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sentence-similarity | sentence-transformers |
# hunkim/sentence-transformersklue-bert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hunkim/sentence-transformersklue-bert-base | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T05:39:14+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# hunkim/sentence-transformersklue-bert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformer... | [
"# hunkim/sentence-transformersklue-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-t... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# hunkim/sentence-transformersklue-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used f... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-colabrathee-intel
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colabrathee-intel", "results": []}]} | pravesh/wav2vec2-large-xls-r-300m-hindi-colabrathee-intel | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T05:40:06+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-colabrathee-intel
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# wav2vec2-large-xls-r-300m-hindi-colabrathee-intel\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi-colabrathee-intel\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voi... |
sentence-similarity | sentence-transformers |
# hunkim/sentence-transformers-klue-bert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Usin... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hunkim/sentence-transformers-klue-bert-base | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T05:46:17+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# hunkim/sentence-transformers-klue-bert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# hunkim/sentence-transformers-klue-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# hunkim/sentence-transformers-klue-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
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/1369269405411139584/B6xO... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/skeptikons/1657445759728/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/skeptikons | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T05:56:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Eddie
@skeptikons
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-classification | transformers |
# Example
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('ddobokki/electra-small-sts-cross-encoder')
model.predict(["그녀는 행복해서 웃었다.", "그녀는 웃겨서 눈물이 났다."])
-> 0.8206561
```
# Dataset
- KorSTS
- Train
- Test
- KLUE STS
- Train
- Test
# Performance
| Dataset | Pearson corr.|Spearman c... | {"language": ["ko"], "tags": ["sentence_transformers", "cross_encoder"]} | ddobokki/electra-small-sts-cross-encoder | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"sentence_transformers",
"cross_encoder",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T06:23:50+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #text-classification #sentence_transformers #cross_encoder #ko #autotrain_compatible #endpoints_compatible #region-us
| Example
=======
Dataset
=======
* KorSTS
+ Train
+ Test
* KLUE STS
+ Train
+ Test
Performance
===========
Dataset: KorSTS(test) + KLUE STS(test), Pearson corr.: 0.8528, Spearman corr.: 0.8504
TODO
====
Using KLUE 1.1 train, dev data
| [] | [
"TAGS\n#transformers #pytorch #electra #text-classification #sentence_transformers #cross_encoder #ko #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e3
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-science-v3-e3", "results": []}]} | theojolliffe/bart-cnn-science-v3-e3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T06:25:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e3
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8586
* Rouge1: 53.3497
* Rouge2: 34.0001
* Rougel: 35.6149
* Rougelsum: 50.5723
* Gen Len: 141.3519
Model desc... | [
"### 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: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
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. -->
# wav2vec2-15
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-15", "results": []}]} | chrisvinsen/wav2vec2-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:01:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-15
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8623
* Wer: 0.8585
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32... |
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... | OneFly/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 | 2022-05-31T07:27:40+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: 0.1372
* 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 #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\\_... |
text-generation | transformers |
This classification model is based on [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
- `relevance`: is the last message in the dialogue relevant in th... | {"language": ["ru"], "license": "mit", "tags": ["conversational"], "widget": [{"text": "[CLS]\u043f\u0440\u0438\u0432\u0435\u0442[SEP]\u043f\u0440\u0438\u0432\u0435\u0442![SEP]\u043a\u0430\u043a \u0434\u0435\u043b\u0430?[RESPONSE_TOKEN]\u0441\u0443\u043f\u0435\u0440, \u0432\u043e\u0442 \u0442\u043e\u043b\u044c\u043a\u0... | tinkoff-ai/response-quality-classifier-tiny | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"conversational",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T07:32:08+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This classification model is based on cointegrated/rubert-tiny2.
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
* 'relevance': is the last message in the dialogue relevant in the context of the full dialogue.
* 'specificity': i... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #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/1476611165157355521/-lvl... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/hellokitty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T07:34:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Hello Kitty
@hellokitty
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. -->
# bart-cnn-science-v3-e4
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-science-v3-e4", "results": []}]} | theojolliffe/bart-cnn-science-v3-e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:36:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e4
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8265
* Rouge1: 53.0296
* Rouge2: 33.4957
* Rougel: 35.8876
* Rougelsum: 50.0786
* Gen Len: 141.5926
Model desc... | [
"### 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: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
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. -->
# electra-base-discriminator-finetuned-removed-0530
This model is a fine-tuned version of [google/electra-base-discriminator](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electra-base-discriminator-finetuned-removed-0530", "results": []}]} | YeRyeongLee/electra-base-discriminator-finetuned-removed-0530 | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:40:07+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-removed-0530
=================================================
This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9713
* Accuracy: 0.8824
* F1: 0.8824
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n... |
text-classification | transformers |
## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available [here](https://research.ibm.com/haifa/dept/vst/debating_data.shtml) and also on 🤗 Transformer datasets hub [here](https://huggingface.co/datasets/ibm/vira-intents).
The model was created as ... | {"language": ["en"], "license": "other", "tags": ["intent detection"], "datasets": ["ibm/vira-intents"], "metrics": ["accuracy"], "widget": [{"text": "Should I be concerned about side effects of the vaccine if I'm breastfeeding?} & Is breastfeeding safe with the vaccine", "example_title": "Breastfeeding"}, {"text": "Do... | ibm/roberta-large-vira-intents | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"intent detection",
"en",
"dataset:ibm/vira-intents",
"arxiv:2205.11966",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:40:27+00:00 | [
"2205.11966"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #intent detection #en #dataset-ibm/vira-intents #arxiv-2205.11966 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on Transformer datasets hub here.
The model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine He... | [
"## Model Description\nThis model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on Transformer datasets hub here.\n\nThe model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vac... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #intent detection #en #dataset-ibm/vira-intents #arxiv-2205.11966 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Description\nThis model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expres... |
feature-extraction | transformers |
# ernie-health-zh
## Introduction
ERNIE-health is a Chinese biomedical language model pre-trained from in-domain text of de-identified online doctor-patient dialogues, electronic medical records, and textbooks.
More detail:
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-health/
https://arxiv... | {"language": "zh"} | nghuyong/ernie-health-zh | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2110.07244",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:43:33+00:00 | [
"2110.07244"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #feature-extraction #zh #arxiv-2110.07244 #endpoints_compatible #region-us
| ernie-health-zh
===============
Introduction
------------
ERNIE-health is a Chinese biomedical language model pre-trained from in-domain text of de-identified online doctor-patient dialogues, electronic medical records, and textbooks.
More detail:
URL
URL
Released Model Info
-------------------
This released... | [] | [
"TAGS\n#transformers #pytorch #ernie #feature-extraction #zh #arxiv-2110.07244 #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. -->
# sarcasm-detection-xlnet-base-cased
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-ca... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-xlnet-base-cased", "results": []}]} | jkhan447/sarcasm-detection-xlnet-base-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T07:50:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-xlnet-base-cased
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1470
- Accuracy: 0.7117
## Model description
More information needed
## Intended uses & limitations
More information needed
## Trai... | [
"# sarcasm-detection-xlnet-base-cased\n\nThis model is a fine-tuned version of xlnet-base-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.1470\n- Accuracy: 0.7117",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-xlnet-base-cased\n\nThis model is a fine-tuned version of xlnet-base-cased on the None dataset.\nIt achieves the following re... |
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/1136186352268132354/PEn3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/xvbones/1653987207699/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/xvbones | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T07:50:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
tommy 🇬🇧
@xvbones
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"
] |
sentence-similarity | sentence-transformers |
# moshew/paraphrase-mpnet-base-v2_SetFit_sst2_nun_training_64
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Tra... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | moshew/paraphrase-mpnet-base-v2_SetFit_sst2_nun_training_64 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T08:24:02+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# moshew/paraphrase-mpnet-base-v2_SetFit_sst2_nun_training_64
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sen... | [
"# moshew/paraphrase-mpnet-base-v2_SetFit_sst2_nun_training_64\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when yo... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# moshew/paraphrase-mpnet-base-v2_SetFit_sst2_nun_training_64\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space... |
null | transformers |
## Multilingual-clip: LABSE-Vit-L-14
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-L-14` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.c... | {"language": "multilingual"} | M-CLIP/LABSE-Vit-L-14 | null | [
"transformers",
"pytorch",
"tf",
"multilingual",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T08:40:25+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #multilingual #endpoints_compatible #has_space #region-us
| Multilingual-clip: LABSE-Vit-L-14
---------------------------------
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model 'ViT-L-14' can be retrieved via instructions found on OpenAI's CLIP reposito... | [] | [
"TAGS\n#transformers #pytorch #tf #multilingual #endpoints_compatible #has_space #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. -->
# bert-base-uncased-squad-v1.0-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-squad-v1.0-finetuned", "results": []}]} | kamalkraj/bert-base-uncased-squad-v1.0-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T08:42:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-squad-v1.0-finetuned
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# bert-base-uncased-squad-v1.0-finetuned\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-squad-v1.0-finetuned\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description... |
image-segmentation | null |
# Demo models for
**Try in [Hugging Face Spaces](https://huggingface.co/spaces/matjesg/deepflash2)** 🤗🤗🤗
- **Task**: Image Segmentation / Semantic Segmentation
- **Paper**: The preprint of our pa... | {"license": "apache-2.0", "tags": ["image-segmentation", "semantic-segmentation", "deepflash2"], "datasets": ["cFOS in HC", "YFP in CTX"]} | matjesg/deepflash2_demo | null | [
"onnx",
"image-segmentation",
"semantic-segmentation",
"deepflash2",
"arxiv:2111.06693",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-31T08:43:39+00:00 | [
"2111.06693"
] | [] | TAGS
#onnx #image-segmentation #semantic-segmentation #deepflash2 #arxiv-2111.06693 #license-apache-2.0 #has_space #region-us
|
# Demo models for
!deepflash2
Try in Hugging Face Spaces
- Task: Image Segmentation / Semantic Segmentation
- Paper: The preprint of our paper is available on arXiv
- Data: The cFOS in HC dataset (Article, Data) describes the indirect immunofluorescent labeling of the transcription factor cFOS in different subreg... | [
"# Demo models for\n\n!deepflash2\n\nTry in Hugging Face Spaces \n\n- Task: Image Segmentation / Semantic Segmentation\n- Paper: The preprint of our paper is available on arXiv\n- Data: The cFOS in HC dataset (Article, Data) describes the indirect immunofluorescent labeling of the transcription factor cFOS in diff... | [
"TAGS\n#onnx #image-segmentation #semantic-segmentation #deepflash2 #arxiv-2111.06693 #license-apache-2.0 #has_space #region-us \n",
"# Demo models for\n\n!deepflash2\n\nTry in Hugging Face Spaces \n\n- Task: Image Segmentation / Semantic Segmentation\n- Paper: The preprint of our paper is available on arXiv\n- ... |
null | transformers |
## Multilingual-clip: XLM-Roberta-Large-Vit-B-32
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-B-32` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](http... | {"language": ["multilingual", "af", "sq", "am", "ar", "az", "bn", "bs", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fr", "de", "el", "hi", "hu", "is", "id", "it", "ja", "mk", "ml", "mr", "pl", "pt", "ro", "ru", "sr", "sl", "es", "sw", "sv", "tl", "te", "tr", "tk", "uk", "ur", "ug", "uz", "vi", "xh"]} | M-CLIP/XLM-Roberta-Large-Vit-B-32 | null | [
"transformers",
"pytorch",
"tf",
"M-CLIP",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
... | null | 2022-05-31T08:50:54+00:00 | [] | [
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"t... | TAGS
#transformers #pytorch #tf #M-CLIP #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #region-us
| Multilingual-clip: XLM-Roberta-Large-Vit-B-32
---------------------------------------------
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model 'ViT-B-32' can be retrieved via instructions found o... | [] | [
"TAGS\n#transformers #pytorch #tf #M-CLIP #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# Hey | {"tags": ["yasas"]} | patrickvonplaten/ddpm_dummy | null | [
"transformers",
"unet",
"yasas",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T08:59:56+00:00 | [] | [] | TAGS
#transformers #unet #yasas #endpoints_compatible #region-us
|
# Hey | [
"# Hey"
] | [
"TAGS\n#transformers #unet #yasas #endpoints_compatible #region-us \n",
"# Hey"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e5
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-science-v3-e5", "results": []}]} | theojolliffe/bart-cnn-science-v3-e5 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T09:00:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e5
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8090
* Rouge1: 54.0053
* Rouge2: 35.5018
* Rougel: 37.3204
* Rougelsum: 51.5456
* Gen Len: 142.0
Model descrip... | [
"### 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: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
text-to-speech | fairseq | # fastspeech2-mf4 | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | Voicemod/fastspeech2-mf4 | null | [
"fairseq",
"audio",
"text-to-speech",
"en",
"has_space",
"region:us"
] | null | 2022-05-31T09:01:24+00:00 | [] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #en #has_space #region-us
| # fastspeech2-mf4 | [
"# fastspeech2-mf4"
] | [
"TAGS\n#fairseq #audio #text-to-speech #en #has_space #region-us \n",
"# fastspeech2-mf4"
] |
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. -->
# sber-framebank-50size-2
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-a... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "sber-framebank-50size-2", "results": []}]} | ruselkomp/sber-framebank-50size-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T09:03:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| sber-framebank-50size-2
=======================
This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3736
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 4\n* eval\\_batch\\_size: 4\n* seed:... |
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. -->
# PathologyBERT-meningioma
This model is a fine-tuned version of [tsantos/PathologyBERT](https://huggingface.co/tsantos/PathologyB... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "PathologyBERT-meningioma", "results": []}]} | Santarabantoosoo/PathologyBERT-meningioma | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T09:14:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| PathologyBERT-meningioma
========================
This model is a fine-tuned version of tsantos/PathologyBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8123
* Accuracy: 0.8783
* Precision: 0.25
* Recall: 0.0833
* F1: 0.125
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
text-generation | transformers |
This classification model is based on [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence).
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
- `relevance`: is the last message in the... | {"language": ["ru"], "license": "mit", "tags": ["conversational"], "widget": [{"text": "[CLS]\u043f\u0440\u0438\u0432\u0435\u0442[SEP]\u043f\u0440\u0438\u0432\u0435\u0442![SEP]\u043a\u0430\u043a \u0434\u0435\u043b\u0430?[RESPONSE_TOKEN]\u0441\u0443\u043f\u0435\u0440, \u0432\u043e\u0442 \u0442\u043e\u043b\u044c\u043a\u0... | tinkoff-ai/response-quality-classifier-base | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"conversational",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T09:17:12+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This classification model is based on DeepPavlov/rubert-base-cased-sentence.
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
* 'relevance': is the last message in the dialogue relevant in the context of the full dialogue.
* 'spe... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
This classification model is based on [sberbank-ai/ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large).
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
- `relevance`: is the last message in the dialogue relevant i... | {"language": ["ru"], "license": "mit", "tags": ["conversational"], "widget": [{"text": "[CLS]\u043f\u0440\u0438\u0432\u0435\u0442[SEP]\u043f\u0440\u0438\u0432\u0435\u0442![SEP]\u043a\u0430\u043a \u0434\u0435\u043b\u0430?[RESPONSE_TOKEN]\u0441\u0443\u043f\u0435\u0440, \u0432\u043e\u0442 \u0442\u043e\u043b\u044c\u043a\u0... | tinkoff-ai/response-quality-classifier-large | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"conversational",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T09:18:01+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #roberta #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This classification model is based on sberbank-ai/ruRoberta-large.
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
* 'relevance': is the last message in the dialogue relevant in the context of the full dialogue.
* 'specificity':... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Sicko-Code/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": ... | Sicko-Code/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-31T09:21:39+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"
] |
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-uncased-squad-v2.0-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "bert-base-uncased-squad-v2.0-finetuned", "results": []}]} | kamalkraj/bert-base-uncased-squad-v2.0-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T09:48:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-squad-v2.0-finetuned
This model is a fine-tuned version of bert-base-uncased 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
### Trai... | [
"# bert-base-uncased-squad-v2.0-finetuned\n\nThis model is a fine-tuned version of bert-base-uncased 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-squad-v2.0-finetuned\n\nThis model is a fine-tuned version of bert-base-uncased on the squad_v2 dataset.",
"## Model descr... |
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"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | wuxiaofei/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:19:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6787
- Accuracy: 0.86
- F1: 0.8636
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6787\n- Accuracy: 0.86\n- F1: 0.8636",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #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 the imdb ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# clementgyj/bert-finetuned-squad-50k
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "clementgyj/bert-finetuned-squad-50k", "results": []}]} | clementgyj/bert-finetuned-squad-50k | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:23:52+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| clementgyj/bert-finetuned-squad-50k
===================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5470
* Epoch: 2
Model description
-----------------
More information needed
Intended use... | [
"### 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': 2e-05, 'decay\\_steps': 9486, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
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/1529814669493682176/BqZU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/binance-dydx-magiceden/1653996837144/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/binance-dydx-magiceden | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T10:31:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Magic Eden & Binance & dYdX
@binance-dydx-magiceden
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"
] |
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. -->
# wav2vec2-16
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-16", "results": []}]} | chrisvinsen/wav2vec2-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:32:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-16
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1016
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e6
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-science-v3-e6", "results": []}]} | theojolliffe/bart-cnn-science-v3-e6 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:35:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e6
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8057
* Rouge1: 53.7462
* Rouge2: 34.9622
* Rougel: 37.5676
* Rougelsum: 51.0619
* Gen Len: 142.0
Model descrip... | [
"### 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: 6\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
null | transformers | !---
# ##############################################################################################
#
# This model has been uploaded to HuggingFace by https://huggingface.co/drAbreu
# The model is based on the NVIDIA checkpoint located at
# https://catalog.ngc.nvidia.com/orgs/nvidia/models/biomegatron345mcased
#
... | {"language": ["english"], "license": "cc-by-4.0", "tags": ["language model"]} | EMBO/BioMegatron345mCased | null | [
"transformers",
"pytorch",
"megatron-bert",
"language model",
"arxiv:2010.06060",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:38:39+00:00 | [
"2010.06060"
] | [
"english"
] | TAGS
#transformers #pytorch #megatron-bert #language model #arxiv-2010.06060 #license-cc-by-4.0 #endpoints_compatible #region-us
| !---
# ##############################################################################################
#
# This model has been uploaded to HuggingFace by URL
# The model is based on the NVIDIA checkpoint located at
# URL
#
# ############################################################################################... | [
"# ##############################################################################################",
"#",
"# This model has been uploaded to HuggingFace by URL",
"# The model is based on the NVIDIA checkpoint located at",
"# URL",
"# #########################################################################... | [
"TAGS\n#transformers #pytorch #megatron-bert #language model #arxiv-2010.06060 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# ##############################################################################################",
"#",
"# This model has been uploaded to HuggingFace by URL",
"# The mode... |
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/1529814669493682176/BqZU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/magiceden/1653997534626/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/magiceden | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T10:42:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Magic Eden
@magiceden
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | batya66/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:45:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0622
* Precision: 0.9288
* Recall: 0.9483
* F1: 0.9385
* Accuracy: 0.9859
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# train_NER_M_V1
This model is a fine-tuned version of [FritzOS/train_basic_M_V3](https://huggingface.co/FritzOS/train_basic_M_V3) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "train_NER_M_V1", "results": []}]} | FritzOS/train_NER_M_V1 | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T10:51:30+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| train\_NER\_M\_V1
=================
This model is a fine-tuned version of FritzOS/train\_basic\_M\_V3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0025
* Validation Loss: 0.0024
* Epoch: 0
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learn... |
null | null | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/378
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Alimeeting dataset with icefall.
The model was trained on the far data of [Alime... | {} | luomingshuang/icefall_asr_alimeeting_pruned_transducer_stateless2 | null | [
"has_space",
"region:us"
] | null | 2022-05-31T11:00:00+00:00 | [] | [] | TAGS
#has_space #region-us
| Note: This recipe is trained with the codes from this PR URL
And the SpecAugment codes from this PR URL
Pre-trained Transducer-Stateless2 models for the Alimeeting dataset with icefall.
=================================================================================
The model was trained on the far data of Alimeet... | [] | [
"TAGS\n#has_space #region-us \n"
] |
null | transformers | !---
# ##############################################################################################
#
# This model has been uploaded to HuggingFace by https://huggingface.co/drAbreu
# The model is based on the NVIDIA checkpoint located at
# https://catalog.ngc.nvidia.com/orgs/nvidia/models/biomegatron345muncased
... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["language model"]} | EMBO/BioMegatron345mUncased | null | [
"transformers",
"pytorch",
"megatron-bert",
"language model",
"en",
"arxiv:2010.06060",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T11:18:55+00:00 | [
"2010.06060"
] | [
"en"
] | TAGS
#transformers #pytorch #megatron-bert #language model #en #arxiv-2010.06060 #license-cc-by-4.0 #endpoints_compatible #region-us
| !---
# ##############################################################################################
#
# This model has been uploaded to HuggingFace by URL
# The model is based on the NVIDIA checkpoint located at
# URL
#
# ############################################################################################... | [
"# ##############################################################################################",
"#",
"# This model has been uploaded to HuggingFace by URL",
"# The model is based on the NVIDIA checkpoint located at",
"# URL",
"# #########################################################################... | [
"TAGS\n#transformers #pytorch #megatron-bert #language model #en #arxiv-2010.06060 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# ##############################################################################################",
"#",
"# This model has been uploaded to HuggingFace by URL",
"# The ... |
null | transformers |
# Dummy diffusion model following architecture of https://github.com/lucidrains/denoising-diffusion-pytorch
Run the model as follows:
```python
from diffusers import UNetModel, GaussianDiffusion
import torch
# 1. Load model
unet = UNetModel.from_pretrained("fusing/ddpm_dummy")
# 2. Do one denoising step with model... | {"tags": ["hf_diffuse"]} | diffusers/ddpm_dummy | null | [
"transformers",
"hf_diffuse",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T11:37:35+00:00 | [] | [] | TAGS
#transformers #hf_diffuse #endpoints_compatible #has_space #region-us
|
# Dummy diffusion model following architecture of URL
Run the model as follows:
| [
"# Dummy diffusion model following architecture of URL\n\nRun the model as follows:"
] | [
"TAGS\n#transformers #hf_diffuse #endpoints_compatible #has_space #region-us \n",
"# Dummy diffusion model following architecture of URL\n\nRun the model as follows:"
] |
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/1503378148544720896/cqXt... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/botphilosophyq-philosophical_9-philosophy_life/1654001783159/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/botphilosophyq-philosophical_9-philosophy_life | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T11:54:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI CYBORG
Philosophy Quotes & Philosophy Quotes & philosophy for life
@botphilosophyq-philosophical\_9-philosophy\_life
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 un... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | sentence-transformers |
# LegalBERTPT-br
LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased).
# Corpora
– From [this site](https://www2.camara.leg.br/transparen... | {"language": "pt", "license": "mit", "tags": ["sentence-transformers"]} | ulysses-camara/legal-bert-pt-br | null | [
"sentence-transformers",
"pt",
"license:mit",
"region:us"
] | null | 2022-05-31T12:30:11+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #pt #license-mit #region-us
|
# LegalBERTPT-br
LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau.
# Corpora
– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/2019, PEC 47... | [
"# LegalBERTPT-br\n\nLegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau.",
"# Corpora\n\n– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/20... | [
"TAGS\n#sentence-transformers #pt #license-mit #region-us \n",
"# LegalBERTPT-br\n\nLegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau.",
"# Corpora\n\n– From this site, we used the column 'Conteud... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Cole/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T13:14:51+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2148
* Accuracy: 0.9275
* F1: 0.9274
Model description
-----------------
Mo... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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-vios-v1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-vios-v1", "results": []}]} | tclong/wav2vec2-base-vios-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T13:48:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-vios-v1
=====================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6352
* Wer: 0.5161
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | arrandi/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T14:03:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1652
* Accuracy: 0.934
* F1: 0.9342
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-4
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-4", "results": []}]} | malra/segformer-b0-finetuned-segments-sidewalk-4 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T14:22:56+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-4
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5207
* Mean Iou: 0.1023
* Mean Accuracy: 0.1567
* Overall Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
text-generation | transformers |
# response-toxicity-classifier-base
[BERT classifier from Skoltech](https://huggingface.co/Skoltech/russian-inappropriate-messages), finetuned on contextual data with 4 labels.
# Training
[*Skoltech/russian-inappropriate-messages*](https://huggingface.co/Skoltech/russian-inappropriate-messages) was finetuned on a m... | {"language": ["ru"], "license": "mit", "tags": ["russian", "pretraining", "conversational"], "widget": [{"text": "[CLS] \u043f\u0440\u0438\u0432\u0435\u0442 [SEP] \u043f\u0440\u0438\u0432\u0435\u0442! [SEP] \u043a\u0430\u043a \u0434\u0435\u043b\u0430? [RESPONSE_TOKEN] \u043d\u043e\u0440\u043c", "example_title": "Dialog... | tinkoff-ai/response-toxicity-classifier-base | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"russian",
"pretraining",
"conversational",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T14:33:57+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #text-classification #russian #pretraining #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| response-toxicity-classifier-base
=================================
BERT classifier from Skoltech, finetuned on contextual data with 4 labels.
Training
========
*Skoltech/russian-inappropriate-messages* was finetuned on a multiclass data with four classes (*check the exact mapping between idx and label in* 'URL')... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #russian #pretraining #conversational #ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | joaogante/test_img | null | [
"transformers",
"pytorch",
"jax",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"region:us"
] | null | 2022-05-31T14:40:15+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repo... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in thi... | [
"TAGS\n#transformers #pytorch #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolutio... |
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 [federicopascual/finetuning-sentiment-model-3000-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | yukta10/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T14:51:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of federicopascual/finetuning-sentiment-model-3000-samples on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information need... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of federicopascual/finetuning-sentiment-model-3000-samples on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #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 federicopascual/finetuning-sentiment... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-segments-warehouse1
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on ... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b5-segments-warehouse1", "results": []}]} | malra/segformer-b5-segments-warehouse1 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T15:02:39+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b5-segments-warehouse1
================================
This model is a fine-tuned version of nvidia/mit-b5 on the jakka/warehouse\_part1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1610
* Mean Iou: 0.6952
* Mean Accuracy: 0.8014
* Overall Accuracy: 0.9648
* Per Category Io... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Trainin... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batc... |
fill-mask | transformers |
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/master... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | joaogante/test_text | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"distilbert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T15:02:39+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #distilbert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT base model (uncased)
===============================
This model is a distilled version of the BERT base model. It was
introduced in this paper. The code for the distillation process can be found
here. This model is uncased: it does
not make a difference between english and English.
Model description
----... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #distilbert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-warehouse-part-1-V2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/m... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-warehouse-part-1-V2", "results": []}]} | jakka/segformer-b0-finetuned-warehouse-part-1-V2 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T15:08:04+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| segformer-b0-finetuned-warehouse-part-1-V2
==========================================
This model is a fine-tuned version of nvidia/mit-b5 on the jakka/warehouse\_part1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2737
* Mean Iou: 0.7224
* Mean Accuracy: 0.8119
* Overall Accuracy: 0.96... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-robust-ft-timit
This model is a fine-tuned version of [facebook/wav2vec2-large-robust](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-robust-ft-timit", "results": []}]} | wrice/wav2vec2-large-robust-ft-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T15:21:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-robust-ft-timit
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-robust on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2768
* Wer: 0.2321
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
token-classification | spacy |
| Feature | Description |
| --- | --- |
| **Name** | `dataset-references` |
| **Version** | n/a |
| **spaCy** | `3.1.1` |
| **Components** | `transformer`, `ner` |
| **License** | `CC` |
| **Author** | [Sara Lafia](saralafia.com) | | {"language": ["en"], "license": "cc", "library_name": "spacy", "tags": ["spacy", "token-classification"], "inference": false} | lafias/dataset-references | null | [
"spacy",
"token-classification",
"en",
"license:cc",
"model-index",
"region:us"
] | null | 2022-05-31T16:09:29+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-cc #model-index #region-us
| [] | [
"TAGS\n#spacy #token-classification #en #license-cc #model-index #region-us \n"
] | |
tabular-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 934630783
- CO2 Emissions (in grams): 38.42484725553464
## Validation Metrics
- Loss: 0.2984429822985684
- Accuracy: 0.8628221244500315
- Precision: 0.7873263888888888
- Recall: 0.5908794788273616
- AUC: 0.9182195921357326
- F1: 0.675... | {"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["rajistics/autotrain-data-Adult"], "co2_eq_emissions": 38.42484725553464} | rajistics/autotrain-Adult-934630783 | null | [
"transformers",
"joblib",
"extra_trees",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:rajistics/autotrain-data-Adult",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T16:54:27+00:00 | [] | [] | TAGS
#transformers #joblib #extra_trees #autotrain #tabular #classification #tabular-classification #dataset-rajistics/autotrain-data-Adult #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 934630783
- CO2 Emissions (in grams): 38.42484725553464
## Validation Metrics
- Loss: 0.2984429822985684
- Accuracy: 0.8628221244500315
- Precision: 0.7873263888888888
- Recall: 0.5908794788273616
- AUC: 0.9182195921357326
- F1: 0.675... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 934630783\n- CO2 Emissions (in grams): 38.42484725553464",
"## Validation Metrics\n\n- Loss: 0.2984429822985684\n- Accuracy: 0.8628221244500315\n- Precision: 0.7873263888888888\n- Recall: 0.5908794788273616\n- AUC: 0.9182195921... | [
"TAGS\n#transformers #joblib #extra_trees #autotrain #tabular #classification #tabular-classification #dataset-rajistics/autotrain-data-Adult #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 934630783\n- CO2 Emissions (i... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-finetuned-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["un_multi"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-finetuned-en-to-ar", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "un_multi", "type": "un_m... | meghazisofiane/opus-mt-en-ar-finetuned-en-to-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T17:13:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-finetuned-en-to-ar
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the un\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8133
* Bleu: 64.6767
* Gen Len: 17.595
Model description
-----------------
More informat... | [
"### 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: 16\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb-demo
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-imdb-demo", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics":... | eugenecamus/distilbert-imdb-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T18:06:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-imdb-demo
====================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4328
* Accuracy: 0.928
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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="arampacha/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": ... | arampacha/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-31T18:30:03+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="arampacha/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": "7.48 +/... | arampacha/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-31T18:31:34+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"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ThePixOne/SeconBERTa | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T18:48:48+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or... |
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. -->
# test-recipe
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## M... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "test-recipe", "results": []}]} | Dizzykong/test-recipe | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T19:42:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test-recipe
This model is a fine-tuned version of gpt2-medium 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... | [
"# test-recipe\n\nThis model is a fine-tuned version of gpt2-medium 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 ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test-recipe\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information ... |
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/1459213153301053442/rL5h... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gretathunberg/1663110082774/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gretathunberg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T19:58:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Greta Thunberg
@gretathunberg
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 |
<!-- 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-tg
This model is a fine-tuned version of [Tajik-Corpus](https://huggingface.co/datasets/muhtasham/tajik-corpus) dataset ... | {"language": ["tg"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u041f\u043e\u0439\u0442\u0430\u0445\u0442\u0438 <mask> \u0414\u0443\u0448\u0430\u043d\u0431\u0435"}, {"text": "<mask> \u0431\u0430 \u0438\u043d \u0441\u0430\u0439\u0442\u0438 \u0448\u0443\u043c\u043e \u043c\u0435\u0434\u0430\u0440\u043e\u044... | muhtasham/RoBERTa-tg | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"tg",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T20:06:31+00:00 | [] | [
"tg"
] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #tg #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-tg
This model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora.
## Model description
You can use model for masked text generation or fine-tune it to a downstream task.
## Intended uses & limitations
More information needed
## Training and evaluation data
More inform... | [
"# RoBERTa-tg\n\nThis model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora.",
"## Model description\n\nYou can use model for masked text generation or fine-tune it to a downstream task.",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluatio... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #tg #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-tg\n\nThis model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora.",
"## Model description\n\nYou can use model for masked text ge... |
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. -->
# test-charles-dickens
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "test-charles-dickens", "results": []}]} | Dizzykong/test-charles-dickens | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T20:10:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test-charles-dickens
This model is a fine-tuned version of gpt2 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 followi... | [
"# test-charles-dickens\n\nThis model is a fine-tuned version of gpt2 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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test-charles-dickens\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMo... |
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. -->
# my-awesome-model-3
This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italia... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-awesome-model-3", "results": []}]} | Simon10/my-awesome-model-3 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T20:20:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| my-awesome-model-3
==================
This model is a fine-tuned version of dbmdz/bert-base-italian-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2061
* Validation Loss: 0.0632
* Epoch: 0
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_n... |
text-generation | transformers | # My Story model
Arthur goes to the beach. Arthur is feeling very hot and bored. He decides to go to the beach. He goes to the beach. He spends the day swimming. Arthur cannot wait for the next day to go swimming.
Arthur goes to the beach. Arthur wants to go to the beach. He gets a map. He looks at the map. He goes t... | {} | jppaolim/v39_Best20Epoch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T20:32:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
Arthur goes to the beach. Arthur is feeling very hot and bored. He decides to go to the beach. He goes to the beach. He spends the day swimming. Arthur cannot wait for the next day to go swimming.
Arthur goes to the beach. Arthur wants to go to the beach. He gets a map. He looks at the map. He goes t... | [
"# My Story model\nArthur goes to the beach. Arthur is feeling very hot and bored. He decides to go to the beach. He goes to the beach. He spends the day swimming. Arthur cannot wait for the next day to go swimming. \nArthur goes to the beach. Arthur wants to go to the beach. He gets a map. He looks at the map. He... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\nArthur goes to the beach. Arthur is feeling very hot and bored. He decides to go to the beach. He goes to the beach. He spends the day swimming. Arthur can... |
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... | skr3178/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 | 2022-05-31T20:47:53+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: 0.1363
* F1: 0.8627
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 #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\\_... |
text-generation | transformers | # My Story model
Arthur goes to the beach. Arthur is in the ocean. He is enjoying the water. He cannot wait for the sun to rise. He goes to the beach. It is very hot outside.
Arthur goes to the beach. Arthur is going to the beach. He is going to the beach. He is going to go swimming. He feels a breeze on his shirt. H... | {} | jppaolim/v40_NeoSmall | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T21:11:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # My Story model
Arthur goes to the beach. Arthur is in the ocean. He is enjoying the water. He cannot wait for the sun to rise. He goes to the beach. It is very hot outside.
Arthur goes to the beach. Arthur is going to the beach. He is going to the beach. He is going to go swimming. He feels a breeze on his shirt. H... | [
"# My Story model\nArthur goes to the beach. Arthur is in the ocean. He is enjoying the water. He cannot wait for the sun to rise. He goes to the beach. It is very hot outside. \nArthur goes to the beach. Arthur is going to the beach. He is going to the beach. He is going to go swimming. He feels a breeze on his s... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# My Story model\nArthur goes to the beach. Arthur is in the ocean. He is enjoying the water. He cannot wait for the sun to rise. He goes to the beach. It is very hot outside. \nArthur goes to the... |
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": []}]} | skr3178/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 | 2022-05-31T21:14:05+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: 0.1644
* F1: 0.8617
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | caldana/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T21:16:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2236
* Accuracy: 0.927
* F1: 0.9271
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-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-DM
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-DM", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args... | mccaffary/finetuning-sentiment-model-3000-samples-DM | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T21:26:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples-DM
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3248
- Accuracy: 0.8667
- F1: 0.8734
## Model description
More information needed
## Intended uses & limitations
More... | [
"# finetuning-sentiment-model-3000-samples-DM\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3248\n- Accuracy: 0.8667\n- F1: 0.8734",
"## Model description\n\nMore information needed",
"## Intended uses &... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples-DM\n\nThis model is a fine-tuned version of distilbert-base-unca... |
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_multilingual_XLSum-sumarizacao-PTBR
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggin... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mT5_multilingual_XLSum-sumarizacao-PTBR", "results": []}]} | GiordanoB/mT5_multilingual_XLSum-sumarizacao-PTBR | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T21:32:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-sumarizacao-PTBR
=========================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3870
* Rouge1: 42.0195
* Rouge2: 24.9493
* Rougel: 32.3653
* Rougels... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
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... | skr3178/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 | 2022-05-31T21:38:23+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: 0.2867
* F1: 0.8355
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | skr3178/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 | 2022-05-31T21:57:02+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: 0.2421
* F1: 0.8248
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-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... | skr3178/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 | 2022-05-31T22:14:17+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: 0.3921
* F1: 0.6922
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text2text-generation | transformers |
This model is a fine-tune checkpoint of [T5-small](https://huggingface.co/t5-small), fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral p... | {"language": ["en"], "license": "apache-2.0", "datasets": ["WNC"], "metrics": ["accuracy"]} | erickfm/t5-small-finetuned-bias | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WNC",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T22:29:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a fine-tune checkpoint of T5-small, fine-tuned on the Wiki Neutrality Corpus (WNC), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model reaches an accuracy of 0.32 on a dev split of the WNC.
Fo... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #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. -->
# 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": []}]} | skr3178/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 | 2022-05-31T22:31:21+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: 0.1752
* F1: 0.8557
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-gender_classification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-gender_classification", "results": []}]} | Abderrahim2/bert-finetuned-gender_classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T23:12:03+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-finetuned-gender\_classification
=====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1484
* F1: 0.9645
* Roc Auc: 0.9732
* Accuracy: 0.964
Model description
-----------------
... | [
"### 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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #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\\_batch\\_s... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/390
| {} | csukuangfj/icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-31T23:17:23+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
| [
"# Introduction\n\nSee URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL"
] |
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. -->
# jiseong/mt5-small-finetuned-news
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jiseong/mt5-small-finetuned-news", "results": []}]} | jiseong/mt5-small-finetuned-news | null | [
"transformers",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T23:47:52+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| jiseong/mt5-small-finetuned-news
================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1208
* Validation Loss: 0.1012
* Epoch: 2
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #mt5 #text2text-generation #generated_from_keras_callback #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* optimizer: {... |
automatic-speech-recognition | 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. -->
# wav2vec2-xls-r-300m-mixed
Finetuned https://huggingface.co/facebook/wav2vec2-xls-r-300m on https://github.com/huseinzol05/malaya-speec... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "wav2vec2-xls-r-300m-mixed", "results": []}]} | mesolitica/wav2vec2-xls-r-300m-mixed | null | [
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T00:18:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #generated_from_keras_callback #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-mixed
Finetuned URL on URL
This model was finetuned on 3 languages,
1. Malay
2. Singlish
3. Mandarin
This model trained on a single RTX 3090 Ti 24GB VRAM, provided by URL
## Evaluation set
Evaluation set from URL with sizes,
It achieves the following results on the evaluation set base... | [
"# wav2vec2-xls-r-300m-mixed\n\nFinetuned URL on URL\n\nThis model was finetuned on 3 languages,\n\n1. Malay\n2. Singlish\n3. Mandarin\n\nThis model trained on a single RTX 3090 Ti 24GB VRAM, provided by URL",
"## Evaluation set\n\nEvaluation set from URL with sizes,\n\n\n\nIt achieves the following results on th... | [
"TAGS\n#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-mixed\n\nFinetuned URL on URL\n\nThis model was finetuned on 3 languages,\n\n1. Malay\n2. Singlish\n3. Mandarin\n\nThis model trained on a single RT... |
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. -->
# wav2vec2-17
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-17", "results": []}]} | chrisvinsen/wav2vec2-17 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T01:17:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-17
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1355
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 3... |
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. -->
# xlmr_mask_punctuation
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an un... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlmr_mask_punctuation", "results": []}]} | jamie613/xlmr_mask_punctuation | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T01:20:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlmr\_mask\_punctuation
=======================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5160
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n... |
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... | Oseias/ppo-LunarLander-v2_review | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T01:25:48+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-to-speech | fastpitch |
**Model card - Kinyarwanda TTS model**
**Model details**
- Kinyarwanda Text to Speech model
- Developed by [Digital Umuganda](digitalumuganda.com), [Arxia](https://www.arxia.com/home.html) and [Zevo Tech](https://zevo-tech.com/)
- Model based from: Fastspeech and Waveglow
- License: Mozilla 2.0 License
- Feedback on ... | {"language": "rw", "library_name": "fastpitch", "tags": ["fastpitch", "waveglow", "text-to-speech"], "datasets": ["mbazaNLP/kinyarwanda-tts-dataset"], "task": "text-to-speech", "widget": [{"text": "Muraho neza, murakaza neza mu Rwanda.", "example_title": "Muraho neza, murakaza neza mu Rwanda."}]} | mbazaNLP/kinyarwanda-tts-model | null | [
"fastpitch",
"waveglow",
"text-to-speech",
"rw",
"dataset:mbazaNLP/kinyarwanda-tts-dataset",
"region:us"
] | null | 2022-06-01T02:42:31+00:00 | [] | [
"rw"
] | TAGS
#fastpitch #waveglow #text-to-speech #rw #dataset-mbazaNLP/kinyarwanda-tts-dataset #region-us
| Model card - Kinyarwanda TTS model
Model details
* Kinyarwanda Text to Speech model
* Developed by Digital Umuganda, Arxia and Zevo Tech
* Model based from: Fastspeech and Waveglow
* License: Mozilla 2.0 License
* Feedback on the model: samuel@URL
Metrics
* We use Mean Opinion Score (MOS) to evaluate the model ... | [] | [
"TAGS\n#fastpitch #waveglow #text-to-speech #rw #dataset-mbazaNLP/kinyarwanda-tts-dataset #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_reviews_with_language_drift
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ecommerce_reviews_with_language_drift"], "metrics": ["accuracy", "f1"], "widget": [{"text": "Poor quality of fabric and ridiculously tight at chest. It's way too short.", "example_title": "Negative"}, {"text": "One worked perfectly, but the oth... | arize-ai/distilbert_reviews_with_language_drift | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ecommerce_reviews_with_language_drift",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T04:46:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ecommerce_reviews_with_language_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_reviews\_with\_language\_drift
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the ecommerce\_reviews\_with\_language\_drift dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4970
* Accuracy: 0.818
* F1: 0.8167
Model... | [
"### 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 #text-classification #generated_from_trainer #dataset-ecommerce_reviews_with_language_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
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": []}]} | adache/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 | 2022-06-01T05:21:05+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: 0.1644
* F1: 0.8617
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
<!-- 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. -->
# SENATOR
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the i... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "SENATOR", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"typ... | RANG012/SENATOR | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T05:51:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# SENATOR
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2707
- Accuracy: 0.916
- F1: 0.9167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
"# SENATOR\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2707\n- Accuracy: 0.916\n- F1: 0.9167",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# SENATOR\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achiev... |
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... | adache/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 | 2022-06-01T05:53:43+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: 0.3196
* F1: 0.8054
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | adache/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 | 2022-06-01T06:14:12+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: 0.2421
* F1: 0.8248
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-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... | adache/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 | 2022-06-01T06:34:03+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: 0.3921
* F1: 0.6922
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ceggian/sbart_pt_reddit_softmax_32 | null | [
"sentence-transformers",
"pytorch",
"bart",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T06:34:31+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bart #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bart #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ceggian/sbart_pt_reddit_softmax_64 | null | [
"sentence-transformers",
"pytorch",
"bart",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T06:43:02+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bart #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bart #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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": []}]} | adache/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 | 2022-06-01T06:54:01+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-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: 0.1782
* F1: 0.8541
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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. -->
# SENATOR-Scaled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "SENATOR-Scaled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics":... | RANG012/SENATOR-Scaled | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T06:55:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# SENATOR-Scaled
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2670
- Accuracy: 0.89
- F1: 0.8898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Traini... | [
"# SENATOR-Scaled\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2670\n- Accuracy: 0.89\n- F1: 0.8898",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# SENATOR-Scaled\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt... |
text-classification | transformers |
# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
**KlimaBERT**, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision ... | {"language": ["da"], "tags": ["climate change", "climate-classifier", "political quotes", "klimabert"]} | jonahank/KlimaBERT | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"climate change",
"climate-classifier",
"political quotes",
"klimabert",
"da",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T07:21:00+00:00 | [
"1810.04805"
] | [
"da"
] | TAGS
#transformers #pytorch #bert #text-classification #climate change #climate-classifier #political quotes #klimabert #da #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
|
# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
KlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0... | [
"# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP\nKlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, \"climate-related\", the model achieves a F1-score of 0.97, Precisi... | [
"TAGS\n#transformers #pytorch #bert #text-classification #climate change #climate-classifier #political quotes #klimabert #da #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP\n... |
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. -->
# jiseong/mt5-small-finetuned-news-ab
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jiseong/mt5-small-finetuned-news-ab", "results": []}]} | jiseong/mt5-small-finetuned-news-ab | null | [
"transformers",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T07:24:29+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| jiseong/mt5-small-finetuned-news-ab
===================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.0174
* Validation Loss: 1.7411
* Epoch: 3
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #mt5 #text2text-generation #generated_from_keras_callback #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* optimizer: {... |
fill-mask | transformers | # SSCI-BERT: A pretrained language model for social scientific text
## Introduction
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a ... | {"license": "apache-2.0"} | KM4STfulltext/SSCI-BERT-e2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T07:59:09+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SSCI-BERT: A pretrained language model for social scientific text
=================================================================
Introduction
------------
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy ... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-SciBERT",
"### Download Models\n\n\n* The version of the model we provide is 'PyTorch'.",
"### From Huggingface\n\n\n* Downlo... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-... |
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