pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-to-speech | espnet | license: cc-by-4.0
--- | {"tags": ["espnet", "audio", "text-to-speech"]} | SYSPIN/Marathi_Male_TTS | null | [
"espnet",
"audio",
"text-to-speech",
"region:us"
] | null | 2022-06-03T05:00:19+00:00 | [] | [] | TAGS
#espnet #audio #text-to-speech #region-us
| license: cc-by-4.0
--- | [] | [
"TAGS\n#espnet #audio #text-to-speech #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sciBERT-case-finetuned-breastcancer
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "sciBERT-case-finetuned-breastcancer", "results": []}]} | ENM/sciBERT-case-finetuned-breastcancer | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T05:38:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| sciBERT-case-finetuned-breastcancer
===================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0058
Model description
-----------------
More information needed
Intended use... | [
"### 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: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch... |
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... | mecusorin/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-03T05:45:26+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | transformers |
# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - [SHINOBU](https://dl.ndl.go.jp/info:ndljp/pid/1302683/3)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences.
The input text is tokenized by [SudachiTra](https://github.... | {"language": "ja", "license": "mit", "datasets": ["mC4 Japanese"]} | megagonlabs/electra-base-japanese-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T05:49:32+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #electra #pretraining #ja #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us
|
# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - SHINOBU
This is an ELECTRA model pretrained on approximately 200M Japanese sentences.
The input text is tokenized by SudachiTra with the WordPiece subword tokenizer.
See 'tokenizer_config.json' for the setting details.
## How to use
Pleas... | [
"# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - SHINOBU\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences.\n\nThe input text is tokenized by SudachiTra with the WordPiece subword tokenizer.\nSee 'tokenizer_config.json' for the setting details.",
"## How ... | [
"TAGS\n#transformers #pytorch #electra #pretraining #ja #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us \n",
"# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - SHINOBU\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences.\n\nThe input text is t... |
fill-mask | transformers |
## jobBERT-de
This is a domain-adapted transformer-based language model for German-speaking job advertisements.
Is is based on [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking j... | {"language": "de", "license": "cc-by-nc-sa-4.0"} | agne/jobBERT-de | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"de",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T05:53:53+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #fill-mask #de #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## jobBERT-de
This is a domain-adapted transformer-based language model for German-speaking job advertisements.
Is is based on bert-base-german-cased and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking job ads from Switzerland 1990-2020 (5.9 GB data). ... | [
"## jobBERT-de\n\nThis is a domain-adapted transformer-based language model for German-speaking job advertisements.\n\nIs is based on bert-base-german-cased and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking job ads from Switzerland 1990-2020 (5.9 GB ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #de #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## jobBERT-de\n\nThis is a domain-adapted transformer-based language model for German-speaking job advertisements.\n\nIs is based on bert-base-german-cased and adapted to the do... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-arxiv
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-arxiv", "results": []}]} | lewtun/t5-small-finetuned-arxiv | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T06:36:30+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-arxiv
========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1556
* Rouge1: 37.8405
* Rouge2: 20.4483
* Rougel: 33.996
* Rougelsum: 34.0071
* Gen Len: 15.8214
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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | chans/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T06:55:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="chans/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | chans/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T06:57:22+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1277369340275437570/R-AX... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mundodeportivo/1654247301367/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mundodeportivo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T07:51:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mundo Deportivo
@mundodeportivo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | ## jobGBERT
This is a domain-adapted transformer-based language model for German-speaking job advertisements.
Is is based on [deepset/gbert-base](https://huggingface.co/deepset/gbert-base), and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking job ads from... | {"language": "de", "license": "cc-by-nc-sa-4.0"} | agne/jobGBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"de",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T08:03:44+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #fill-mask #de #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ## jobGBERT
This is a domain-adapted transformer-based language model for German-speaking job advertisements.
Is is based on deepset/gbert-base, and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking job ads from Switzerland 1990-2020 (5.9 GB data).
### O... | [
"## jobGBERT\n\nThis is a domain-adapted transformer-based language model for German-speaking job advertisements.\n\nIs is based on deepset/gbert-base, and adapted to the domain of job advertisements trough continued in-domain pretraining on 4 million German-speaking job ads from Switzerland 1990-2020 (5.9 GB data)... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #de #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## jobGBERT\n\nThis is a domain-adapted transformer-based language model for German-speaking job advertisements.\n\nIs is based on deepset/gbert-base, and adapted to the domain ... |
sentence-similarity | sentence-transformers |
# kimcando/ko-paraKQC-demo2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model beco... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | kimcando/ko-paraKQC-demo2 | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T08:27:32+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# kimcando/ko-paraKQC-demo2
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# kimcando/ko-paraKQC-demo2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers inst... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# kimcando/ko-paraKQC-demo2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks ... |
text2text-generation | transformers |
# Model Card of `lmqg/mbart-large-cc25-jaquad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "ja", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_jaquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u30be\u30d5\u30a3\u30fc\u306f\u8cb4\u65cf\u51fa\u8eab\u3067\u306f\u3042\u3063\u3... | research-backup/mbart-large-cc25-jaquad-qg | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T08:35:08+00:00 | [
"2210.03992"
] | [
"ja"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/mbart-large-cc25-jaquad-qg'
===============================================
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg\_jaquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/mbart-large-cc25
* Language... | [
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: ja\n* Training data: lmqg/qg\\_jaquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question generation #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: ja\n* Training data:... |
text-classification | transformers |
Frederik Gaasdal Jensen • Henry Stoll • Sippo Rossi • Raghava Rao Mukkamala
# UNHCR Hate Speech Detection Model
This is a transformer model that can detect hate and offensive speech for English text. The primary use-case of this model is to detect hate speech targeted at refugees. The model is based on *roberta-unca... | {"language": "en", "tags": ["text classification", "hate speech", "offensive language", "hatecheck"], "datasets": ["unhcr-hatespeech"], "metrics": ["f1", "hatecheck"]} | unhcr/hatespeech-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"text classification",
"hate speech",
"offensive language",
"hatecheck",
"en",
"dataset:unhcr-hatespeech",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T08:46:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #text classification #hate speech #offensive language #hatecheck #en #dataset-unhcr-hatespeech #autotrain_compatible #endpoints_compatible #region-us
|
Frederik Gaasdal Jensen • Henry Stoll • Sippo Rossi • Raghava Rao Mukkamala
# UNHCR Hate Speech Detection Model
This is a transformer model that can detect hate and offensive speech for English text. The primary use-case of this model is to detect hate speech targeted at refugees. The model is based on *roberta-unca... | [
"# UNHCR Hate Speech Detection Model\nThis is a transformer model that can detect hate and offensive speech for English text. The primary use-case of this model is to detect hate speech targeted at refugees. The model is based on *roberta-uncased* and was fine-tuned on 12 abusive language datasets.\n\nThe model ha... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #text classification #hate speech #offensive language #hatecheck #en #dataset-unhcr-hatespeech #autotrain_compatible #endpoints_compatible #region-us \n",
"# UNHCR Hate Speech Detection Model\nThis is a transformer model that can detect hate and offensiv... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-Slippery-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "... | jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T09:14:08+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3-v2", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-Slippery-v3-v2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}... | jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3-v2 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T09:41:48+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
This model is a fine-tuned version of [persiannlp/mt5-small-parsinl... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "metrics": ["bleu"], "model-index": [{"name": "mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "data... | PontifexMaximus/mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T09:59:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-parsinlu-opus-translation\_fa\_en-finetuned-fa-to-en
==============================================================
This model is a fine-tuned version of persiannlp/mt5-small-parsinlu-opus-translation\_fa\_en on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\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: 30\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | baru98/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T10:00:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1274
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# martinbiber/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/H... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "martinbiber/marian-finetuned-kde4-en-to-fr", "results": []}]} | martinbiber/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T10:31:17+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| martinbiber/marian-finetuned-kde4-en-to-fr
==========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0539
* Validation Loss: 0.8992
* Epoch: 0
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 5911, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. Arthur wasn't a big fan of the beach. He asked his friend Steve to go to the beach with him. Steve brought a box of chips with Arthur's and him. Arthur a... | {} | jppaolim/v47_Move2PT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T10:58:10+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. Arthur wasn't a big fan of the beach. He asked his friend Steve to go to the beach with him. Steve brought a box of chips with Arthur's and him. Arthur a... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur wanted to go to the beach with his friends. Arthur wasn't a big fan of the beach. He asked his friend Steve to go to the beach with him. Steve brought a box of chips with Arthur's and him. A... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur wanted to go to the beach with his friends. Arth... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'RMSprop', 'learning_rate... | {"library_name": "keras", "tags": ["fewshot-learning"]} | keras-io/keras-reptile | null | [
"keras",
"fewshot-learning",
"has_space",
"region:us"
] | null | 2022-06-03T11:46:01+00:00 | [] | [] | TAGS
#keras #fewshot-learning #has_space #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'RMSprop', 'learning_rate... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #fewshot-learning #has_space #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following h... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1381256890542387204/zaT8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/washirerpadvice/1654262967962/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/washirerpadvice | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T12:23:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Washire RP Tips
@washirerpadvice
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pega_570_articles
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pega_570_articles", "results": []}]} | Worldman/pega_570_articles | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T12:51:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# pega_570_articles
This model is a fine-tuned version of google/pegasus-cnn_dailymail 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 hyperpa... | [
"# pega_570_articles\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# pega_570_articles\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore informat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | kaouther/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T12:51:59+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1703
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text-generation | transformers |
# GPT2-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## Model description
More information need... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": "cmotions/Beatles_lyrics", "model-index": [{"name": "GPT2-Beatles-Lyrics-finetuned-newlyrics", "results": []}]} | wvangils/GPT2-Beatles-Lyrics-finetuned-newlyrics | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T12:55:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT2-Beatles-Lyrics-finetuned-newlyrics
=======================================
This model is a fine-tuned version of gpt2 on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sinhprous/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | sinhprous/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T13:02:22+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": ... | arrandi/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T13:04:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532055379688841216/qJTj... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/calamitiddy/1654265229643/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/calamitiddy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T13:06:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
lauren rhiannon (nail cleanup duty)
@calamitiddy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sinhprous/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | sinhprous/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T13:12:06+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distiBERT_mLM_V3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_mLM_V3", "results": []}]} | FritzOS/TEdetection_distiBERT_mLM_V3 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T13:28:56+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distiBERT_mLM_V3
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More... | [
"# TEdetection_distiBERT_mLM_V3\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and e... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distiBERT_mLM_V3\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results ... |
text2text-generation | transformers |
# An Arabic abstractive text summarization model
A fine-tuned AraT5 model on a dataset of 84,764 paragraph-summary pairs.
Paper: [Arabic abstractive text summarization using RNN-based and transformer-based architectures](https://www.sciencedirect.com/science/article/abs/pii/S0306457322003284).
Dataset: [link](https:... | {"language": ["ar"], "tags": ["Arabic T5", "T5", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing"], "widget": [{"text": "\u0634\u0647\u062f\u062a \u0645\u062f\u064a\u0646\u0629 \u0637\u0631\u0627\u0628\u0644\u0633\u060c \u0645\u0633\u0627\u0621 \u0623\u0645\u0633 \u0627\u0644\u0... | malmarjeh/t5-arabic-text-summarization | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Arabic T5",
"T5",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T13:36:08+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Arabic T5 #T5 #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# An Arabic abstractive text summarization model
A fine-tuned AraT5 model on a dataset of 84,764 paragraph-summary pairs.
Paper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.
Dataset: link.
The model can be used as follows:
## Contact:
<banimarje@URL>
| [
"# An Arabic abstractive text summarization model\nA fine-tuned AraT5 model on a dataset of 84,764 paragraph-summary pairs.\n\nPaper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.\n\nDataset: link.\n\nThe model can be used as follows:",
"## Contact:\n<banimarje@URL>"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Arabic T5 #T5 #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# An Arabic abstractive text summarization model\nA fine-... |
null | null | See <https://github.com/k2-fsa/icefall/pull/344>
Note: In the uploaded files, the epoch number counts from 0.
| {} | Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03 | null | [
"tensorboard",
"region:us"
] | null | 2022-06-03T13:36:27+00:00 | [] | [] | TAGS
#tensorboard #region-us
| See <URL
Note: In the uploaded files, the epoch number counts from 0.
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text2text-generation | transformers | # Overview
This is a fine-tuned version of the model [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi?text=My+name+is+Sarah+and+I+live+in+London) on the dataset [IWSLT'15 English-Vietnamese](https://huggingface.co/datasets/mt_eng_vietnamese).
Performance in terms of [sacrebleu](https://hu... | {} | tdobrxl/opus-mt-en-vi-finetuned-IWSLT15 | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T13:41:47+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # Overview
This is a fine-tuned version of the model Helsinki-NLP/opus-mt-en-vi on the dataset IWSLT'15 English-Vietnamese.
Performance in terms of sacrebleu on the test set is as follows:
* Original opus-mt-en-vi: 29.83
* Fine-tuned opus-mt-en-vi: 37.35
# Parameters
* learning_rate=2e-5
* batch_size: 32
* weight_d... | [
"# Overview\nThis is a fine-tuned version of the model Helsinki-NLP/opus-mt-en-vi on the dataset IWSLT'15 English-Vietnamese. \nPerformance in terms of sacrebleu on the test set is as follows:\n\n* Original opus-mt-en-vi: 29.83\n* Fine-tuned opus-mt-en-vi: 37.35",
"# Parameters\n* learning_rate=2e-5\n* batch_siz... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Overview\nThis is a fine-tuned version of the model Helsinki-NLP/opus-mt-en-vi on the dataset IWSLT'15 English-Vietnamese. \nPerformance in terms of sacrebleu on the test set is as follows:\n... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur wants to go to the beach. He calls the beach and asks for a spot on the sand. Arthur gets a new friend with a beach towel. Arthur takes the beach. Arthur spends the day relaxing and having a great ... | {} | jppaolim/v48_GPT2Medium_PT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T13:44:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur wants to go to the beach. He calls the beach and asks for a spot on the sand. Arthur gets a new friend with a beach towel. Arthur takes the beach. Arthur spends the day relaxing and having a great ... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur wants to go to the beach. He calls the beach and asks for a spot on the sand. Arthur gets a new friend with a beach towel. Arthur takes the beach. Arthur spends the day relaxing and having a... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur wants to go to the beach. He calls the beach and... |
text2text-generation | transformers |
# An Arabic abstractive text summarization model
A BERT2BERT-based model whose parameters are initialized with mBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.
Paper: [Arabic abstractive text summarization using RNN-based and transformer-based architectures](https://www.scie... | {"language": ["ar"], "tags": ["Multilingual BERT", "BERT2BERT", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing"]} | malmarjeh/mbert2mbert-arabic-text-summarization | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"Multilingual BERT",
"BERT2BERT",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-03T13:45:34+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #Multilingual BERT #BERT2BERT #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# An Arabic abstractive text summarization model
A BERT2BERT-based model whose parameters are initialized with mBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.
Paper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.
Dataset: link.
... | [
"# An Arabic abstractive text summarization model\nA BERT2BERT-based model whose parameters are initialized with mBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.\n\nPaper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.\n\nDataset... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #Multilingual BERT #BERT2BERT #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# An Arabic abstractive text summarization model\nA BERT... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Eulaliefy/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T14:00:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9251
* Recall: 0.9350
* F1: 0.9300
* Accuracy: 0.9836
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_70_articles
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
## Model ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5_70_articles", "results": []}]} | Worldman/t5_70_articles | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T14:29:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5_70_articles
This model is a fine-tuned version of t5-base 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 ... | [
"# t5_70_articles\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training h... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_70_articles\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Model descriptio... |
text2text-generation | transformers |
# Hotel review multi-aspect sentiment classification using T5
We fine tune a T5 pretrained model to generate multi-aspect sentiment classes. The outputs are whole sentiment, aspect, and aspect+sentiment.
T5情緒面向分類多任務，依據中文簡體孟子T5預訓練模型微調，訓練資料集只有3萬筆，做NLP研究與課程的範例模型用途。
# 如何測試
在右側測試區輸入不同的任務文字
範例1:
面向::早餐... | {"language": ["tw"], "license": "afl-3.0", "tags": ["t5"]} | clhuang/t5-hotel-review-sentiment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"tw",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T14:34:25+00:00 | [] | [
"tw"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Hotel review multi-aspect sentiment classification using T5
We fine tune a T5 pretrained model to generate multi-aspect sentiment classes. The outputs are whole sentiment, aspect, and aspect+sentiment.
T5情緒面向分類多任務，依據中文簡體孟子T5預訓練模型微調，訓練資料集只有3萬筆，做NLP研究與課程的範例模型用途。
# 如何測試
在右側測試區輸入不同的任務文字
範例1:
面向::早餐... | [
"# Hotel review multi-aspect sentiment classification using T5\n\nWe fine tune a T5 pretrained model to generate multi-aspect sentiment classes. The outputs are whole sentiment, aspect, and aspect+sentiment. \n\nT5情緒面向分類多任務，依據中文簡體孟子T5預訓練模型微調，訓練資料集只有3萬筆，做NLP研究與課程的範例模型用途。",
"# 如何測試\n在右側測試區輸入不同的任務文字\n\n 範例1:... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Hotel review multi-aspect sentiment classification using T5\n\nWe fine tune a T5 pretrained model to generate multi-aspect sentiment classes. The o... |
null | transformers |
# Cour de Cassation automatic *titrage* prediction model
Model for the automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf). If you use this model, pl... | {"language": "fr", "license": "cc-by-4.0"} | rbawden/CCASS-auto-titrages-base | null | [
"transformers",
"pytorch",
"fsmt",
"fr",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T14:36:54+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #fsmt #fr #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Cour de Cassation automatic *titrage* prediction model
Model for the automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are described in this paper. If you use this model, please cite our research paper (see below).
## Model description
The model is a tra... | [
"# Cour de Cassation automatic *titrage* prediction model\n\nModel for the automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are described in this paper. If you use this model, please cite our research paper (see below).",
"## Model description\n\nThe mo... | [
"TAGS\n#transformers #pytorch #fsmt #fr #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Cour de Cassation automatic *titrage* prediction model\n\nModel for the automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are described in this paper. If ... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was bored today. He took a vacation to the beach. The beach was very crowded. Arthur finally enjoyed the beach for the beach. He had so much fun he decided to take his vacation there.
Arthur goes... | {} | jppaolim/v49Neo | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T15:26:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was bored today. He took a vacation to the beach. The beach was very crowded. Arthur finally enjoyed the beach for the beach. He had so much fun he decided to take his vacation there.
Arthur goes... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur was bored today. He took a vacation to the beach. The beach was very crowded. Arthur finally enjoyed the beach for the beach. He had so much fun he decided to take his vacation there. \nArt... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur was bored today. He took a vacation to the beach. The beach was very cro... |
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... | NikitaBaramiia/PPO-LunarLander-v2-1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-03T15:51:42+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Edric111/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T16:07:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0599
* Precision: 0.9274
* Recall: 0.9372
* F1: 0.9323
* Accuracy: 0.9840
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the N... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | VictorZhu/results | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T16:10:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1194
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-grammar-corruption
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## M... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-grammar-corruption", "results": []}]} | juancavallotti/t5-grammar-corruption | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T16:54:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-grammar-corruption
This model is a fine-tuned version of t5-base 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 follo... | [
"# t5-grammar-corruption\n\nThis model is a fine-tuned version of t5-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-grammar-corruption\n\nThis model is a fine-tuned version of t5-base on the None dataset.",
"## Model descr... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_0.6
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmin... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_base_tcm_0.6", "results": []}]} | ricardo-filho/bert_base_tcm_0.6 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T17:39:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert\_base\_tcm\_0.6
====================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0193
* Criterio Julgamento Precision: 0.8875
* Criterio Julgamento Recall: 0.8659
* Criterio Julgamento F1: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size:... |
question-answering | transformers | TrainOutput(global_step=5475, training_loss=1.7323438837756848, metrics={'train_runtime': 4630.6634, 'train_samples_per_second': 18.917, 'train_steps_per_second': 1.182, 'total_flos': 1.1445080909703168e+16, 'train_loss': 1.7323438837756848, 'epoch': 1.0})
| {} | haritzpuerto/distilbert-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T19:04:42+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
| TrainOutput(global_step=5475, training_loss=1.7323438837756848, metrics={'train_runtime': 4630.6634, 'train_samples_per_second': 18.917, 'train_steps_per_second': 1.182, 'total_flos': 1.1445080909703168e+16, 'train_loss': 1.7323438837756848, 'epoch': 1.0})
| [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **TQC** Agent playing **donkey-avc-sparkfun-v0**
This is a trained model of a **TQC** agent playing **donkey-avc-sparkfun-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["donkey-avc-sparkfun-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "donkey-avc-sparkfun-v0",... | araffin/tqc-donkey-avc-sparkfun-v0 | null | [
"stable-baselines3",
"donkey-avc-sparkfun-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-03T19:43:54+00:00 | [] | [] | TAGS
#stable-baselines3 #donkey-avc-sparkfun-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TQC Agent playing donkey-avc-sparkfun-v0
This is a trained model of a TQC agent playing donkey-avc-sparkfun-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# TQC Agent playing donkey-avc-sparkfun-v0\nThis is a trained model of a TQC agent playing donkey-avc-sparkfun-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #donkey-avc-sparkfun-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TQC Agent playing donkey-avc-sparkfun-v0\nThis is a trained model of a TQC agent playing donkey-avc-sparkfun-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-samsum-en
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-ba... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-finetuned-samsum-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling... | santiviquez/bart-base-finetuned-samsum-en | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:samsum",
"base_model:facebook/bart-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T20:37:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-samsum #base_model-facebook/bart-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-samsum-en
=============================
This model is a fine-tuned version of facebook/bart-base on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3676
* Rouge1: 46.8825
* Rouge2: 22.0923
* Rougel: 39.7249
* Rougelsum: 42.9187
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-samsum #base_model-facebook/bart-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | jgriffi/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T21:14:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1496
* F1: 0.8646
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Sicko-Code/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Sicko-Code/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-03T21:17:03+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_fine_tuned_sentiment_newsmtsc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta_fine_tuned_sentiment_newsmtsc", "results": []}]} | RogerKam/roberta_fine_tuned_sentiment_newsmtsc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T21:19:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta_fine_tuned_sentiment_newsmtsc
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6134
- Accuracy: 0.7713
- F1 Score: 0.7710
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# roberta_fine_tuned_sentiment_newsmtsc\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6134\n- Accuracy: 0.7713\n- F1 Score: 0.7710",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_fine_tuned_sentiment_newsmtsc\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-samsum-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-samsum-en", "results": []}]} | santiviquez/mt5-small-finetuned-samsum-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-03T21:28:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-samsum-en
=============================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4304
* Rouge1: 21.9966
* Rouge2: 9.1451
* Rougel: 19.532
* Rougelsum: 20.6359
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-keyword-extractor
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unk... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (\u20ac57bn) cash-and stock deal, massively diversifying the chipmaker\u2019s business a... | yanekyuk/bert-keyword-extractor | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-03T22:06:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-keyword-extractor
======================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1341
* Precision: 0.8565
* Recall: 0.8874
* Accuracy: 0.9738
* F1: 0.8717
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | jgriffi/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T22:13:02+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1774
* F1: 0.8594
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | jgriffi/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T22:57:48+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0994
* F1: 0.9321
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | jgriffi/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T23:16:55+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2556
* F1: 0.8374
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | jgriffi/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T23:32:54+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4218
* F1: 0.7055
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | gagan3012/document-denoiser | null | [
"keras",
"region:us"
] | null | 2022-06-03T23:41:26+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | jgriffi/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-03T23:52:21+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1448
* F1: 0.8881
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# berturk-keyword-extractor
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/ber... | {"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz... | yanekyuk/berturk-keyword-extractor | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T00:02:48+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| berturk-keyword-extractor
=========================
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4306
* Precision: 0.6770
* Recall: 0.6899
* Accuracy: 0.9169
* F1: 0.6834
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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1527089805955301377/vNsx... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/ww_bokudyo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T00:05:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
wuwu
@ww\_bokudyo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1000482851853340672/LhUd... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/katieoneuro/1654306303616/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/katieoneuro | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T00:26:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Katie O'Nell
@katieoneuro
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-base-squad2_3
This model is a fine-tuned version of [tau/splinter-base-qass](https://huggingface.co/tau/splinter-base-q... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "splinter-base-squad2_3", "results": []}]} | nbroad/splinter-base-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"splinter",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T00:30:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #splinter #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# splinter-base-squad2_3
This model is a fine-tuned version of tau/splinter-base-qass on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# splinter-base-squad2_3\n\nThis model is a fine-tuned version of tau/splinter-base-qass on the squad_v2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #tensorboard #splinter #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# splinter-base-squad2_3\n\nThis model is a fine-tuned version of tau/splinter-base-qass on the squad_v2 dataset.",
"## Model description\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]} | baru98/bert-base-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T00:42:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased-finetuned-squad
===============================
This model is a fine-tuned version of bert-base-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 5.4212
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
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. -->
# camembert-keyword-extractor
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an ... | {"language": ["fr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Le pr\u00e9sident de la R\u00e9publique appelle en outre les Fran\u00e7ais \u00e0 faire le choix d'une \"majorit\u00e9 stable et s\u00e9rieuse pour les prot\u00e9ger face... | yanekyuk/camembert-keyword-extractor | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T01:03:03+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-keyword-extractor
===========================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2199
* Precision: 0.6743
* Recall: 0.6979
* Accuracy: 0.9346
* F1: 0.6859
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #generated_from_trainer #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\... |
text-generation | transformers |
## GPT2 Japanese base model version 2
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 60,000.
### Training Data
* [wiki40b/ja](https://www.tensorflow.org/datasets/catalog/wiki40b#wi... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "\u5929\u6c17\u4e88\u5831\u306b\u3088\u308c\u3070\u660e\u65e5\u306f"}, {"text": "\u79c1\u306e\u4eca\u65e5\u306e\u663c\u98ef\u306f"}, {"text": "\u30b5\u30c3\u30ab\u30fc\u65e5\u672c\u4ee3\u8868\u306f\u30d9\u30eb\u30ae\u3... | ClassCat/gpt2-base-japanese-v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T01:30:34+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## GPT2 Japanese base model version 2
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 60,000.
### Training Data
* wiki40b/ja (Japanese Wikipedia)
* Subset of CC-100/ja : Monolingual... | [
"## GPT2 Japanese base model version 2",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses GPT2 base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 60,000.",
"### Training Data \n\n* wiki40b/ja (Japanese Wikipedia)\n* Subs... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 Japanese base model version 2",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-indonesian-NER-finetuned-ner
This model is a fine-tuned version of [cahya/xlm-roberta-large-indonesian-NER](ht... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-large-indonesian-NER-finetuned-ner", "results": []}]} | kaniku/xlm-roberta-large-indonesian-NER-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T01:44:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-large-indonesian-NER-finetuned-ner
==============================================
This model is a fine-tuned version of cahya/xlm-roberta-large-indonesian-NER on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0489
* Precision: 0.9254
* Recall: 0.9394
* F1: 0.9324
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* ... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="send-it/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | send-it/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T02:07:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | send-it/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T02:08:56+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prediccion_titulos
Este modelo predice los encabezados de las noticias
## Model description
Este modelo fue entrenado con un Trans... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Prediccion_titulos", "results": []}]} | LinaR/Prediccion_titulos | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T02:33:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Prediccion_titulos
Este modelo predice los encabezados de las noticias
## Model description
Este modelo fue entrenado con un Transformador T5 y una base de datos en español
## Intended uses & limitations
More information needed
## Training and evaluation data
Los datos fueron tomado del siguiente dataset ... | [
"# Prediccion_titulos\n\nEste modelo predice los encabezados de las noticias",
"## Model description\n\nEste modelo fue entrenado con un Transformador T5 y una base de datos en español",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nLos datos fueron tomado de... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Prediccion_titulos\n\nEste modelo predice los encabezados de las noticias",
"## Model description\n\nEste modelo fue entrenado con ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# madatnlp/rob-large-krmath2
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/rob-large-krmath2", "results": []}]} | madatnlp/rob-large-krmath2 | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T02:47:50+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| madatnlp/rob-large-krmath2
==========================
This model is a fine-tuned version of klue/roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0707
* Validation Loss: 0.2571
* Epoch: 17
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'SGD', 'learning\\_rate': 0.01, 'decay': 0.0, 'momentum': 0.9, 'nesterov': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.19.2\n... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'SGD', 'learning\\_rate': 0.01, 'decay': 0.0, 'mo... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pasajes_de_la_biblia
Este modelo fue entrenado con el dataset publicado en Kaggle de los versiculos de la biblia en el siguiente enla... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "pasajes_de_la_biblia", "results": []}]} | ssantanag/pasajes_de_la_biblia | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T02:56:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pasajes_de_la_biblia
Este modelo fue entrenado con el dataset publicado en Kaggle de los versiculos de la biblia en el siguiente enlace puede encontrar el dataset URL
## Training and evaluation data
la distribución de la data fue la siguiente:
- Training set: 58.20%
- Validation set: 9.65%
- Test set: 32.15%
... | [
"# pasajes_de_la_biblia\n\nEste modelo fue entrenado con el dataset publicado en Kaggle de los versiculos de la biblia en el siguiente enlace puede encontrar el dataset URL",
"## Training and evaluation data\n\nla distribución de la data fue la siguiente:\n- Training set: 58.20%\n- Validation set: 9.65%\n- Test ... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pasajes_de_la_biblia\n\nEste modelo fue entrenado con el dataset publicado en Kaggle de los versiculos de la biblia en el siguiente ... |
text-classification | transformers | This SciBert-based multi-label classifier, trained as part of the work "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse", distinguishes three different forms of science-relatedness for Tweets. See details at https://github.com/AI-4-Sci/SciTweets . | {"license": "cc-by-4.0", "widget": [{"text": "Study: Shifts in electricity generation spur net job growth, but coal jobs decline - via @DukeU https://www.eurekalert.org/news-releases/637217", "example_title": "All categories"}, {"text": "Shifts in electricity generation spur net job growth, but coal jobs decline", "exa... | sschellhammer/SciTweets_SciBert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T05:16:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| This SciBert-based multi-label classifier, trained as part of the work "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse", distinguishes three different forms of science-relatedness for Tweets. See details at URL . | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | awalmeida/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T05:23:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="awalmeida/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3-4x4-no_slippery", "type": "Taxi-v3-4x4-no_slippery"}, "metr... | awalmeida/q-Taxi-v3 | null | [
"Taxi-v3-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T06:04:21+00:00 | [] | [] | TAGS
#Taxi-v3-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | lbw/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T06:30:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0596
* Precision: 0.9279
* Recall: 0.9378
* F1: 0.9328
* Accuracy: 0.9840
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# convberturk-keyword-extractor
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-cased](https://huggingface.co/d... | {"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz... | yanekyuk/convberturk-keyword-extractor | null | [
"transformers",
"pytorch",
"convbert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T08:32:23+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #convbert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| convberturk-keyword-extractor
=============================
This model is a fine-tuned version of dbmdz/convbert-base-turkish-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4098
* Precision: 0.6742
* Recall: 0.7035
* Accuracy: 0.9175
* F1: 0.6886
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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #convbert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | VedantS01/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T10:45:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | cutten/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T12:17:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6342
* Wer: 0.5808
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-lar... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["hi_ner_config"], "model-index": [{"name": "xlm-roberta-large-finetuned-ner", "results": []}]} | SaiNikhileshReddy/xlm-roberta-large-finetuned-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:hi_ner_config",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T12:21:23+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-hi_ner_config #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-large-finetuned-ner
This model is a fine-tuned version of xlm-roberta-large on the hi_ner_config dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2329
- eval_precision: 0.7110
- eval_recall: 0.6854
- eval_f1: 0.6980
- eval_accuracy: 0.9332
- eval_runtime: 162.3478
- eva... | [
"# xlm-roberta-large-finetuned-ner\n\nThis model is a fine-tuned version of xlm-roberta-large on the hi_ner_config dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2329\n- eval_precision: 0.7110\n- eval_recall: 0.6854\n- eval_f1: 0.6980\n- eval_accuracy: 0.9332\n- eval_runtime: 162... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-hi_ner_config #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-large-finetuned-ner\n\nThis model is a fine-tuned version of xlm-roberta-large on the hi_ner_config dataset.\nIt a... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/Yen-Ju_Lu_spatilaizedslurp_asr_train_asr_conformer_transformer_valid.acc.best`
This model was trained by neillu23 using slurp_mixture recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0fae8113d99d092e7cbe4bcc48f93... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slurp_mixture"]} | espnet/Yen-Ju_Lu_spatilaizedslurp_asr_train_asr_conformer_transformer_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp_mixture",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-04T12:35:31+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-slurp_mixture #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/Yen-Ju\_Lu\_spatilaizedslurp\_asr\_train\_asr\_conformer\_transformer\_valid.URL'
This model was trained by neillu23 using slurp\_mixture recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Mar 29 04:17:37 U... | [
"### 'espnet/Yen-Ju\\_Lu\\_spatilaizedslurp\\_asr\\_train\\_asr\\_conformer\\_transformer\\_valid.URL'\n\n\nThis model was trained by neillu23 using slurp\\_mixture recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Mar 29 04:17:37 UTC 2022... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-slurp_mixture #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/Yen-Ju\\_Lu\\_spatilaizedslurp\\_asr\\_train\\_asr\\_conformer\\_transformer\\_valid.URL'\n\n\nThis model was trained by neillu23 using slurp\\_mixture recipe in espnet.... |
image-classification | keras |
# Compact Convolutional Transformers
Based on the _Compact Convolutional Transformers_ example on [keras.io](https://keras.io/examples/vision/cct/) created by [Sayak Paul](https://twitter.com/RisingSayak).
## Model description
As discussed in the [Vision Transformers (ViT)](https://arxiv.org/abs/2010.11929) paper, ... | {"library_name": "keras", "tags": ["image-classification", "vision"]} | keras-io/cct | null | [
"keras",
"tensorboard",
"image-classification",
"vision",
"arxiv:2010.11929",
"arxiv:2104.05704",
"has_space",
"region:us"
] | null | 2022-06-04T13:00:16+00:00 | [
"2010.11929",
"2104.05704"
] | [] | TAGS
#keras #tensorboard #image-classification #vision #arxiv-2010.11929 #arxiv-2104.05704 #has_space #region-us
| Compact Convolutional Transformers
==================================
Based on the *Compact Convolutional Transformers* example on URL created by Sayak Paul.
Model description
-----------------
As discussed in the Vision Transformers (ViT) paper, a Transformer-based architecture for vision typically requires a la... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel reproduced by [Edoardo Abati](URL target=)"
] | [
"TAGS\n#keras #tensorboard #image-classification #vision #arxiv-2010.11929 #arxiv-2104.05704 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel reproduced by [Edoar... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-ddlm
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/models/microsoft... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-v3-large-ddlm", "results": []}]} | scales-okn/docket-language-model | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T14:01:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-ddlm
=====================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5241
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/t5 | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-06-04T14:43:41+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## T5 model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the T5 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Habana's custom AdamW implementation\n- 'use_fus... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## T5 model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the T5 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': wh... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510438749154549764/sar6... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/orc_nft/1654359188989/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/orc_nft | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T15:12:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ORC.A ⍬
@orc\_nft
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | R4 checkpoint-16000
| {} | gciaffoni/wav2vec2-large-xls-r-300m-it-colab4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T15:26:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| R4 checkpoint-16000
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling
This model is a fine-tuned version of [cointegrated/rubert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling", "results": []}]} | mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T15:44:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| rubert-tiny2\_finetuned\_emotion\_experiment\_modified\_CE\_LOSS\_resampling
============================================================================
This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4520
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | mcditoos/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T16:09:40+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | mcditoos/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T16:14:07+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# layoutlmv2-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micr... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["funsd"], "model_index": [{"name": "layoutlmv2-finetuned-funsd", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "funsd", "type": "funsd", "args": "funsd"}}]}]} | mishtert/iec | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-04T16:22:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #dataset-funsd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# layoutlmv2-finetuned-funsd
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the funsd dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# layoutlmv2-finetuned-funsd\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the funsd dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #dataset-funsd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# layoutlmv2-finetuned-funsd\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased ... |
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. -->
# berturk-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz... | {"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz... | yanekyuk/berturk-cased-keyword-discriminator | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T16:29:51+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| berturk-keyword-discriminator
=============================
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4196
* Precision: 0.6729
* Recall: 0.6904
* Accuracy: 0.9163
* F1: 0.6815
* Ent/precision: 0.6776
... | [
"### 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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text-generation | transformers |
# mgfrantz/distilgpt2-finetuned-reddit-tifu
This model was trained to as practice for fine-tuning a causal language model.
There was no intended use case for this model besides having some fun seeing how different things might be screwed up.
## Data
This model was trained on "short" subset of [`reddit_tifu`](https:... | {"language": ["en"], "license": "mit", "datasets": ["reddit_tifu (subset: short)"], "thumbnail": "https://styles.redditmedia.com/t5_2to41/styles/communityIcon_qedoavxzocr61.png?width=256&s=9c7c19b81474c3788279b8d6d6823e791d0524fc", "widget": [{"text": "I told my friend"}]} | mgfrantz/distilgpt2-finetuned-reddit-tifu | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T16:47:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mgfrantz/distilgpt2-finetuned-reddit-tifu
This model was trained to as practice for fine-tuning a causal language model.
There was no intended use case for this model besides having some fun seeing how different things might be screwed up.
## Data
This model was trained on "short" subset of 'reddit_tifu' dataset.... | [
"# mgfrantz/distilgpt2-finetuned-reddit-tifu\n\nThis model was trained to as practice for fine-tuning a causal language model.\nThere was no intended use case for this model besides having some fun seeing how different things might be screwed up.",
"## Data\n\nThis model was trained on \"short\" subset of 'reddit... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mgfrantz/distilgpt2-finetuned-reddit-tifu\n\nThis model was trained to as practice for fine-tuning a causal language model.\nThe... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | AlphaZetta/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T17:00:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4338
- Accuracy: 0.85
- F1: 0.9189
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4338\n- Accuracy: 0.85\n- F1: 0.9189",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | atoivat/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T17:10:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1504
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532142310741495808/VWMu... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/centraldamiku/1654366478559/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/centraldamiku | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-04T17:13:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Central da Miku
@centraldamiku
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-keyword-discriminator
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (\u20ac57bn) cash-and stock deal, massively diversifying the chipmaker\u2019s business a... | yanekyuk/bert-cased-keyword-discriminator | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-04T17:20:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-keyword-discriminator
==========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1310
* Precision: 0.8522
* Recall: 0.8868
* Accuracy: 0.9732
* F1: 0.8692
* Ent/precision: 0.8874
* Ent/accuracy: 0.92... | [
"### 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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
reinforcement-learning | stable-baselines3 |
# **PPO-v1** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-v1** 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 huggingfac... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "Lun... | vjeansel/RI | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-04T17:21:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO-v1 Agent playing LunarLander-v2
This is a trained model of a PPO-v1 agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO-v1 Agent playing LunarLander-v2\nThis is a trained model of a PPO-v1 agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO-v1 Agent playing LunarLander-v2\nThis is a trained model of a PPO-v1 agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: A... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **BipedalWalker-v3**
This is a trained model of a **SAC** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | format37/BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-04T17:27:31+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing BipedalWalker-v3
This is a trained model of a SAC agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# SAC Agent playing BipedalWalker-v3\n This is a trained model of a SAC agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing BipedalWalker-v3\n This is a trained model of a SAC agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kingabzpro/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | kingabzpro/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T17:51:10+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kingabzpro/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | kingabzpro/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-04T17:53:45+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | fastai |
# Model card
## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image.
## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which w... | {"tags": ["fastai"]} | edwinhung/bird_classifier | null | [
"fastai",
"region:us"
] | null | 2022-06-04T18:43:58+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Model card
## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image.
## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which w... | [
"# Model card",
"## Model description\nA neural network model trained with fastai and timm to classify 400 bird species in an image.",
"## Intended uses & limitations\nThis bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classifica... | [
"TAGS\n#fastai #region-us \n",
"# Model card",
"## Model description\nA neural network model trained with fastai and timm to classify 400 bird species in an image.",
"## Intended uses & limitations\nThis bird classifier is used to predict bird species in a given image. The Image fed should have only one bird.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.