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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers |
# Model Description
* A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes.
* Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health informati... | {"language": ["en"], "license": "mit", "tags": ["deidentification", "medical notes", "ehr", "phi"], "datasets": ["I2B2"], "metrics": ["F1", "Recall", "Precision"], "thumbnail": "https://www.onebraveidea.org/wp-content/uploads/2019/07/OBI-Logo-Website.png", "widget": [{"text": "Physician Discharge Summary Admit date: 10... | obi/deid_roberta_i2b2 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"deidentification",
"medical notes",
"ehr",
"phi",
"en",
"dataset:I2B2",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #deidentification #medical notes #ehr #phi #en #dataset-I2B2 #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Description
=================
* A RoBERTa [[Liu et al., 2019]](URL model fine-tuned for de-identification of medical notes.
* Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information categories is g... | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #deidentification #medical notes #ehr #phi #en #dataset-I2B2 #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers | ----
tags:
- conversational
---
# Doctor strange DialGPT model | {} | obito69/DialoGPT-small-Doctorstrange | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ----
tags:
- conversational
---
# Doctor strange DialGPT model | [
"# Doctor strange DialGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Doctor strange DialGPT model"
] |
text-classification | transformers | # Buy vs Sell Intent Classifier
| Train Loss | Validation Acc.| Test Acc.|
| ------------- |:-------------: | -----: |
| 0.013 | 0.988 | 0.992 |
# Sample Intents for Testings
LABEL_0 => **"SELLING_INTENT"** <br/>
LABEL_1 => **"BUYING_INTENT"**
## Buying Intents
- I am interested in this style of PGN-ES-... | {"language": "en", "tags": ["buy-intent", "sell-intent", "consumer-intent"], "widget": [{"text": "Can you please share pictures for Face Shields ? We are looking for large quantity pcs"}]} | obsei-ai/sell-buy-intent-classifier-bert-mini | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"buy-intent",
"sell-intent",
"consumer-intent",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #buy-intent #sell-intent #consumer-intent #en #autotrain_compatible #endpoints_compatible #region-us
| Buy vs Sell Intent Classifier
=============================
Sample Intents for Testings
===========================
LABEL\_0 => "SELLING\_INTENT"
LABEL\_1 => "BUYING\_INTENT"
Buying Intents
--------------
* I am interested in this style of PGN-ES-D-6150 /Direct drive energy saving servo motor price and in ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #buy-intent #sell-intent #consumer-intent #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# mt5-base for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-base-3task-highlight-combined3')
```
## Citation 📜
```
@article{akyon2022questgen,
author =... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: Legendary Entertainment, 2016 y\u01... | obss/mt5-base-3task-highlight-combined3 | null | [
"transformers",
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"question-generation",
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"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-base
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train, TQuADv2-val, URL
Code: UR... | [
"# mt5-base for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-base \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train, TQuADv2-val, URL \... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt... |
text2text-generation | transformers |
# mt5-base for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-base-3task-highlight-tquad2')
```
## Citation 📜
```
@article{akyon2022questgen,
author = {A... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: Legendary Entertainment, 2016 y\u01... | obss/mt5-base-3task-highlight-tquad2 | null | [
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"mt5",
"text2text-generation",
"question-generation",
"answer-extraction",
"question-answering",
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"tr",
"dataset:tquad1",
"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"e... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-base
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train
Code: URL
Paper: URL
#... | [
"# mt5-base for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-base \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train \nCode: URL \nPap... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us... |
text2text-generation | transformers |
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-small-3task-both-tquad2', qg_format='both')
```
## Citation 📜
```
@article{akyon2022questgen,
... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "answer: film ve TV haklar\u0131n\u0131 context: Legend... | obss/mt5-small-3task-both-tquad2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"answer-extraction",
"question-answering",
"text-generation",
"tr",
"dataset:tquad1",
"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-small
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train
Code: URL
Paper: URL
#... | [
"# mt5-small for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-small \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train\nCode: URL \nPap... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt... |
text2text-generation | transformers |
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-small-3task-highlight-combined3')
```
## Citation 📜
```
@article{akyon2022questgen,
author... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: Legendary Entertainment, 2016 y\u01... | obss/mt5-small-3task-highlight-combined3 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"answer-extraction",
"question-answering",
"text-generation",
"tr",
"dataset:tquad1",
"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-small
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train, TQuADv2-val, URL
Code: ... | [
"# mt5-small for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-small \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train, TQuADv2-val, URL ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt... |
text2text-generation | transformers |
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-small-3task-highlight-tquad2')
```
## Citation 📜
```
@article{akyon2022questgen,
author = ... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: Legendary Entertainment, 2016 y\u01... | obss/mt5-small-3task-highlight-tquad2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"answer-extraction",
"question-answering",
"text-generation",
"tr",
"dataset:tquad1",
"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-small
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train
Code: URL
Paper: URL ... | [
"# mt5-small for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-small \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train \nCode: URL \nP... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt... |
text2text-generation | transformers |
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-small-3task-prepend-tquad2', qg_format='prepend')
```
## Citation 📜
```
@article{akyon2022ques... | {"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "answer: film ve TV haklar\u0131n\u0131 context: Legend... | obss/mt5-small-3task-prepend-tquad2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"answer-extraction",
"question-answering",
"text-generation",
"tr",
"dataset:tquad1",
"dataset:tquad2",
"dataset:xquad",
"arxiv:2111.06476",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-03-02T23:29:05+00:00 | [
"2111.06476"
] | [
"tr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
## Overview ️
Language model: mt5-small
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train
Code: URL
Paper: URL
... | [
"# mt5-small for Turkish Question Generation\nAutomated question generation and question answering using text-to-text transformers by OBSS AI.",
"## Overview ️\nLanguage model: mt5-small \nLanguage: Turkish \nDownstream-task: Extractive QA/QG, Answer Extraction \nTraining data: TQuADv2-train\nCode: URL \nPap... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt... |
text-generation | transformers |
# Joebot | {"tags": ["conversational"]} | odinmay/joebot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joebot | [
"# Joebot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Joebot"
] |
null | null | crasd | {} | oelkrise/ew | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| crasd | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | oemga38/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7475
* Matthews Correlation: 0.5570
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finance-sentiment-noisy-search
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["Finance-sentiment-analysis", "generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "widget": [{"text": "Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent on a currency-neutral basis", "example_title": "... | oferweintraub/bert-base-finance-sentiment-noisy-search | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"Finance-sentiment-analysis",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #Finance-sentiment-analysis #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-finance-sentiment-noisy-search
This model is a fine-tuned version of bert-base-uncased on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below:
1. First "bert-base-uncased" was fine-tuned on Kaggle's finance news sentiment analysis URL dataset ... | [
"# bert-base-finance-sentiment-noisy-search\n\nThis model is a fine-tuned version of bert-base-uncased on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below:\n\n1. First \"bert-base-uncased\" was fine-tuned on Kaggle's finance news sentiment analysis URL ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #Finance-sentiment-analysis #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-base-finance-sentiment-noisy-search\n\nThis model is a fine-tuned version of bert-base-uncase... |
text-generation | transformers |
# Michael DialoGPT model | {"tags": ["conversational"]} | ogpat123/DialoGPT-small-Michael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Michael DialoGPT model | [
"# Michael DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael DialoGPT model"
] |
text-generation | transformers |
# Chat bot based on Pulp fiction Character Jules
# Model trained on Pytorch framework uisng Pulp fiction dialogue script dataset from kaggle
| {"tags": ["conversational"]} | ogpat23/Jules-Chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Chat bot based on Pulp fiction Character Jules
# Model trained on Pytorch framework uisng Pulp fiction dialogue script dataset from kaggle
| [
"# Chat bot based on Pulp fiction Character Jules",
"# Model trained on Pytorch framework uisng Pulp fiction dialogue script dataset from kaggle"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Chat bot based on Pulp fiction Character Jules",
"# Model trained on Pytorch framework uisng Pulp fiction dialogue script dataset from kaggle"... |
question-answering | transformers |
# Turkish SQuAD Model : Question Answering
I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the "loodos/bert-base-turkish-uncased" model gave the best results for the Turkish language in classification in the "Auto-tagging of Short Conversational Sentences using Transfo... | {"language": "tr", "tags": ["question-answering", "loodos-bert-base", "TQuAD", "tr"], "datasets": ["TQuAD"], "model-index": [{"name": "loodos-bert-base-uncased-QA-fine-tuned", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "TQuAD", "type": "question-answering", "a... | oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #question-answering #loodos-bert-base #TQuAD #tr #dataset-TQuAD #model-index #endpoints_compatible #region-us
|
# Turkish SQuAD Model : Question Answering
I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the "loodos/bert-base-turkish-uncased" model gave the best results for the Turkish language in classification in the "Auto-tagging of Short Conversational Sentences using Transfo... | [
"# Turkish SQuAD Model : Question Answering\n\nI fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the \"loodos/bert-base-turkish-uncased\" model gave the best results for the Turkish language in classification in the \"Auto-tagging of Short Conversational Sentences usin... | [
"TAGS\n#transformers #pytorch #bert #question-answering #loodos-bert-base #TQuAD #tr #dataset-TQuAD #model-index #endpoints_compatible #region-us \n",
"# Turkish SQuAD Model : Question Answering\n\nI fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the \"loodos/bert-b... |
zero-shot-classification | transformers |
# Fb_improved_zeroshot
Zero-Shot Model designed to classify academic search logs in German and English. Developed by students at ETH Zürich.
This model was trained using the [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli/) checkpoint provided by Meta on Huggingface. It was then fine-tuned to suit ... | {"datasets": ["multi_nli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "natural language processing", "candidate_labels": "Location & Address, Employment, Organizational, Name, Service, Studies, Science", "hypothesis_template": "This is {}."}]} | oigele/Fb_improved_zeroshot | null | [
"transformers",
"pytorch",
"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"arxiv:1909.00161",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.00161"
] | [] | TAGS
#transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1909.00161 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Fb_improved_zeroshot
Zero-Shot Model designed to classify academic search logs in German and English. Developed by students at ETH Zürich.
This model was trained using the bart-large-mnli checkpoint provided by Meta on Huggingface. It was then fine-tuned to suit the needs of this project.
## NLI-based Zero-Shot T... | [
"# Fb_improved_zeroshot\n\nZero-Shot Model designed to classify academic search logs in German and English. Developed by students at ETH Zürich.\n\nThis model was trained using the bart-large-mnli checkpoint provided by Meta on Huggingface. It was then fine-tuned to suit the needs of this project.",
"## NLI-based... | [
"TAGS\n#transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1909.00161 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fb_improved_zeroshot\n\nZero-Shot Model designed to classify academic search logs in German and English. Developed by st... |
zero-shot-classification | transformers |
ETH Zeroshot
| {"datasets": ["multi_nli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "ETH", "candidate_labels": "Location & Address, Employment, Organizational, Name, Service, Studies, Science", "hypothesis_template": "This is {}."}]} | oigele/awesome_fb_model | null | [
"transformers",
"pytorch",
"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #autotrain_compatible #endpoints_compatible #region-us
|
ETH Zeroshot
| [] | [
"TAGS\n#transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | ETH Zeroshot tag: zero-shot-classification | {} | oigele/fb_zeroshot_new | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ETH Zeroshot tag: zero-shot-classification | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | # BERT MCN-Model using SMM4H 2017 (subtask 3) data
The model was trained using [clagator/biobert_v1.1_pubmed_nli_sts](https://huggingface.co/clagator/biobert_v1.1_pubmed_nli_sts) as a base and the smm4h dataset from 2017 from subtask 3.
## Dataset
See [here](https://github.com/olastor/medical-concept-normalization/... | {} | olastor/mcn-en-smm4h | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT MCN-Model using SMM4H 2017 (subtask 3) data
The model was trained using clagator/biobert_v1.1_pubmed_nli_sts as a base and the smm4h dataset from 2017 from subtask 3.
## Dataset
See here for the scripts and datasets.
Attribution
Sarker, Abeed (2018), “Data and systems for medication-related text classifica... | [
"# BERT MCN-Model using SMM4H 2017 (subtask 3) data\n\nThe model was trained using clagator/biobert_v1.1_pubmed_nli_sts as a base and the smm4h dataset from 2017 from subtask 3.",
"## Dataset\n\nSee here for the scripts and datasets.\n\nAttribution\n\nSarker, Abeed (2018), “Data and systems for medication-related... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT MCN-Model using SMM4H 2017 (subtask 3) data\n\nThe model was trained using clagator/biobert_v1.1_pubmed_nli_sts as a base and the smm4h dataset from 2017 from subtask 3.",
"## Datase... |
text-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. -->
# distilgpt2-finetuned-reddit-aita-text-gen
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt2-finetuned-reddit-aita-text-gen", "results": []}]} | oliverP/distilgpt2-finetuned-reddit-aita-text-gen | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-finetuned-reddit-aita-text-gen
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More... | [
"# distilgpt2-finetuned-reddit-aita-text-gen\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and e... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-finetuned-reddit-aita-text-gen\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achi... |
token-classification | transformers |
This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language.
This multilanguage model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl) provided by the [SEPP-NLG Shared Task](https://site... | {"language": ["en", "de", "fr", "it", "multilingual"], "license": "mit", "tags": ["punctuation prediction", "punctuation"], "datasets": "wmt/europarl", "metrics": ["f1"], "widget": [{"text": "Ho sentito che ti sei laureata il che mi fa molto piacere", "example_title": "Italian"}, {"text": "Tous les matins vers quatre h... | oliverguhr/fullstop-punctuation-multilang-large | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"xlm-roberta",
"token-classification",
"punctuation prediction",
"punctuation",
"en",
"de",
"fr",
"it",
"multilingual",
"dataset:wmt/europarl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"r... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"fr",
"it",
"multilingual"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #xlm-roberta #token-classification #punctuation prediction #punctuation #en #de #fr #it #multilingual #dataset-wmt/europarl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language.
This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. *Please note that this dataset consists of political speeches.... | [
"### Restore Punctuation\n\n\noutput\n\n\n\n> \n> My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?\n> \n> \n>",
"### Predict Labels\n\n\noutput\n\n\n\n> \n> [['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99... | [
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #xlm-roberta #token-classification #punctuation prediction #punctuation #en #de #fr #it #multilingual #dataset-wmt/europarl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Restore Punctuation\n\n\noutput\n\n\n\n> \n> My ... |
text-classification | transformers |
# German Sentiment Classification with Bert
This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model,
we provide a Python packa... | {"language": ["de"], "license": "mit", "tags": ["sentiment", "bert"], "metrics": ["f1"], "widget": [{"text": "Das ist gar nicht mal so schlecht"}]} | oliverguhr/german-sentiment-bert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| German Sentiment Classification with Bert
=========================================
This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage o... | [
"### Output class probabilities\n\n\nModel and Data\n--------------\n\n\nIf you are interested in code and data that was used to train this model please have a look at this repository and our paper. Here is a table of the F1 scores that this model achieves on different datasets. Since we trained this model with a n... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Output class probabilities\n\n\nModel and Data\n--------------\n\n\nIf you are interested in code and data that was used to train t... |
text-generation | transformers | #My Awesome model | {"tags": ["conversational"]} | omkar1309/RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #My Awesome model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# DialoGPT Jakeamal model | {"tags": ["conversational"]} | omnimokha/DialoGPT-medium-jakeamal | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Jakeamal model | [
"# DialoGPT Jakeamal model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Jakeamal model"
] |
text-generation | transformers |
# DialoGPT Jakeamal model | {"tags": ["conversational"]} | omnimokha/DialoGPT-small-jakeamal | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Jakeamal model | [
"# DialoGPT Jakeamal model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Jakeamal model"
] |
text-generation | transformers |
# DialoGPT Jakeamal model | {"tags": ["conversational"]} | omnimokha/jakebot2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Jakeamal model | [
"# DialoGPT Jakeamal model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Jakeamal model"
] |
text2text-generation | transformers | ## OPUS Tatoeba English-Yoruba
This model was obtained by running the script convert_marian_to_pytorch.py with the flag -m eng-yor. The original models were trained by Jörg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group.
---
- tags: translation... | {} | omoekan/opus-tatoeba-eng-yor | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| OPUS Tatoeba English-Yoruba
---------------------------
This model was obtained by running the script convert\_marian\_to\_pytorch.py with the flag -m eng-yor. The original models were trained by Jörg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP grou... | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805).
#### How to use
```python
from transformers import BertModel, BertTokenizerFast
alephbert_tokenizer = BertTokenizerFast.from_pretrained('on... | {"language": ["he"], "license": "apache-2.0", "tags": ["language model"], "datasets": ["oscar", "wikipedia", "twitter"]} | onlplab/alephbert-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"language model",
"he",
"dataset:oscar",
"dataset:wikipedia",
"dataset:twitter",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"he"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #language model #he #dataset-oscar #dataset-wikipedia #dataset-twitter #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture (Devlin et al. 2018).
#### How to use
## Training data
1. OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).
2. Hebrew dump of Wikipedia (650 MB text, 3 million sentences).
3. ... | [
"# AlephBERT",
"## Hebrew Language Model\n\nState-of-the-art language model for Hebrew.\nBased on Google's BERT architecture (Devlin et al. 2018).",
"#### How to use",
"## Training data\n1. OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).\n2. Hebrew dump of Wikipedia (650 MB text, 3 mill... | [
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"# AlephBERT",
"## Hebrew Language Model\n\nState-of-the-art language model ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | oo/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-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",
"#... | [
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"## Mode... |
text-generation | transformers |
# Elon Musk DialogGPT Model | {"tags": ["conversational"]} | oododo/DialoGPT-small-elon | null | [
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"gpt2",
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Elon Musk DialogGPT Model | [
"# Elon Musk DialogGPT Model"
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"# Elon Musk DialogGPT Model"
] |
zero-shot-image-classification | transformers | # Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision... | {"tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png", "candidate_labels": "playing music, playing sports", "example_title": "Cat & Dog"}]} | openai/clip-vit-base-patch16 | null | [
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"zero-shot-image-classification",
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"arxiv:1908.04913",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.00020",
"1908.04913"
] | [] | TAGS
#transformers #pytorch #jax #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #has_space #region-us
| # Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability o... | [
"# Model Card: CLIP\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details\nThe CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the... | [
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"# Model Card: CLIP\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details... |
zero-shot-image-classification | transformers |
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer visi... | {"tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png", "candidate_labels": "playing music, playing sports", "example_title": "Cat & Dog"}]} | openai/clip-vit-base-patch32 | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.00020",
"1908.04913"
] | [] | TAGS
#transformers #pytorch #tf #jax #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #has_space #region-us
|
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability... | [
"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details\n\nThe CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test... | [
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"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model D... |
zero-shot-image-classification | transformers |
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer visi... | {"tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png", "candidate_labels": "playing music, playing sports", "example_title": "Cat & Dog"}]} | openai/clip-vit-large-patch14 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2103.00020",
"1908.04913"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #has_space #region-us
|
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability... | [
"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details\n\nThe CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test... | [
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"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
... |
null | transformers |
# ImageGPT (large-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first relea... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"]} | openai/imagegpt-large | null | [
"transformers",
"pytorch",
"imagegpt",
"vision",
"dataset:imagenet-21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #imagegpt #vision #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us
|
# ImageGPT (large-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.
Disclaimer: The t... | [
"# ImageGPT (large-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.\n\nDisclaime... | [
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"# ImageGPT (large-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Gene... |
null | transformers |
# ImageGPT (medium-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first rele... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"]} | openai/imagegpt-medium | null | [
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"imagegpt",
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"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #imagegpt #vision #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# ImageGPT (medium-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.
Disclaimer: The ... | [
"# ImageGPT (medium-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.\n\nDisclaim... | [
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"# ImageGPT (medium-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in th... |
null | transformers |
# ImageGPT (small-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first relea... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"]} | openai/imagegpt-small | null | [
"transformers",
"pytorch",
"imagegpt",
"vision",
"dataset:imagenet-21k",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #imagegpt #vision #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# ImageGPT (small-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.
Disclaimer: The t... | [
"# ImageGPT (small-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post.\n\nDisclaime... | [
"TAGS\n#transformers #pytorch #imagegpt #vision #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# ImageGPT (small-sized model) \n\nImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the... |
null | transformers |
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation ... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing", "gan"]} | openclimatefix/dgmr | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #gan #license-mit #endpoints_compatible #region-us
|
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation ... | [
"# DGMR",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
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"## Training data\n\n[More information needed]",
"## Training procedure\n\n[... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #gan #license-mit #endpoints_compatible #region-us \n",
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"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]... |
token-classification | transformers |
## Extract names in any language.
| {"language": "multilingual", "license": "apache-2.0", "tags": ["Extract Names"]} | opensource/extract_names | null | [
"transformers",
"tf",
"xlm-roberta",
"token-classification",
"Extract Names",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #tf #xlm-roberta #token-classification #Extract Names #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Extract names in any language.
| [
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] | [
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"## Extract names in any language."
] |
null | null |
# MyModelName
Borges02
## Model description
You can generate new short stories from Jorge Luis Borges.
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## T... | {} | ordinarykids/borges02 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# MyModelName
Borges02
## Model description
You can generate new short stories from Jorge Luis Borges.
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you... | [
"# MyModelName\nBorges02",
"## Model description\n\nYou can generate new short stories from Jorge Luis Borges.",
"## Intended uses & limitations",
"#### How to use",
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"## Training data\n\nDescribe the data you use... | [
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"## Model description\n\nYou can generate new short stories from Jorge Luis Borges.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\n... |
text-classification | transformers | # **Descriptive Sentences Classifier**
Based on [AlephBERT](https://huggingface.co/onlplab/alephbert-base) model.
# **Metrics**
[accuracy](https://huggingface.co/metrics/accuracy): 0.813953488372093
</br>
[f1](https://huggingface.co/metrics/f1): 0.8181818181818182
## How to Use the model:
```python
from tra... | {"language": "he", "license": "afl-3.0", "tags": ["Text Classification"], "datasets": ["orisuchy/Descriptive_Sentences_He"], "metrics": ["accuracy", "f1"], "widget": [{"text": "\u05d4\u05d9\u05e2\u05e8 \u05d4\u05e9\u05d7\u05d5\u05e8 \u05d5\u05d4\u05d2\u05d3\u05d5\u05dc"}, {"text": "\u05d5\u05d0\u05d6 \u05d4\u05d5\u05d0... | orisuchy/Descriptive_Classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"he",
"dataset:orisuchy/Descriptive_Sentences_He",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #he #dataset-orisuchy/Descriptive_Sentences_He #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| # Descriptive Sentences Classifier
Based on AlephBERT model.
# Metrics
accuracy: 0.813953488372093
</br>
f1: 0.8181818181818182
## How to Use the model:
#### Or, if you want only the final class:
Created by Daniel Smotritsky & Ori Suchy
<br>
GitHub
<iframe src="URL style="border:none;height:1024px;wi... | [
"# Descriptive Sentences Classifier\r\n\r\nBased on AlephBERT model.",
"# Metrics\r\naccuracy: 0.813953488372093\r\n</br>\r\nf1: 0.8181818181818182",
"## How to Use the model:",
"#### Or, if you want only the final class:\r\n\r\nCreated by Daniel Smotritsky & Ori Suchy\r\n<br>\r\nGitHub\r\n<iframe src=\"URL s... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #he #dataset-orisuchy/Descriptive_Sentences_He #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Descriptive Sentences Classifier\r\n\r\nBased on AlephBERT model.",
"# Metrics\r\naccuracy: 0.8139534883720... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Systurnar Gu\u00f0r\u00fan og Monique \u00e1tu einar \u00e1 McDonalds og horf\u00f0u \u00e1 St\u00f6\u00f0 2, \u00fear glitti \u00ed Bruce Willis leika \u00... | orri/IceBERT-finetuned-ner | null | [
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"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0802
* Precision: 0.8940
* Recall: 0.8664
* F1: 0.8800
* Accuracy: 0.9854
Model description
-----------------
More ... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | orri/XLMR-ENIS-finetuned-ner | null | [
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"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Precision: 0.8714
* Recall: 0.8423
* F1: 0.8566
* Accuracy: 0.9827
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text2text-generation | transformers |
BART model fine-tuned to aggregate crowd-sourced transcriptions.
Repository: [GitHub](https://github.com/orzhan/bart-transcription-aggregation) | {"language": "ru"} | orzhan/bart-transcription-aggregation | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bart #text2text-generation #ru #autotrain_compatible #endpoints_compatible #region-us
|
BART model fine-tuned to aggregate crowd-sourced transcriptions.
Repository: GitHub | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Text simplification model for Russian. Fine-tuned ruGPT3-large
https://github.com/orzhan/rusimscore
---
language: ru
| {} | orzhan/rugpt3-simplify-large | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Text simplification model for Russian. Fine-tuned ruGPT3-large
URL
---
language: ru
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | T5-small model fine-tuned for extractive summarization on long documents.
Repository: [GitHub](https://github.com/orzhan/t5-long-extract) | {} | orzhan/t5-long-extract | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| T5-small model fine-tuned for extractive summarization on long documents.
Repository: GitHub | [] | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a t5-base model trained on the multi_news dataset for abstraction summarization | {} | osama7/t5-summarization-multinews | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a t5-base model trained on the multi_news dataset for abstraction summarization | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-to-image | generic |
# Image generation using pretrained BigGAN
## Warning: This only works for ImageNet inputs.
List of possible inputs: https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
GitHub repository: https://github.com/huggingface/pytorch-pretrained-BigGAN
| {"library_name": "generic", "tags": ["text-to-image"]} | osanseviero/BigGAN-deep-128 | null | [
"generic",
"pytorch",
"text-to-image",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #text-to-image #has_space #region-us
|
# Image generation using pretrained BigGAN
## Warning: This only works for ImageNet inputs.
List of possible inputs: URL
GitHub repository: URL
| [
"# Image generation using pretrained BigGAN\n\n ## Warning: This only works for ImageNet inputs.\n \n List of possible inputs: URL\n \n GitHub repository: URL"
] | [
"TAGS\n#generic #pytorch #text-to-image #has_space #region-us \n",
"# Image generation using pretrained BigGAN\n\n ## Warning: This only works for ImageNet inputs.\n \n List of possible inputs: URL\n \n GitHub repository: URL"
] |
audio-to-audio | null |
## Clone from Asteroid model `JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_... | {"license": "cc-by-sa-4.0", "tags": ["audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"], "library_tag": "generic"} | osanseviero/ConvTasNet_Libri1Mix_enhsingle_16k | null | [
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
|
## Clone from Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On Libri1Mix min test set :
License notice:
... | [
"## Clone from Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n... | [
"TAGS\n#pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n",
"## Clone from Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was tr... |
null | adapter-transformers |
# Adapter transformers | {"tags": ["adapter-transformers"]} | osanseviero/adapter-test | null | [
"adapter-transformers",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #region-us
|
# Adapter transformers | [
"# Adapter transformers"
] | [
"TAGS\n#adapter-transformers #region-us \n",
"# Adapter transformers"
] |
automatic-speech-recognition | superb |
# Fork of Wav2Vec2-Base-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16K... | {"language": "en", "license": "apache-2.0", "library_name": "superb", "tags": ["audio", "automatic-speech-recognition", "superb"], "datasets": ["librispeech_asr"], "benchmark": "superb", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exampl... | osanseviero/asr-with-transformers-wav2vec2 | null | [
"superb",
"pytorch",
"tf",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#superb #pytorch #tf #wav2vec2 #audio #automatic-speech-recognition #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #region-us
| Fork of Wav2Vec2-Base-960h
==========================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Moh... | [] | [
"TAGS\n#superb #pytorch #tf #wav2vec2 #audio #automatic-speech-recognition #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #region-us \n"
] |
audio-to-audio | generic |
# Audio to Audio repository template
This is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:
1. Specify the requirements by defining a `requirement... | {"library_name": "generic", "tags": ["audio-to-audio"]} | osanseviero/audio_test | null | [
"generic",
"audio-to-audio",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #audio-to-audio #region-us
|
# Audio to Audio repository template
This is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:
1. Specify the requirements by defining a 'URL' file.
... | [
"# Audio to Audio repository template\n\nThis is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:\n\n1. Specify the requirements by defining a 'URL... | [
"TAGS\n#generic #audio-to-audio #region-us \n",
"# Audio to Audio repository template\n\nThis is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:... |
question-answering | allennlp |
# TODO: Fill this model card
| {"tags": ["allennlp", "question-answering"]} | osanseviero/bidaf-model-2020.03.19 | null | [
"allennlp",
"question-answering",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#allennlp #question-answering #region-us
|
# TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#allennlp #question-answering #region-us \n",
"# TODO: Fill this model card"
] |
feature-extraction | sentence-transformers |
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: DistilBertModel
(1) Pooling mean
(2) Dense 768x512
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentenc... | {"tags": ["sentence-transformers", "feature-extraction"]} | osanseviero/clip-st | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us
|
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: DistilBertModel
(1) Pooling mean
(2) Dense 768x512
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have sentence-transformers installed:
Then you can use the ... | [
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: DistilBertModel\n\n(1) Pooling mean\n\n(2) Dense 768x512",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us \n",
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: DistilBertModel\n\n(1) Pooling mean\n\n(2) Dense 768x512",
"## Usage (Sente... |
null | null |
# Core NLP model for ar
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["ar"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_arabic | null | [
"corenlp",
"ar",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#corenlp #ar #license-gpl #region-us
|
# Core NLP model for ar
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for ar\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #ar #license-gpl #region-us \n",
"# Core NLP model for ar\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for ch
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["ch"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_chinese | null | [
"corenlp",
"ch",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ch"
] | TAGS
#corenlp #ch #license-gpl #region-us
|
# Core NLP model for ch
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for ch\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #ch #license-gpl #region-us \n",
"# Core NLP model for ch\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["en"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_english-default | null | [
"corenlp",
"en",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#corenlp #en #license-gpl #region-us
|
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #en #license-gpl #region-us \n",
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["en"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_english-extra | null | [
"corenlp",
"en",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#corenlp #en #license-gpl #region-us
|
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #en #license-gpl #region-us \n",
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["en"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_english-kbp | null | [
"corenlp",
"en",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#corenlp #en #license-gpl #region-us
|
# Core NLP model for en
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #en #license-gpl #region-us \n",
"# Core NLP model for en\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for fr
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["fr"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_french | null | [
"corenlp",
"fr",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#corenlp #fr #license-gpl #region-us
|
# Core NLP model for fr
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for fr\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #fr #license-gpl #region-us \n",
"# Core NLP model for fr\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for ge
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["ge"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_german | null | [
"corenlp",
"ge",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ge"
] | TAGS
#corenlp #ge #license-gpl #region-us
|
# Core NLP model for ge
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for ge\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #ge #license-gpl #region-us \n",
"# Core NLP model for ge\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
null | null |
# Core NLP model for sp
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | {"language": ["sp"], "license": "gpl", "tags": ["corenlp"], "library_tag": "corenlp"} | osanseviero/corenlp_spanish | null | [
"corenlp",
"sp",
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sp"
] | TAGS
#corenlp #sp #license-gpl #region-us
|
# Core NLP model for sp
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment,... | [
"# Core NLP model for sp\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | [
"TAGS\n#corenlp #sp #license-gpl #region-us \n",
"# Core NLP model for sp\n\nCoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, de... |
token-classification | spacy | ### Details: https://spacy.io/models/da#da_core_news_sm
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `da_core_news_sm` |
| **Version** | `3.4.0` |
| **spaCy** | `>=3.... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | osanseviero/da_core_news_sm | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (194 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
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"### Accuracy"
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"### Label Scheme\n\n\n\nView label scheme (194 ... |
text-to-image | generic |
## Fork of DALL·E mini - Generate images from text
For the original repo, head to https://huggingface.co/flax-community/dalle-mini | {"language": ["en"], "library_name": "generic", "pipeline_tag": "text-to-image"} | osanseviero/dalle-mini-fork | null | [
"generic",
"jax",
"bart",
"text-to-image",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#generic #jax #bart #text-to-image #en #has_space #region-us
|
## Fork of DALL·E mini - Generate images from text
For the original repo, head to URL | [
"## Fork of DALL·E mini - Generate images from text\n\nFor the original repo, head to URL"
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"## Fork of DALL·E mini - Generate images from text\n\nFor the original repo, head to URL"
] |
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... | osanseviero/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 | 2022-03-02T23:29:05+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: 0.2251
* Accuracy: 0.9225
* F1: 0.9227
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... |
token-classification | spacy | ### Details: https://spacy.io/models/el#el_core_news_sm
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_sm` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Defaul... | {"language": ["el"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | osanseviero/el_core_news_sm | null | [
"spacy",
"token-classification",
"el",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (396 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
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"### Label Scheme\n\n\n\nView label scheme (396 labels for 4 components... |
token-classification | spacy | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_ner_fashion
results:
- task:
name: NER
type: token-classification
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | osanseviero/en_ner_fashion | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #region-us
|
---
tags:
* spacy
* token-classification
language:
* en
model-index:
* name: en\_ner\_fashion
results:
+ task:
name: NER
type: token-classification
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F Score
type: f\_score
value: 0.0
---
... | [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
audio-classification | null |
Test with audio from same repo | {"tags": ["audio-classification"], "widget": [{"example_title": "English Sample", "src": "https://huggingface.co/osanseviero/example_audio/resolve/main/audio.wav"}]} | osanseviero/example_audio | null | [
"audio-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audio-classification #region-us
|
Test with audio from same repo | [] | [
"TAGS\n#audio-classification #region-us \n"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_ner_fashion` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **A... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | osanseviero/fashion_brands_patterns | null | [
"spacy",
"token-classification",
"en",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #has_space #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
image-classification | generic |
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_f... | {"library_name": "generic", "tags": ["image-classification"]} | osanseviero/fastai_cat_vs_dog | null | [
"generic",
"image-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #image-classification #has_space #region-us
|
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI Quick Start. Example training
## Training
The model was trained as follows
| [
"# Dog vs Cat Image Classification with FastAI CNN\n\nTraining is based in FastAI Quick Start. Example training",
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] |
image-classification | generic |
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_f... | {"library_name": "generic", "tags": ["image-classification"]} | osanseviero/fastai_cat_vs_dog_fork2 | null | [
"generic",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #image-classification #region-us
|
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI Quick Start. Example training
## Training
The model was trained as follows
| [
"# Dog vs Cat Image Classification with FastAI CNN\n\nTraining is based in FastAI Quick Start. Example training",
"## Training\n\nThe model was trained as follows"
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image-classification | generic |
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_f... | {"library_name": "generic", "tags": ["image-classification"]} | osanseviero/fastai_cat_vs_dog_fork_3 | null | [
"generic",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #image-classification #region-us
|
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI Quick Start. Example training
## Training
The model was trained as follows
| [
"# Dog vs Cat Image Classification with FastAI CNN\n\nTraining is based in FastAI Quick Start. Example training",
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translation | null |
# Fastspeech english model | {"license": "apache-2.0", "tags": ["translation"], "widget": [{"text": "I have a problem with my iphone that needs to be resolved asap!!"}, {"max_length": 1}]} | osanseviero/fastspeech | null | [
"translation",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#translation #license-apache-2.0 #region-us
|
# Fastspeech english model | [
"# Fastspeech english model"
] | [
"TAGS\n#translation #license-apache-2.0 #region-us \n",
"# Fastspeech english model"
] |
feature-extraction | fasttext |
# Fasttext example of feature extraction | {"library_name": "fasttext", "tags": ["feature-extraction"], "widget": [{"text": "apple", "example_title": "apple"}, {"text": "cat", "example_title": "cat"}, {"text": "sunny", "example_title": "sunny"}, {"text": "water", "example_title": "water"}]} | osanseviero/fasttext_embedding | null | [
"fasttext",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fasttext #feature-extraction #region-us
|
# Fasttext example of feature extraction | [
"# Fasttext example of feature extraction"
] | [
"TAGS\n#fasttext #feature-extraction #region-us \n",
"# Fasttext example of feature extraction"
] |
feature-extraction | generic |
# Pretrained FastText word vector for English
https://github.com/facebookresearch/fastText
Usage
```
import fasttext.util
ft = fasttext.load_model('cc.en.300.bin')
ft.get_word_vector('hello')
``` | {"library_name": "generic", "tags": ["feature-extraction"]} | osanseviero/fasttext_english | null | [
"generic",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #feature-extraction #region-us
|
# Pretrained FastText word vector for English
URL
Usage
| [
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] | [
"TAGS\n#generic #feature-extraction #region-us \n",
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] |
text-classification | fasttext |
# Fasttext nearest neighbors | {"library_name": "fasttext", "tags": ["text-classification"], "widget": [{"text": "apple", "example_title": "apple"}, {"text": "cat", "example_title": "cat"}, {"text": "sunny", "example_title": "sunny"}, {"text": "water", "example_title": "water"}]} | osanseviero/fasttext_nearest | null | [
"fasttext",
"text-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fasttext #text-classification #region-us
|
# Fasttext nearest neighbors | [
"# Fasttext nearest neighbors"
] | [
"TAGS\n#fasttext #text-classification #region-us \n",
"# Fasttext nearest neighbors"
] |
feature-extraction | generic |
# Pretrained FastText word vector for English
https://github.com/facebookresearch/fastText
Usage
```
import fasttext.util
ft = fasttext.load_model('cc.en.300.bin')
ft.get_word_vector('hello')
``` | {"library_name": "generic", "tags": ["feature-extraction"]} | osanseviero/fasttext_test | null | [
"generic",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #feature-extraction #region-us
|
# Pretrained FastText word vector for English
URL
Usage
| [
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] | [
"TAGS\n#generic #feature-extraction #region-us \n",
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] |
token-classification | flair | ## English NER in Flair (default model) | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]} | osanseviero/flair-ner-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us
| ## English NER in Flair (default model) | [
"## English NER in Flair (default model)"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us \n",
"## English NER in Flair (default model)"
] |
sentence-similarity | sentence-transformers |
## Testing Sentence Transformer | {"tags": ["sentence-transformers", "sentence-similarity"]} | osanseviero/full-sentence-distillroberta2 | null | [
"sentence-transformers",
"pytorch",
"jax",
"roberta",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #jax #roberta #sentence-similarity #endpoints_compatible #region-us
|
## Testing Sentence Transformer | [
"## Testing Sentence Transformer"
] | [
"TAGS\n#sentence-transformers #pytorch #jax #roberta #sentence-similarity #endpoints_compatible #region-us \n",
"## Testing Sentence Transformer"
] |
sentence-similarity | sentence-transformers |
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) instal... | {"license": ["cc-by-sa-4.0"], "tags": ["sentence-transformers", "causal-lm"], "pipeline_tag": "sentence-similarity"} | osanseviero/full-sentence-distillroberta3 | null | [
"sentence-transformers",
"pytorch",
"jax",
"roberta",
"causal-lm",
"sentence-similarity",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #jax #roberta #causal-lm #sentence-similarity #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have sentence-transformers installed:
Then you can use the model like this:
#... | [
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"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1) Pooling mean",
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"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1) Pooling me... |
image-classification | transformers |
# hot_dog_or_sandwich
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | osanseviero/hot_dog_or_sandwich | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# hot_dog_or_sandwich
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### hot dog
!hot dog
#### sandwich
!sandwich | [
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"## Example Images",
"#### hot dog\n\n!hot dog",
"#### sandwich\n\n!sandwich"
] | [
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null | superb |
# Test for superb using hubert downstream SD
## Usage
```python
import io
import soundfile as sf
from urllib.request import urlopen
from model import PreTrainedModel
model = PreTrainedModel()
url = "https://huggingface.co/datasets/lewtun/s3prl-sd-dummy/raw/main/audio.wav"
data, samplerate = sf.read(io.BytesIO(urlop... | {"library_name": "superb", "tags": ["superb", "speaker-diarization", "benchmark:superb"], "pipeline_tag": "speech-segmentation"} | osanseviero/hubert-sd | null | [
"superb",
"speaker-diarization",
"benchmark:superb",
"speech-segmentation",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #speaker-diarization #benchmark-superb #speech-segmentation #region-us
|
# Test for superb using hubert downstream SD
## Usage
| [
"# Test for superb using hubert downstream SD",
"## Usage"
] | [
"TAGS\n#superb #speaker-diarization #benchmark-superb #speech-segmentation #region-us \n",
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"## Usage"
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automatic-speech-recognition | superb |
# Test for superb using hubert downstream ASR and upstream hubert model from the HF Hub
This repo uses: https://huggingface.co/osanseviero/hubert_base | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition", "benchmark:superb"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | osanseviero/hubert_asr_using_hub | null | [
"superb",
"automatic-speech-recognition",
"benchmark:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #benchmark-superb #region-us
|
# Test for superb using hubert downstream ASR and upstream hubert model from the HF Hub
This repo uses: URL | [
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] | [
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"# Test for superb using hubert downstream ASR and upstream hubert model from the HF Hub\n\nThis repo uses: URL"
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null | null | # Base Hubert model (~95M params)
Source: https://github.com/pytorch/fairseq/tree/master/examples/hubert | {} | osanseviero/hubert_base | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Base Hubert model (~95M params)
Source: URL | [
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] | [
"TAGS\n#region-us \n",
"# Base Hubert model (~95M params)\n\nSource: URL"
] |
automatic-speech-recognition | superb |
# Test for superb using hubert downstream ASR | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition", "benchmark:superb"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | osanseviero/hubert_s3prl | null | [
"superb",
"automatic-speech-recognition",
"benchmark:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #benchmark-superb #region-us
|
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] |
automatic-speech-recognition | superb |
# Test for superb using hubert downstream ASR | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | osanseviero/hubert_s3prl_req | null | [
"superb",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #region-us
|
# Test for superb using hubert downstream ASR | [
"# Test for superb using hubert downstream ASR"
] | [
"TAGS\n#superb #automatic-speech-recognition #region-us \n",
"# Test for superb using hubert downstream ASR"
] |
image-classification | transformers |
# hugging-geese
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | osanseviero/hugging-geese | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# hugging-geese
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dog
!dog
#### duck
!duck
#### goose
!goose
#### pigeon
!pigeon
#### swan
!swan | [
"# hugging-geese\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dog\n\n!dog",
"#### duck\n\n!duck",
"#### goose\n\n!goose",
"#### pigeon\n\n!pigeon"... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# hugging-geese\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
sentence-similarity | sentence-transformers |
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) instal... | {"license": ["cc-by-sa-4.0"], "tags": ["sentence-transformers", "causal-lm"], "pipeline_tag": "sentence-similarity"} | osanseviero/just-a-test | null | [
"sentence-transformers",
"pytorch",
"jax",
"roberta",
"causal-lm",
"sentence-similarity",
"doi:10.57967/hf/0820",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #jax #roberta #causal-lm #sentence-similarity #doi-10.57967/hf/0820 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have sentence-transformers installed:
Then you can use the model like this:
#... | [
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1) Pooling mean",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use th... | [
"TAGS\n#sentence-transformers #pytorch #jax #roberta #causal-lm #sentence-similarity #doi-10.57967/hf/0820 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1)... |
image-classification | keras | Simple MNIST convnet based on the [official Keras documentation](https://keras.io/examples/vision/mnist_convnet/) | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification", "keras"]} | osanseviero/keras-conv-mnist | null | [
"keras",
"tf",
"image-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tf #image-classification #license-apache-2.0 #region-us
| Simple MNIST convnet based on the official Keras documentation | [] | [
"TAGS\n#keras #tf #image-classification #license-apache-2.0 #region-us \n"
] |
image-classification | keras |
Keras Dog vs Cat based on the [official Keras documentation](https://keras.io/examples/vision/image_classification_from_scratch/) | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification", "keras"]} | osanseviero/keras-dog-or-cat | null | [
"keras",
"tf",
"image-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tf #image-classification #license-apache-2.0 #region-us
|
Keras Dog vs Cat based on the official Keras documentation | [] | [
"TAGS\n#keras #tf #image-classification #license-apache-2.0 #region-us \n"
] |
image-classification | transformers |
# Llamastics
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Examp... | {"tags": ["image-classification", "pytorch", "huggingpics", "llama-leaderboard"], "metrics": ["accuracy"]} | osanseviero/llama-alpaca-guanaco-vicuna | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"doi:10.57967/hf/0035",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #doi-10.57967/hf/0035 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Llamastics
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### alpaca
!alpaca
#### guanaco
!guanaco
#### llama
!llama
#### vicuna
!vicuna | [
"# Llamastics\n\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna\n\n!vicuna"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #llama-leaderboard #doi-10.57967/hf/0035 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Llamastics\n\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by run... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-xlm-r-multilingual-v1
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.
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | osanseviero/my-new-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us
|
# sentence-transformers/paraphrase-xlm-r-multilingual-v1
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... | [
"# sentence-transformers/paraphrase-xlm-r-multilingual-v1\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 hav... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us \n",
"# sentence-transformers/paraphrase-xlm-r-multilingual-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional... |
feature-extraction | sentence-transformers |
# Name of Model
<!--- Describe your model here -->
## Model Description
The model consists of the following layers:
(0) Base Transformer Type: RobertaModel
(1) mean Pooling
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/s... | {"tags": ["sentence-transformers", "feature-extraction"]} | osanseviero/my_new_model | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
|
# Name of Model
## Model Description
The model consists of the following layers:
(0) Base Transformer Type: RobertaModel
(1) mean Pooling
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have sentence-transformers installed:
Then you can use the model like this:
## Usag... | [
"# Name of Model",
"## Model Description\nThe model consists of the following layers:\n\n(0) Base Transformer Type: RobertaModel\n\n(1) mean Pooling",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use the model li... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# Name of Model",
"## Model Description\nThe model consists of the following layers:\n\n(0) Base Transformer Type: RobertaModel\n\n(1) mean Pooling",
"## Usage (Sentence-Transformers)\n\nUsing this model... |
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... | osanseviero/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | generic |
# pyctcdecode + Hugging Face model
Inspired on https://github.com/kensho-technologies/pyctcdecode/blob/main/tutorials/02_pipeline_huggingface.ipynb | {"library_name": "generic", "tags": ["automatic-speech-recognition"]} | osanseviero/pyctcdecode_asr | null | [
"generic",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #tf #wav2vec2 #automatic-speech-recognition #region-us
|
# pyctcdecode + Hugging Face model
Inspired on URL | [
"# pyctcdecode + Hugging Face model\n\nInspired on URL"
] | [
"TAGS\n#generic #pytorch #tf #wav2vec2 #automatic-speech-recognition #region-us \n",
"# pyctcdecode + Hugging Face model\n\nInspired on URL"
] |
summarization | transformers |
# Model name
Wikihow T5-small
## Model description
This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.... | {"language": "eng", "tags": ["wikihow", "t5-small", "pytorch", "lm-head", "seq2seq", "t5", "pipeline:summarization", "summarization"], "datasets": ["Wikihow"], "metrics": [{"Rouge1": 31.2}, {"RougeL": 24.5}], "widget": [{"max_length": 1}, {"text": "Lack of fluids can lead to dry mouth, which is a leading cause of bad b... | osanseviero/t5-finetuned-test | null | [
"transformers",
"pytorch",
"jax",
"coreml",
"t5",
"text2text-generation",
"wikihow",
"t5-small",
"lm-head",
"seq2seq",
"pipeline:summarization",
"summarization",
"eng",
"dataset:Wikihow",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"r... | null | 2022-03-02T23:29:05+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #jax #coreml #t5 #text2text-generation #wikihow #t5-small #lm-head #seq2seq #pipeline-summarization #summarization #eng #dataset-Wikihow #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model name
Wikihow T5-small
## Model description
This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.... | [
"# Model name\nWikihow T5-small",
"## Model description\n\nThis is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL s... | [
"TAGS\n#transformers #pytorch #jax #coreml #t5 #text2text-generation #wikihow #t5-small #lm-head #seq2seq #pipeline-summarization #summarization #eng #dataset-Wikihow #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model name\nWikihow T5-small",
"## Model des... |
image-classification | transformers |
# taco_or_what
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | osanseviero/taco_or_what | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# taco_or_what
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### burrito
!burrito
#### fajitas
!fajitas
#### kebab
!kebab
#### quesadilla
!quesadilla
#### taco
!tac... | [
"# taco_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### burrito\n\n!burrito",
"#### fajitas\n\n!fajitas",
"#### kebab\n\n!kebab",
"#### quesa... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# taco_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras"} | osanseviero/test | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
null | null | t | {} | osanseviero/test4 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| t | [] | [
"TAGS\n#region-us \n"
] |
null | allennlp |
# TODO: Fill this model card
| {"tags": ["allennlp"]} | osanseviero/test_allennlp | null | [
"allennlp",
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#allennlp #tensorboard #region-us
|
# TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#allennlp #tensorboard #region-us \n",
"# TODO: Fill this model card"
] |
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