only.
## How to use
... | [
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"## Model description\nThis model is a *Data Question Generation* model based on T5-small, that generates questions, given a structured table as input and the conditioned answer. \nIt is actually a component of QuestEval metric but can be used independently as it is, for QG only.",
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text2text-generation | transformers | # t5-weighter_cnndm-en
## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of [QuestEval](https://github.com/ThomasSci... | {"language": "en", "license": "mit", "tags": ["qa", "classification", "question", "answering", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad", "cnndm"], "widget": [{"text": "a Buckingham Palace guard </s> Who felt on a manhole? </s> This is the embarrassing moment a Buckingham Palace guard slipped and fell... | ThomasNLG/t5-weighter_cnndm-en | null | [
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"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"classification",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad",
"dataset:cnndm",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"t... | null | 2022-03-02T23:29:05+00:00 | [
"2103.12693"
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"en"
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| # t5-weighter_cnndm-en
## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of QuestEval metric but can be used indepen... | [
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reinforcement-learning | ml-agents |
# Snowball Fight ☃️, a multi-agent environment for ML-Agents made by Hugging Face
A multi-agent environment using Unity ML-Agents Toolkit where two agents compete in a 1vs1 snowball fight game.
👉 You can [play it online at this link](https://hu... | {"license": "apache-2.0", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "ml-agents"], "environment": ["SnowballFight-1vs1"]} | ThomasSimonini/ML-Agents-SnowballFight-1vs1 | null | [
"ml-agents",
"onnx",
"deep-reinforcement-learning",
"reinforcement-learning",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#ml-agents #onnx #deep-reinforcement-learning #reinforcement-learning #license-apache-2.0 #region-us
|
# Snowball Fight ️, a multi-agent environment for ML-Agents made by Hugging Face
!Snowball Fight 1vs1
A multi-agent environment using Unity ML-Agents Toolkit where two agents compete in a 1vs1 snowball fight game.
You can play it online at this link.
️ You need to have some skills in ML-Agents if you want to use ... | [
"# Snowball Fight ️, a multi-agent environment for ML-Agents made by Hugging Face \n!Snowball Fight 1vs1\nA multi-agent environment using Unity ML-Agents Toolkit where two agents compete in a 1vs1 snowball fight game.\n\n You can play it online at this link.\n\n️ You need to have some skills in ML-Agents if you wa... | [
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reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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 import... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | ThomasSimonini/demo-hf-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
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] |
reinforcement-learning | null |
# mlagents-snowballfight-1vs1-ppo ☃️
This is a saved model of a PPO 1vs1 agent playing Snowball Fight.
| {"license": "apache-2.0", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "mlagents"], "environment": [{"MLAgents": "Snowballfight-1vs1-ppo"}]} | ThomasSimonini/mlagents-snowballfight-1vs1-ppo | null | [
"deep-reinforcement-learning",
"reinforcement-learning",
"mlagents",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#deep-reinforcement-learning #reinforcement-learning #mlagents #license-apache-2.0 #region-us
|
# mlagents-snowballfight-1vs1-ppo ️
This is a saved model of a PPO 1vs1 agent playing Snowball Fight.
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] |
reinforcement-learning | stable-baselines3 | # ppo-Walker2DBulletEnv-v0
This is a pre-trained model of a PPO agent playing AntBulletEnv-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip inst... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | ThomasSimonini/ppo-AntBulletEnv-v0 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # ppo-Walker2DBulletEnv-v0
This is a pre-trained model of a PPO agent playing AntBulletEnv-v0 using the stable-baselines3 library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
### Evaluation ... | [
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reinforcement-learning | stable-baselines3 |
# PPO Agent playing BreakoutNoFrameskip-v4
This is a trained model of a **PPO agent playing BreakoutNoFrameskip-v4 using the [stable-baselines3 library](https://stable-baselines3.readthedocs.io/en/master/index.html)**.
The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-Benchmark--Vmlldzo... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3", "atari"], "model-index": [{"name": "PPO Agent", "results": [{"task": {"type": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4", "type": "BreakoutNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": 3... | ThomasSimonini/ppo-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"atari",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #atari #model-index #region-us
|
# PPO Agent playing BreakoutNoFrameskip-v4
This is a trained model of a PPO agent playing BreakoutNoFrameskip-v4 using the stable-baselines3 library.
The training report: URL
## Evaluation Results
Mean_reward: '339.0'
# Usage (with Stable-baselines3)
- You need to use 'gym==0.19' since it includes Atari Roms.
- The... | [
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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... | ThomasSimonini/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #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
| [
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reinforcement-learning | stable-baselines3 | # PPO Agent playing PongNoFrameskip-v4
This is a trained model of a **PPO agent playing PongNoFrameskip-v4 using the [stable-baselines3 library](https://stable-baselines3.readthedocs.io/en/master/index.html)** (our agent is the 🟢 one).
The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-B... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3", "atari"], "model-index": [{"name": "PPO Agent", "results": [{"task": {"type": "reinforcement-learning"}, "dataset": {"name": "PongNoFrameskip-v4", "type": "PongNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": 21}]}]}]} | ThomasSimonini/ppo-PongNoFrameskip-v4 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"atari",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #atari #model-index #region-us
| # PPO Agent playing PongNoFrameskip-v4
This is a trained model of a PPO agent playing PongNoFrameskip-v4 using the stable-baselines3 library (our agent is the 🟢 one).
The training report: URL
## Evaluation Results
Mean_reward: '21.00 +/- 0.0'
# Usage (with Stable-baselines3)
- You need to use 'gym==0.19' since it ... | [
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reinforcement-learning | stable-baselines3 | # PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a **PPO agent playing QbertNoFrameskip-v4 using the [stable-baselines3 library](https://stable-baselines3.readthedocs.io/en/master/index.html)**.
The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-Benchmark--VmlldzoxNjI3NT... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3", "atari"], "model-index": [{"name": "PPO Agent", "results": [{"task": {"type": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type": "QbertNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "15685.... | ThomasSimonini/ppo-QbertNoFrameskip-v4 | null | [
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#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #atari #model-index #has_space #region-us
| # PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-v4 using the stable-baselines3 library.
The training report: URL
## Evaluation Results
Mean_reward: '15685.00 +/- 115.217'
# Usage (with Stable-baselines3)
- You need to use 'gym==0.19' since it includes Atari Rom... | [
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reinforcement-learning | stable-baselines3 | # PPO Agent playing SeaquestNoFrameskip-v4
This is a trained model of a **PPO agent playing SeaquestNoFrameskip-v4 using the [stable-baselines3 library](https://stable-baselines3.readthedocs.io/en/master/index.html)**.
The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-Benchmark--Vmlldzox... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3", "atari"], "model-index": [{"name": "PPO Agent", "results": [{"task": {"type": "reinforcement-learning"}, "dataset": {"name": "SeaquestNoFrameskip-v4", "type": "SeaquestNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "... | ThomasSimonini/ppo-SeaquestNoFrameskip-v4 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
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"atari",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #atari #model-index #region-us
| # PPO Agent playing SeaquestNoFrameskip-v4
This is a trained model of a PPO agent playing SeaquestNoFrameskip-v4 using the stable-baselines3 library.
The training report: URL
## Evaluation Results
Mean_reward: '1820.00 +/- 20.0'
# Usage (with Stable-baselines3)
- You need to use 'gym==0.19' since it includes Atari R... | [
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reinforcement-learning | stable-baselines3 | # ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4
This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. It is taken from [RL-trained-agents](https://github.com/DLR-RM/rl-trained-agents)
### Usage (with Stable-bas... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
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#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4
This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 ins... | [
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reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from hugg... | {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | ThomasSimonini/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
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reinforcement-learning | null | model-index:
- name: stable-baselines3-ppo-LunarLander-v2
---
# ARCHIVED MODEL, DO NOT USE IT
# stable-baselines3-ppo-LunarLander-v2 🚀👩🚀
This is a saved model of a PPO agent playing [LunarLander-v2](https://gym.openai.com/envs/LunarLander-v2/). The model is taken from [rl-baselines3-zoo](https://github.com/DLR-RM/r... | {"license": "apache-2.0", "tags": ["deep-reinforcement-learning", "reinforcement-learning"]} | ThomasSimonini/stable-baselines3-ppo-LunarLander-v2 | null | [
"deep-reinforcement-learning",
"reinforcement-learning",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#deep-reinforcement-learning #reinforcement-learning #license-apache-2.0 #has_space #region-us
| model-index:
- name: stable-baselines3-ppo-LunarLander-v2
---
# ARCHIVED MODEL, DO NOT USE IT
# stable-baselines3-ppo-LunarLander-v2
This is a saved model of a PPO agent playing LunarLander-v2. The model is taken from rl-baselines3-zoo
The goal is to correctly land the lander by controlling firing engines (fire left... | [
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text2text-generation | transformers |
# t5-end2end-question-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset to generate questions based on a context.
👉 If you want to learn how to fine-tune the t5 model to do the same, you can follow this [tutorial](https://colab.research.google.com/driv... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"]} | ThomasSimonini/t5-end2end-question-generation | null | [
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#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-end2end-question-generation
==============================
This model is a fine-tuned version of t5-base on the squad dataset to generate questions based on a context.
If you want to learn how to fine-tune the t5 model to do the same, you can follow this tutorial
For instance:
It achieves the following resul... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ThoracicCosine/DialoGPT-small-harrypotter | null | [
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"gpt2",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-generation | transformers |
#Michael DialoGPT Model | {"tags": ["conversational"]} | Tidum/DialoGPT-large-Michael | null | [
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"text-generation",
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"text-generation-inference",
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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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "typ... | Titantoe/IceBERT-finetuned-ner | null | [
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| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0772
* Precision: 0.8920
* Recall: 0.8656
* F1: 0.8786
* Accuracy: 0.9855
Model description
-----------------
More ... | [
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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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | Titantoe/XLMR-ENIS-finetuned-ner | null | [
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| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0941
* Precision: 0.8714
* Recall: 0.8450
* F1: 0.8580
* Accuracy: 0.9827
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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text-generation | transformers |
# Mast DialoGPT Model | {"tags": ["conversational"]} | Toadally/DialoGPT-small-david_mast | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
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"region:us"
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers |
# Boon 2 DialoGPT Model | {"tags": ["conversational"]} | Tofu05/DialoGPT-large-boon2 | null | [
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"endpoints_compatible",
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text-generation | transformers |
# Boon Bot DialoGPT Model | {"tags": ["conversational"]} | Tofu05/DialoGPT-med-boon3 | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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text-generation | transformers |
# DialoGPT Model | {"tags": ["conversational"]} | TofuBoy/DialoGPT-medium-Yubin2 | null | [
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"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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text-generation | transformers |
# Boon Bot DialoGPT Model | {"tags": ["conversational"]} | TofuBoy/DialoGPT-medium-boon | null | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | TomO/xlm-roberta-base-finetuned-marc-en | null | [
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"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9237
* Mae: 0.5122
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TOMFINSEN
This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["recall", "accuracy", "precision"], "model-index": [{"name": "TOMFINSEN", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type... | tomwetherell/TOMFINSEN | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| TOMFINSEN
=========
This model is a fine-tuned version of deepmind/language-perceiver on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3642
* Recall: 0.8986
* Accuracy: 0.8742
* Precision: 0.8510
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: tpu\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset) and [Finnish parliame... | {"language": "fi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "CSS10", "Finnish parliament session 2"], "metrics": ["wer"], "model-index": [{"name": "Finnish XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic... | Tommi/wav2vec2-large-xlsr-53-finnish | null | [
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|
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice, CSS10 and Finnish parliament session 2 datasets.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:... | [
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text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | Tr1ex/DialoGPT-small-rick | null | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dgpt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model desc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "dgpt", "results": []}]} | TrLOX/gpt2-tdk | null | [
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"has_space",
"text-generation-inference",
"region:us"
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|
# dgpt
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperpa... | [
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token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "de-en", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-de_en-pharmaceutical-smt | null | [
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-cs", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_cs-it-smt | null | [
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"en-cs"
] | TAGS
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_de-it-nmt | null | [
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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null | null |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_de-it-smt | null | [
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_de-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate th... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qual... |
token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-lv", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_lv-pharmaceutical-nmt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-lv"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate th... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qual... |
token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-lv", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_lv-pharmaceutical-smt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-lv"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate th... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qual... |
token-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-zh", "license": "apache-2.0", "tags": ["Quality Estimation", "microtransquest"]} | TransQuest/microtransquest-en_zh-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-zh"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate th... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #Quality Estimation #microtransquest #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qual... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "multilingual-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-any_en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual-en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-multilingual", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-en_any | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-en_de-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-zh", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-en_zh-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-zh"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "et-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-et_en-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et-en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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7,
235
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "multilingual-multilingual", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-multilingual | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual-multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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7,
235
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ne-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-ne_en-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ne-en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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3,
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7,
235
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ro-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-ro_en-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro-en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate ... | [
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7,
235
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text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ru-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-ru_en-reddit_wikiquotes | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru-en"
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#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "si-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "DA"]} | TransQuest/monotransquest-da-si_en-wiki | null | [
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"Quality Estimation",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si-en"
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#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #DA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the qu... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "de-en", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-de_en-pharmaceutical | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de-en"
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#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-multilingual", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "HTER"]} | TransQuest/monotransquest-hter-en_any | null | [
"transformers",
"pytorch",
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"text-classification",
"Quality Estimation",
"monotransquest",
"HTER",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #HTER #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #HTER #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #HTER #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-cs", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_cs-pharmaceutical | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-cs"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_de-it-nmt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_de-it-smt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluat... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_de-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluat... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-lv", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_lv-it-nmt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-lv"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluat... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-lv", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_lv-it-smt | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-lv"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluat... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
text-classification | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-zh", "license": "apache-2.0", "tags": ["Quality Estimation", "monotransquest", "hter"]} | TransQuest/monotransquest-hter-en_zh-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-zh"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluat... | [
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"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Quality Estimation #monotransquest #hter #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the ... |
feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-de", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-en_de-wiki | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en-de"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "en-zh", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-en_zh-wiki | null | [
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "et-en", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-et_en-wiki | null | [
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"region:us"
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"et-en"
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|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "multilingual-multilingual", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-multilingual | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual-multilingual"
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#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ne-en", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-ne_en-wiki | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ne-en"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
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"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ro-en", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-ro_en-wiki | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro-en"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "ru-en", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru-en"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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feature-extraction | transformers |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | {"language": "si-en", "license": "apache-2.0", "tags": ["Quality Estimation", "siamesetransquest", "da"]} | TransQuest/siamesetransquest-da-si_en-wiki | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si-en"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #Quality Estimation #siamesetransquest #da #license-apache-2.0 #endpoints_compatible #region-us
|
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commer... | [
"# TransQuest: Translation Quality Estimation with Cross-lingual Transformers\nThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many co... | [
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text-generation | transformers |
#Michael Scott DialoGPT model | {"tags": ["conversational"]} | TrebleJeff/DialoGPT-small-Michael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Michael Scott DialoGPT model | [] | [
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text-generation | transformers |
#Deadpool DialoGPT Model | {"tags": ["conversational"]} | TrimPeachu/Deadpool | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
#Deadpool DialoGPT Model | [] | [
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text-generation | transformers |
# GPT-2 for Music
Language Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.
This model is a rather small instance of GPT-2 trained on [TristanBehrens/js-fakes-4bars](https://huggingface.co/datasets/Tristan... | {"tags": ["gpt2", "text-generation", "music-modeling", "music-generation"], "widget": [{"text": "PIECE_START"}, {"text": "PIECE_START STYLE=JSFAKES GENRE=JSFAKES TRACK_START INST=48 BAR_START NOTE_ON=60"}, {"text": "PIECE_START STYLE=JSFAKES GENRE=JSFAKES TRACK_START INST=48 BAR_START NOTE_ON=58"}]} | TristanBehrens/js-fakes-4bars | null | [
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"gpt2",
"text-generation",
"music-modeling",
"music-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #music-modeling #music-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT-2 for Music
Language Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.
This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars. The model generates 4 bars at a time of ... | [
"# GPT-2 for Music\n\nLanguage Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.\n\nThis model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars. The model generates 4 bars at a t... | [
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text-generation | transformers |
Rick chatbot made with GPT2 ai from the show Rick and Morty, discord bot available now!
https://discord.com/oauth2/authorize?client_id=894569097818431519&permissions=1074113536&scope=bot
(v1 is no longer supported with RickBot) | {"tags": ["conversational"]} | Trixzy/rickai-v1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Rick chatbot made with GPT2 ai from the show Rick and Morty, discord bot available now!
URL
(v1 is no longer supported with RickBot) | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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39
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] |
text-generation | transformers |
# Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | Tropics/DialoGPT-small-peppa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peppa Pig DialoGPT Model | [
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text-generation | transformers | # CPM-Generate
## Model description
CPM (Chinese Pre-trained Language Model) is a Transformer-based autoregressive language model, with 2.6 billion parameters and 100GB Chinese training data. To the best of our knowledge, CPM is the largest Chinese pre-trained language model, which could facilitate downstream Chinese... | {"language": ["zh"], "license": "mit", "tags": ["cpm"], "datasets": ["100GB Chinese corpus"]} | TsinghuaAI/CPM-Generate | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"cpm",
"zh",
"arxiv:2012.00413",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.00413"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #cpm #zh #arxiv-2012.00413 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| CPM-Generate
============
Model description
-----------------
CPM (Chinese Pre-trained Language Model) is a Transformer-based autoregressive language model, with 2.6 billion parameters and 100GB Chinese training data. To the best of our knowledge, CPM is the largest Chinese pre-trained language model, which could f... | [
"#### How to use",
"#### Limitations and bias\n\n\nThe text generated by CPM is automatically generated by a neural network model trained on a large number of texts, which does not represent the authors' or their institutes' official attitudes and preferences. The text generated by CPM is only used for technical ... | [
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"#### Limitations and bias\n\n\nThe text generated by CPM is automatically generated by a neural network mode... | [
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fill-mask | transformers |
# ClinicalPubMedBERT
## Description
A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes ([MIMIC-III](https://mimic.physionet.org/)). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs and biomedical papers. We hope this model can serve... | {"language": ["en"], "license": "mit", "datasets": ["MIMIC-III"], "widget": [{"text": "Due to shortness of breath, the patient is diagnosed with [MASK], and other respiratory problems.", "example_title": "Example 1"}]} | Tsubasaz/clinical-pubmed-bert-base-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:MIMIC-III",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-MIMIC-III #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ClinicalPubMedBERT
## Description
A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes (MIMIC-III). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs and biomedical papers. We hope this model can serve better results on clinical-rela... | [
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"## Description\n\nA BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes (MIMIC-III). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs and biomedical papers. We hope this model can serve better results on cl... | [
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"# ClinicalPubMedBERT",
"## Description\n\nA BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes (MIMIC-III). We try combi... | [
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fill-mask | transformers |
# ClinicalPubMedBERT
## Description
A pre-trained model for clinical decision support, for more details, please see https://github.com/NtaylorOX/Public_Prompt_Mimic_III
A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes ([MIMIC-III](https://mimic.physionet.org/)). We try combini... | {"language": ["en"], "license": "mit", "datasets": ["MIMIC-III"], "widget": [{"text": "Due to shortness of breath, the patient is diagnosed with [MASK], and other respiratory problems.", "example_title": "Example 1"}, {"text": "Due to high blood sugar, and very low blood pressure, the patient is diagnosed with [MASK]."... | Tsubasaz/clinical-pubmed-bert-base-512 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:MIMIC-III",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-MIMIC-III #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# ClinicalPubMedBERT
## Description
A pre-trained model for clinical decision support, for more details, please see URL
A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes (MIMIC-III). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs... | [
"# ClinicalPubMedBERT",
"## Description\nA pre-trained model for clinical decision support, for more details, please see URL\n\nA BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes (MIMIC-III). We try combining two domains that have fewer overlaps with general knowledge text c... | [
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null | null | The older generation has a vulnerability, so they need to be monitored and taken care of. A large number of people, young and old, play really responsibly, but such a pastime can turn into a big problem. Many authoritative blogs and news portals of the gambling world like QYTO share statistics about this area and recom... | {} | Tsurakawi/erererere | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| The older generation has a vulnerability, so they need to be monitored and taken care of. A large number of people, young and old, play really responsibly, but such a pastime can turn into a big problem. Many authoritative blogs and news portals of the gambling world like QYTO share statistics about this area and recom... | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
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] |
null | null | # Model to Recognize Faces using eigenfaces and scikit-learn
Simple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka [LFW](http://vis-www.cs.umass.edu/lfw/)
This demo was taken from [Scikit-learn](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.h... | {} | Tuana/eigenfaces-sklearn-lfw | null | [
"joblib",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#joblib #region-us
| # Model to Recognize Faces using eigenfaces and scikit-learn
Simple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW
This demo was taken from Scikit-learn
The dataset includes 7 classes (individuals):
!Eigenfaces | [
"# Model to Recognize Faces using eigenfaces and scikit-learn\n\nSimple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW\nThis demo was taken from Scikit-learn\nThe dataset includes 7 classes (individuals):\n!Eigenfaces"
] | [
"TAGS\n#joblib #region-us \n",
"# Model to Recognize Faces using eigenfaces and scikit-learn\n\nSimple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW\nThis demo was taken from Scikit-learn\nThe dataset includes 7 classes (individuals):\n!Eigenfaces"
] | [
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"TAGS\n#joblib #region-us \n# Model to Recognize Faces using eigenfaces and scikit-learn\n\nSimple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW\nThis demo was taken from Scikit-learn\nThe dataset includes 7 classes (individuals):\n!Eigenfaces"
] |
fill-mask | transformers |
## Quickstart
**Release 1.0** (November 25, 2019)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. T... | {"language": "fi"} | TurkuNLP/bert-base-finnish-cased-v1 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07076",
"1908.04212"
] | [
"fi"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Quickstart
----------
Release 1.0 (November 25, 2019)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: arXiv:1912.07076
What's this?
------------
A version of Google's BERT deep transfer learning model for Finnish. The model can be fine-tuned to achieve state-of-the-art result... | [
"### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performanc... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT)... | [
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"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT) on do... |
fill-mask | transformers |
## Quickstart
**Release 1.0** (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: [bert-base-finnish-cased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased-v1.zip)
* Uncased Finnish BERT Base: [bert-base-finnish-uncased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-unca... | {"language": "fi"} | TurkuNLP/bert-base-finnish-uncased-v1 | null | [
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"tf",
"jax",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07076",
"1908.04212"
] | [
"fi"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Quickstart
----------
Release 1.0 (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: URL
* Uncased Finnish BERT Base: URL
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: arXiv:1912.07076
What's this?
------------
A version of Google's BERT deep trans... | [
"### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performanc... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT)... | [
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44,
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"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #fi #arxiv-1912.07076 #arxiv-1908.04212 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Document classification\n\n\n!learning curves for Yle and Ylilauta document classification\n\n\nFinBERT outperforms multilingual BERT (M-BERT) on do... |
sentence-similarity | sentence-transformers |
# Cased Finnish Sentence BERT model
Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found [here](http://epsilon-it.utu.fi/sbert400m).
## Training
- Library: [sentence-transformers](https://www.sbert.net/)
- FinBERT model: Tu... | {"language": ["fi"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "Minusta t\u00e4\u00e4ll\u00e4 on ihana asua!"}]} | TurkuNLP/sbert-cased-finnish-paraphrase | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"fi",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #fi #endpoints_compatible #region-us
|
# Cased Finnish Sentence BERT model
Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found here.
## Training
- Library: sentence-transformers
- FinBERT model: TurkuNLP/bert-base-finnish-cased-v1
- Data: The data provided here... | [
"# Cased Finnish Sentence BERT model\n\nFinnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found here.",
"## Training\n\n- Library: sentence-transformers\n- FinBERT model: TurkuNLP/bert-base-finnish-cased-v1\n- Data: The data ... | [
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sentence-similarity | sentence-transformers |
# Uncased Finnish Sentence BERT model
Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences *using [the cased model](https://huggingface.co/TurkuNLP/sbert-cased-finnish-paraphrase)* can be found [here](http://epsilon-it.utu.fi/sbert400m).
... | {"language": ["fi"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "Minusta t\u00e4\u00e4ll\u00e4 on ihana asua!"}]} | TurkuNLP/sbert-uncased-finnish-paraphrase | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"fi",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #fi #endpoints_compatible #region-us
|
# Uncased Finnish Sentence BERT model
Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences *using the cased model* can be found here.
## Training
- Library: sentence-transformers
- FinBERT model: TurkuNLP/bert-base-finnish-uncased-v1
- ... | [
"# Uncased Finnish Sentence BERT model\n\nFinnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences *using the cased model* can be found here.",
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token-classification | transformers |
# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)
## Introduction
[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based the language model for French Camembert (based on the RoBERTa architecture).... | {"language": "fr", "widget": [{"text": "Je m'appelle Hicham et je vis a F\u00e8s"}]} | TypicaAI/magbert-ner | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #token-classification #fr #autotrain_compatible #endpoints_compatible #region-us
|
# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)
## Introduction
[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based the language model for French Camembert (based on the RoBERTa architecture).... | [
"# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)",
"## Introduction\n\n[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based the language model for French Camembert (based on the RoBERTa arch... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #fr #autotrain_compatible #endpoints_compatible #region-us \n",
"# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)",
"## Introduction\n\n[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Mag... | [
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"TAGS\n#transformers #pytorch #camembert #token-classification #fr #autotrain_compatible #endpoints_compatible #region-us \n# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)## Introduction\n\n[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The M... |
fill-mask | transformers | <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**ARBERT** is one of three models described in our **ACl 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://mageed.arts.ubc.ca/files/2020/12/m... | {"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]} | UBC-NLP/ARBERT | null | [
"transformers",
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"Twitter",
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"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
| <img src="URL alt="drawing" width="30%" height="30%" align="right"/>
ARBERT is one of three models described in our ACl 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic". ARBERT is a large-scale pre-trained masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use t... | [
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
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"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this re... |
text2text-generation | transformers |
# AraT5-base-title-generation
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for ... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-base-title-generation | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translati... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #autotrain_compatible #endpoints_compatible ... | AraT5-base-title-generation
===========================
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Te... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #autotrain_compatible #endpoints_compa... | [
90
] | [
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null | transformers | # AraT5-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Und... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-base | null | [
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"MSA",
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"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
| AraT5-base
==========
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Languag... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us ... | [
75
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null | transformers | # AraT5-msa-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-msa-base | null | [
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
| AraT5-msa-base
==============
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us ... | [
75
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null | transformers | # AraT5-msa-small
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Languag... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-msa-small | null | [
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
| AraT5-msa-small
===============
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arab... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us ... | [
75
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null | transformers | # AraT5-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Und... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-tweet-base | null | [
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
| AraT5-base
==========
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Languag... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us ... | [
75
] | [
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null | transformers | # AraT5-tweet-small
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Langu... | {"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]} | UBC-NLP/AraT5-tweet-small | null | [
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
| AraT5-tweet-small
=================
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for ... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us ... | [
75
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null | transformers | # IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
<img src="https://huggingface.co/UBC-NLP/IndT5/raw/main/IND_langs_large7.png" alt="drawing" width="45%" height="45%" align="right"/>
In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train ... | {} | UBC-NLP/IndT5 | null | [
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us
| IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
=============================================================
<img src="URL alt="drawing" width="45%" height="45%" align="right"/>
In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build I... | [
"### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)\n\n\n\nGithub\n======\n\n\nMore details about our model can be found here: URL\n\n\nBibTex\n======"
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] |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="200" height="200" align="right"/>
**MARBERT** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.5... | {"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]} | UBC-NLP/MARBERT | null | [
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"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="drawing" width="200" height="200" align="right"/>
MARBERT is one of three models described in our ACL 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic". MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multi... | [
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research ... | [
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fill-mask | transformers | <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**MARBERTv2** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-lon... | {"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]} | UBC-NLP/MARBERTv2 | null | [
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"MSA",
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"ar",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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| <img src="URL alt="drawing" width="30%" height="30%" align="right"/>
MARBERTv2 is one of three models described in our ACL 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic".
We find that results with ARBERT and MARBERT on QA are not competitive, a clear discrepancy from what we have obser... | [
"# BibTex\r\n\r\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\r\nWe gratefully acknowledge support from the Natural Sciences and Engineering Res... | [
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token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_scibert_ScienceIE` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License**... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | UBIAI/en_scibert_ScienceIE | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (3 labels for 1 components)
### Accuracy
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | UKJ5/DialoGPT-small-harrypotter | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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] |
null | transformers |
# CZERT
This repository keeps Czert-A model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/CZERT-A-c... | {"tags": ["cs"]} | UWB-AIR/Czert-A-base-uncased | null | [
"transformers",
"tf",
"albert",
"cs",
"arxiv:2103.13031",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.13031"
] | [] | TAGS
#transformers #tf #albert #cs #arxiv-2103.13031 #endpoints_compatible #region-us
| CZERT
=====
This repository keeps Czert-A model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
Available Models
----------------
You can download MLM & NSP only pretrained models
~~CZERT-A-v1
CZERT-B-v1~~
After some additional experiments, we found ... | [
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
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fill-mask | transformers | # CZERT
This repository keeps trained Czert-B-base-cased-long-zero-shot model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
This is long version of Czert-B-base-cased created without any finetunning on long documents. Po... | {"tags": ["cs", "fill-mask"]} | UWB-AIR/Czert-B-base-cased-long-zero-shot | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"cs",
"fill-mask",
"arxiv:2103.13031",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.13031"
] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #region-us
| CZERT
=====
This repository keeps trained Czert-B-base-cased-long-zero-shot model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
This is long version of Czert-B-base-cased created without any finetunning on long documents. Positional embedings were crea... | [
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.",
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"### Token Level Tasks\n\n\nWe evaluate ... | [
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fill-mask | transformers |
# CZERT
This repository keeps trained Czert-B model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/C... | {"tags": ["cs", "fill-mask"]} | UWB-AIR/Czert-B-base-cased | null | [
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"region:us"
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"2103.13031"
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#transformers #pytorch #tf #bert #pretraining #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #has_space #region-us
| CZERT
=====
This repository keeps trained Czert-B model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
Available Models
----------------
You can download MLM & NSP only pretrained models
~~CZERT-A-v1
CZERT-B-v1~~
After some additional experiments, w... | [
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe ... | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# avengers2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | Ulto/avengers2 | null | [
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| avengers2
=========
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0131
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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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. -->
# pythonCoPilot
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot", "results": []}]} | Ulto/pythonCoPilot | null | [
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|
# pythonCoPilot
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hype... | [
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"## Training procedure",
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythonCoPilot2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the followin... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot2", "results": []}]} | Ulto/pythonCoPilot2 | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pythonCoPilot2
==============
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0479
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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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. -->
# pythonCoPilot3
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
Mor... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot3", "results": []}]} | Ulto/pythonCoPilot3 | null | [
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"gpt2",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pythonCoPilot3
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
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