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text2text-generation | transformers | # IT5 Cased Small Efficient EL32 for Question Generation 💭 🇮🇹
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model ... | {"language": ["it"], "license": "apache-2.0", "tags": ["Italian", "efficient", "sequence-to-sequence", "question-generation", "squad_it", "text2text-generation"], "datasets": ["squad_it"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto pi\... | it5/it5-efficient-small-el32-question-generation | null | [
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*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on question generation on the SQuAD-IT corpus as part of the experiments of th... | [
"# IT5 Cased Small Efficient EL32 for Question Generation 🇮🇹\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on question generation on the SQuAD-IT corpus as part of the experime... | [
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text2text-generation | transformers |
# IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Il Giornale to Repubblica) 🗞️➡️🗞️ 🇮🇹
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://hugging... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "newspaper", "efficient", "ilgiornale", "repubblica", "style-transfer"], "datasets": ["gsarti/change_it"], "metrics": ["rouge", "bertscore", "headline-headline-consistency-classifier", "headline-article-consistency-classifier"], "... | it5/it5-efficient-small-el32-ilgiornale-to-repubblica | null | [
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# IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Il Giornale to Repubblica) ️️️ 🇮🇹
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline style transfer in th... | [
"# IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Il Giornale to Repubblica) ️️️ 🇮🇹\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline style transf... | [
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summarization | transformers | # IT5 Cased Small Efficient EL32 for Wikipedia Summarization 📑 🇮🇹
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) mo... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "wikipedia", "summarization", "efficient", "wits"], "datasets": ["wits"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "La 5\u00aa Commissione ha competenza per i disegni di legge riguardanti le specifiche materie del bi... | it5/it5-efficient-small-el32-wiki-summarization | null | [
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| # IT5 Cased Small Efficient EL32 for Wikipedia Summarization 🇮🇹
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on Wikipedia summarization on the WITS dataset as part of the experiments ... | [
"# IT5 Cased Small Efficient EL32 for Wikipedia Summarization 🇮🇹\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on Wikipedia summarization on the WITS dataset as part of the exp... | [
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text2text-generation | transformers | # IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingf... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "efficient", "newspaper", "ilgiornale", "repubblica", "style-transfer"], "datasets": ["gsarti/change_it"], "metrics": ["rouge", "bertscore", "headline-headline-consistency-classifier", "headline-article-consistency-classifier"], "... | it5/it5-efficient-small-el32-repubblica-to-ilgiornale | null | [
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*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline style transfer in the... | [
"# IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Repubblica to Il Giornale) ️️️ 🇮🇹\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline style transf... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ShreeGanesh
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-ShreeGanesh", "results": []}]} | stevems1/bert-base-uncased-ShreeGanesh | null | [
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"region:us"
] | null | 2022-04-28T13:19:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-ShreeGanesh
This model is a fine-tuned version of [](URL on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# bert-base-uncased-ShreeGanesh\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
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"## Model description\n\nMore information needed",
"## Inten... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 797524592
- CO2 Emissions (in grams): 27.564419884224776
## Validation Metrics
- Loss: 2.2697999477386475
- SacreBLEU: 14.9797
- Gen len: 13.7071 | {"language": ["en", "hi"], "tags": ["autotrain", "translation"], "datasets": ["aakarshan/autotrain-data-Question-translation"], "co2_eq_emissions": 27.564419884224776} | aakarshan/autotrain-Question-translation-797524592 | null | [
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|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 797524592
- CO2 Emissions (in grams): 27.564419884224776
## Validation Metrics
- Loss: 2.2697999477386475
- SacreBLEU: 14.9797
- Gen len: 13.7071 | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n... |
text-generation | transformers | # Echidona DialoGPT-Medium Model | {"tags": ["conversational"]} | Azuris/DialoGPT-medium-ekidona | null | [
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"text-generation",
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"endpoints_compatible",
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Echidona DialoGPT-Medium Model | [
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fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.20 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:27:59+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
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fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.30 | null | [
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"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:28:08+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
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fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.50 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:28:20+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.60 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:28:27+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.70 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:28:36+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.80 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-28T14:28:43+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the s... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-finetuned-squad2", "results": []}]} | 123tarunanand/roberta-base-finetuned | null | [
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"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T14:29:48+00:00 | [] | [] | TAGS
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| roberta-base-finetuned-squad2
=============================
This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9325
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16... |
question-answering | transformers | ### Model
**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters
Trained on 4 NVIDI... | {} | 123tarunanand/albert-xlarge-finetuned | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T14:30:55+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
| ### Model
'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\_squad.py'
### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
### Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
### Usage
See huggingface documentation. Training on 'SQuAD V2' allows the model t... | [
"### Model\n\n\n'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the dev set. I did not sweep for best threshold.",
"### Usage\n\n\nSee huggingface documentation. Training on 'SQ... | [
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n",
"### Model\n\n\n'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the dev set. I di... |
null | null |
# HowTo QA with distilGPT2
DistilGPT2 English language model fine-tuned with ±20.000 entries from WikiHow.
Input prompt should follow the following format:
`\n<|startoftext|>[WP] How to {text} \n[RESPONSE]`
Example:
`\n<|startoftext|>[WP] How to create a universe \n[RESPONSE]`
| {"language": "en", "license": "mit"} | soyasis/distilgpt2-finetuned-how-to-qa | null | [
"en",
"license:mit",
"region:us"
] | null | 2022-04-28T15:13:24+00:00 | [] | [
"en"
] | TAGS
#en #license-mit #region-us
|
# HowTo QA with distilGPT2
DistilGPT2 English language model fine-tuned with ±20.000 entries from WikiHow.
Input prompt should follow the following format:
'\n<|startoftext|>[WP] How to {text} \n[RESPONSE]'
Example:
'\n<|startoftext|>[WP] How to create a universe \n[RESPONSE]'
| [
"# HowTo QA with distilGPT2\n\nDistilGPT2 English language model fine-tuned with ±20.000 entries from WikiHow.\n\nInput prompt should follow the following format:\n'\\n<|startoftext|>[WP] How to {text} \\n[RESPONSE]'\n\nExample:\n'\\n<|startoftext|>[WP] How to create a universe \\n[RESPONSE]'"
] | [
"TAGS\n#en #license-mit #region-us \n",
"# HowTo QA with distilGPT2\n\nDistilGPT2 English language model fine-tuned with ±20.000 entries from WikiHow.\n\nInput prompt should follow the following format:\n'\\n<|startoftext|>[WP] How to {text} \\n[RESPONSE]'\n\nExample:\n'\\n<|startoftext|>[WP] How to create a univ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-culinary-finetuned
This model was trained from scratch on the None dataset.
It achieves the following results on th... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-culinary-finetuned", "results": []}]} | juancavallotti/roberta-base-culinary-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T16:06:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-culinary-finetuned
===============================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0657
* F1: 0.9929
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-qmsum-meeting-summarization
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/all... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "longformer-qmsum-meeting-summarization", "results": []}]} | mikeadimech/longformer-qmsum-meeting-summarization | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T16:15:54+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| longformer-qmsum-meeting-summarization
======================================
This model is a fine-tuned version of allenai/led-base-16384 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2055
* Rouge1: 20.5333
* Rouge2: 7.6756
* Rougel: 16.2531
* Rougelsum: 19.0336
* Gen Len:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-07\n* train\\_batch\\_size: 2\n* e... |
text-classification | transformers | # Cross-Encoder
The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.
<p align="center">
<img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br>
Bridget Riley... | {"language": ["it"], "license": "apache-2.0", "tags": ["cross-encoder", "sentence-similarity", "transformers"], "pipeline_tag": "text-classification"} | efederici/cross-encoder-distilbert-it | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T17:05:39+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #cross-encoder #sentence-similarity #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder
The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.
<p align="center">
<img src="URL width="400"> </br>
Bridget Riley, COOL EDGE
</p>
## Training Data
This model was trained on a custom bio... | [
"# Cross-Encoder\n\nThe model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.\n\n<p align=\"center\">\n <img src=\"URL width=\"400\"> </br>\n Bridget Riley, COOL EDGE\n</p>",
"## Training Data\n\nThis model was ... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #cross-encoder #sentence-similarity #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder\n\nThe model can be used for Information Retrieval: given a query, encode the query will all possible... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-shuffled_take3-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-shuffled_take3-small", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args":... | chv5/t5-small-shuffled_take3-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T17:06:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-shuffled\_take3-small
==============================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4505
* Rouge1: 11.883
* Rouge2: 9.4784
* Rougel: 10.9978
* Rougelsum: 11.5961
* Gen Len: 18.9834
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-log-parser-winlogbeat
This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https:... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-base-cased-finetuned-log-parser-winlogbeat", "results": []}]} | Slavka/bert-base-cased-finetuned-log-parser-winlogbeat | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T17:08:22+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-cased-finetuned-log-parser-winlogbeat
This model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
##... | [
"# bert-base-cased-finetuned-log-parser-winlogbeat\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-cased-finetuned-log-parser-winlogbeat\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.\nIt achieves ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | davidenam/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T17:08:50+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1391
* F1: 0.8626
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | transformers |
# Model trained on sonobois convos | {"tags": ["conversational"]} | aditeyabaral/sonobois | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T17:29:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model trained on sonobois convos | [
"# Model trained on sonobois convos"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model trained on sonobois convos"
] |
text2text-generation | transformers | # ebanko-base
Model was finetuned by [black_samorez](https://github.com/BlackSamorez).
Based off [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base).
Finetuned on [
russe_detox_2022](https://github.com/skoltech-nlp/russe_detox_2022) train to toxify text.
I recommend using it with **temperature = 1.... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"]} | BlackSamorez/ebanko-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T17:43:43+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ebanko-base
Model was finetuned by black_samorez.
Based off sberbank-ai/ruT5-base.
Finetuned on
russe_detox_2022 train to toxify text.
I recommend using it with temperature = 1.5
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'
---
lice... | [
"# ebanko-base\nModel was finetuned by black_samorez.\n\nBased off sberbank-ai/ruT5-base.\n\nFinetuned on \nrusse_detox_2022 train to toxify text.\n\nI recommend using it with temperature = 1.5\n\n* Task: 'text2text generation'\n* Type: 'encoder-decoder'\n* Tokenizer: 'bpe'\n* Dict size: '32 101'\n* Num Parameters:... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ebanko-base\nModel was finetuned by black_samorez.\n\nBased off sberbank-ai/ruT5-base.\n\nFinetuned on \nrusse_detox_2022 train to toxify tex... |
text-generation | transformers | # DialoGPT-medium Model of Simpsons Episode s1e10 "Homer's Night Out"
| {"tags": ["conversational"]} | Jonesy/HomersNightOut | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T17:44:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT-medium Model of Simpsons Episode s1e10 "Homer's Night Out"
| [
"# DialoGPT-medium Model of Simpsons Episode s1e10 \"Homer's Night Out\""
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-medium Model of Simpsons Episode s1e10 \"Homer's Night Out\""
] |
text2text-generation | transformers | # ebanko-base
Model was finetuned by [black_samorez](https://github.com/BlackSamorez).
Based off [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base).
Finetuned on [Russian Language Toxic Comments](https://www.kaggle.com/datasets/blackmoon/russian-language-toxic-comments) and [
russe_detox_2022](http... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/model-zoo"} | BlackSamorez/ebanko-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T18:11:32+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ebanko-base
Model was finetuned by black_samorez.
Based off sberbank-ai/ruT5-base.
Finetuned on Russian Language Toxic Comments and
russe_detox_2022 train to toxify text.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Parameters: '737 M'
---
license: apach... | [
"# ebanko-base\nModel was finetuned by black_samorez.\n\nBased off sberbank-ai/ruT5-base.\n\nFinetuned on Russian Language Toxic Comments and \nrusse_detox_2022 train to toxify text.\n* Task: 'text2text generation'\n* Type: 'encoder-decoder'\n* Tokenizer: 'bpe'\n* Dict size: '32 101 '\n* Num Parameters: '737 M'\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ebanko-base\nModel was finetuned by black_samorez.\n\nBased off sberbank-ai/ruT5-base.\n\nFinetuned on Russian Language Toxic Comments and \n... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Andrei0086/Chat-small-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T18:49:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
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"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547362404061052928/WWnV... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/inversebrah/1664000969650/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/inversebrah | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T19:05:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
smolting (wassie, verse)
@inversebrah
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibert_scivocab_uncased-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingf... | {"tags": ["generated_from_trainer"], "datasets": ["plo_dunfiltered_config"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "scibert_scivocab_uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "plo_dunfiltere... | dipteshkanojia/scibert_scivocab_uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:plo_dunfiltered_config",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T19:21:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-plo_dunfiltered_config #model-index #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_uncased-finetuned-ner
========================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the plo\_dunfiltered\_config dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1390
* Precision: 0.9649
* Recall: 0.9612
* F1: 0.963... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 11",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-plo_dunfiltered_config #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: 1e-05\n* tr... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 798824628
- CO2 Emissions (in grams): 121.67185089502216
## Validation Metrics
- Loss: 0.5046824812889099
- Accuracy: 0.8472124039775673
- Macro F1: 0.7812978033330673
- Micro F1: 0.8472124039775673
- Weighted F1: 0.8464983956259... | {"language": "en", "tags": "autotrain", "datasets": ["Sathira/autotrain-data-mbtiNlp"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 121.67185089502216} | Sathira/autotrain-mbtiNlp-798824628 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:Sathira/autotrain-data-mbtiNlp",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T20:01:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Sathira/autotrain-data-mbtiNlp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 798824628
- CO2 Emissions (in grams): 121.67185089502216
## Validation Metrics
- Loss: 0.5046824812889099
- Accuracy: 0.8472124039775673
- Macro F1: 0.7812978033330673
- Micro F1: 0.8472124039775673
- Weighted F1: 0.8464983956259... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 798824628\n- CO2 Emissions (in grams): 121.67185089502216",
"## Validation Metrics\n\n- Loss: 0.5046824812889099\n- Accuracy: 0.8472124039775673\n- Macro F1: 0.7812978033330673\n- Micro F1: 0.8472124039775673\n- Weighted F... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Sathira/autotrain-data-mbtiNlp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 798824628\n- CO2 Emissions... |
text-classification | transformers | This model helps to identify the equivalent of two sentences.
==>python 3.8 working in transformers installation
-->pip install git+https://github.com/huggingface/transformers
-->python -m pip install jupyter
-->pip install torch==1.5.0 -f https://download.pytorch.org/whl/torch_stable.html
-->pip install tensorflow-gp... | {} | shahidul034/sentence_equivalent_check | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T20:13:19+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model helps to identify the equivalent of two sentences.
==>python 3.8 working in transformers installation
-->pip install git+URL
-->python -m pip install jupyter
-->pip install torch==1.5.0 -f URL
-->pip install tensorflow-gpu
How to create virtual environment:
Main tutorial: URL
URL
# Creating a new Virtual E... | [
"# Creating a new Virtual Environment.\nThe following command takes '-n' as a flag, which is for creating a new environment with its name as 'env' and the specific Python version of '3.7'. \n-->conda create -n env python=3.6\n\nActivating the Virtual Environment.\nThe command below activates the Virtual Environment... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Creating a new Virtual Environment.\nThe following command takes '-n' as a flag, which is for creating a new environment with its name as 'env' and the specific Python version of '3.7'. \n-->con... |
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-stsb
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": ["spearmanr"], "model-index": [{"name": "distilbert-base-uncased-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "stsb"... | lilykaw/distilbert-base-uncased-finetuned-stsb | 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 | 2022-04-28T20:14:27+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-stsb
======================================
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: 0.5634
* Pearson: 0.8680
* Spearmanr: 0.8652
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: 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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace
This model is a fine-tuned version of [bert-base-cased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace", "results": []}]} | Slavka/bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T20:46:10+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace
This model is a fine-tuned version of bert-base-cased 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 a... | [
"# bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace\n\nThis model is a fine-tuned version of bert-base-cased 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 neede... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following res... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-vorarlbergerisch
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | bkh6722/wav2vec2-vorarlbergerisch | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T21:14:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-vorarlbergerisch
=========================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9241
* Wer: 0.4358
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | AbhiNaiky/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T21:16:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3170
- Accuracy: 0.8733
- F1: 0.875
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3170\n- Accuracy: 0.8733\n- F1: 0.875",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text-generation | transformers |
# mGPT: fine-tune on message data MWE
This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. Trained for one epoch, will be updated in a (separate) model repo later.
## Model description
- testing if fine-tuned personality data bleeds over to other langua... | {"license": "apache-2.0", "tags": ["multilingual", "PyTorch", "Transformers", "gpt3", "gpt2", "Deepspeed", "Megatron"], "datasets": ["mc4", "Wikipedia"], "pipeline_tag": "text-generation", "widget": [{"text": "I know you're tired, but can we go for another walk this evening?\npeter szemraj:\n\n", "example_title": "walk... | pszemraj/mGPT-Peter-mwe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"multilingual",
"PyTorch",
"Transformers",
"gpt3",
"Deepspeed",
"Megatron",
"dataset:mc4",
"dataset:Wikipedia",
"base_model:sberbank-ai/mGPT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-... | null | 2022-04-28T21:54:47+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #multilingual #PyTorch #Transformers #gpt3 #Deepspeed #Megatron #dataset-mc4 #dataset-Wikipedia #base_model-sberbank-ai/mGPT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mGPT: fine-tune on message data MWE
This model is a fine-tuned version of sberbank-ai/mGPT on 80k messages. Trained for one epoch, will be updated in a (separate) model repo later.
## Model description
- testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitl... | [
"# mGPT: fine-tune on message data MWE\n\nThis model is a fine-tuned version of sberbank-ai/mGPT on 80k messages. Trained for one epoch, will be updated in a (separate) model repo later.",
"## Model description\n\n- testing if fine-tuned personality data bleeds over to other languages without being trained in the... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #multilingual #PyTorch #Transformers #gpt3 #Deepspeed #Megatron #dataset-mc4 #dataset-Wikipedia #base_model-sberbank-ai/mGPT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mGPT: fine-tune on message... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410587808666955776/mWkK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/usmnt/1651680543545/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/usmnt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T22:16:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
USMNT
@usmnt
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-rater", "results": []}]} | megrisdal/distilbert-rater | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T23:15:48+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-rater
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore informa... |
text-generation | transformers | # My Awesome Model | {"tags": ["conversational"]} | awvik360/UncleRuckus | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T23:31:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kimhieu/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "kimhieu/distilbert-base-uncased-finetuned-cola", "results": []}]} | kimhieu/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T01:39:26+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| kimhieu/distilbert-base-uncased-finetuned-cola
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1828
* Validation Loss: 0.5520
* Train Matthews Correlation: 0.5... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-vorarlbergerisch
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | bkh6722/xlsr-vorarlbergerisch | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T01:50:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-vorarlbergerisch
==============================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-german on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3193
* Wer: 0.3235
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
null | null | #Introduction
See <https://github.com/k2-fsa/icefall/pull/312>
Models in the folder [exp][exp] are generated using the following command:
```bash
epoch=27
avg=10
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch $epoch... | {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29 | null | [
"tensorboard",
"region:us"
] | null | 2022-04-29T02:01:55+00:00 | [] | [] | TAGS
#tensorboard #region-us
| #Introduction
See <URL
Models in the folder [exp][exp] are generated using the following command:
[exp]: URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ko-en-finetuned-ko-to-en3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ko-en-finetuned-ko-to-en3", "results": []}]} | norefly/opus-mt-ko-en-finetuned-ko-to-en3 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T03:28:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ko-en-finetuned-ko-to-en3
=================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ko-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1864
* Bleu: 0.7037
* Gen Len: 11.0
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 256\n* total\\_train\\_batch\\_size: 2048\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batc... |
automatic-speech-recognition | espnet |
## ESPnet2 model
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Apr 26 15:33:18 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr", "librispeech 960h"]} | chaitu619/chai_librispeech_asr_train_transducer_v2_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-29T03:32:10+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 model
-------------
This model was trained by Chaitanya Narisetty using recipe in espnet.
RESULTS
=======
Environments
------------
* date: 'Tue Apr 26 15:33:18 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'
* espnet version: 'espnet 202204'
* pytorch version: 'pytorch 1... | [
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
fill-mask | transformers |
# Pile of Law BERT large model (uncased)
Pretrained model on English language legal and administrative text using the [RoBERTa](https://arxiv.org/abs/1907.11692) pretraining objective.
## Model description
Pile of Law BERT large is a transformers model with the [BERT large model (uncased)](https://huggingface.co/bert... | {"language": ["en"], "datasets": ["pile-of-law/pile-of-law"], "pipeline_tag": "fill-mask"} | pile-of-law/legalbert-large-1.7M-1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:pile-of-law/pile-of-law",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:2110.00976",
"arxiv:2207.00220",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-29T05:01:04+00:00 | [
"1907.11692",
"1810.04805",
"2110.00976",
"2207.00220"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-pile-of-law/pile-of-law #arxiv-1907.11692 #arxiv-1810.04805 #arxiv-2110.00976 #arxiv-2207.00220 #endpoints_compatible #has_space #region-us
| Pile of Law BERT large model (uncased)
======================================
Pretrained model on English language legal and administrative text using the RoBERTa pretraining objective.
Model description
-----------------
Pile of Law BERT large is a transformers model with the BERT large model (uncased) architect... | [
"### Preprocessing\n\n\nThe model vocabulary consists of 29,000 tokens from a custom word-piece vocabulary fit to Pile of Law using the HuggingFace WordPiece tokenizer and 3,000 randomly sampled legal terms from Black's Law Dictionary, for a vocabulary size of 32,000 tokens. The 80-10-10 masking, corruption, leave ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-pile-of-law/pile-of-law #arxiv-1907.11692 #arxiv-1810.04805 #arxiv-2110.00976 #arxiv-2207.00220 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe model vocabulary consists of 29,000 tokens from a custom word-piece vocabulary fi... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Das282000Prit/fyp-finetuned-brown
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Das282000Prit/fyp-finetuned-brown", "results": []}]} | Das282000Prit/fyp-finetuned-brown | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T05:15:15+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Das282000Prit/fyp-finetuned-brown
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.5777
* Validation Loss: 3.0737
* Epoch: 0
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
null | null | #Introduction
See <https://github.com/k2-fsa/icefall/pull/288>
Models in the folder [exp][exp] are generated using the following command:
```bash
epoch=38
avg=10
./pruned_transducer_stateless2/export.py \
--exp-dir ./pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch $epoch... | {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29 | null | [
"tensorboard",
"region:us"
] | null | 2022-04-29T05:32:42+00:00 | [] | [] | TAGS
#tensorboard #region-us
| #Introduction
See <URL
Models in the folder [exp][exp] are generated using the following command:
[exp]: URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base_2
This model is a fine-tuned version of [obokkkk/mt5-base](https://huggingface.co/obokkkk/mt5-base) on the None dataset... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base_2", "results": []}]} | obokkkk/mt5-base_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T05:50:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base\_2
===========
This model is a fine-tuned version of obokkkk/mt5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1742
* Bleu: 9.479
* Gen Len: 16.9226
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 256\n* total\\_train\\_batch\\_size: 2048\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1514648481281056772/ACun... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/cokedupoptions-greg16676935420-parikpatelcfa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T06:44:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
greg & John W. Rich (Fake Tech Exec) & Dr. Parik Patel, BA, CFA, ACCA Esq. (URL)
@cokedupoptions-greg16676935420-parikpatelcfa
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317
## Validation Metrics
- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.925925925925925... | {"language": "unk", "tags": "autotrain", "datasets": ["Mim/autotrain-data-procell-expert"], "widget": [{"text": "ACE2 overexpression in AAV cell lines"}], "co2_eq_emissions": 0.004814823138367317} | Mim/pro-cell-expert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Mim/autotrain-data-procell-expert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T07:30:08+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Mim/autotrain-data-procell-expert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317
## Validation Metrics
- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.925925925925925... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 800724769\n- CO2 Emissions (in grams): 0.004814823138367317",
"## Validation Metrics\n\n- Loss: 0.4749071002006531\n- Accuracy: 0.9\n- Precision: 0.8928571428571429\n- Recall: 0.9615384615384616\n- AUC: 0.9065934065934066\n- F1... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Mim/autotrain-data-procell-expert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 800724769\n- CO2 Emissions (in gr... |
text2text-generation | transformers |
# doc2query/msmarco-german-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 2... | {"language": "de", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python ist eine universelle, \u00fcblicherweise interpretierte, h\u00f6here Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu f\u00f6rdern. So werden beispielsweise Bl\u00f6cke nich... | doc2query/msmarco-german-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"de",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T07:49:21+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #de #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-german-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucen... | [
"# doc2query/msmarco-german-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSea... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #de #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-german-mt5-base-v1\r\n\r\nThis is a doc2quer... |
automatic-speech-recognition | transformers | # wav2vec2-large-xlsr-galician
---
language: gl
datasets:
- OpenSLR 77
- mozilla-foundation common_voice_8_0
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Galician wav2vec2-large-xlsr-galician
results:
- task:
name: Speec... | {} | ifrz/wav2vec2-large-xlsr-galician | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T07:55:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| # wav2vec2-large-xlsr-galician
---
language: gl
datasets:
- OpenSLR 77
- mozilla-foundation common_voice_8_0
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Galician wav2vec2-large-xlsr-galician
results:
- task:
name: Speec... | [
"# wav2vec2-large-xlsr-galician\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\n- mozilla-foundation common_voice_8_0\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache-2.0\nmodel-index:\n- name: Galician wav2vec2-large-xlsr-galician\n results:\n - ta... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-galician\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\n- mozilla-foundation common_voice_8_0\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- ... |
token-classification | transformers |
# gunghio/xlm-roberta-base-finetuned-panx-ner
This model was trained starting from xlm-roberta-base on a subset of xtreme dataset.
`xtreme` datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish.
Only 75% of the whole dataset was used.
## In... | {"language": ["it", "en", "de", "fr", "es", "multilingual"], "license": ["mit"], "datasets": ["xtreme"], "metrics": [{"precision": 0.874}, {"recall": 0.88}, {"f1": 0.877}, {"accuracy": 0.943}], "inference": {"parameters": {"aggregation_strategy": "first"}}} | gunghio/xlm-roberta-base-finetuned-panx-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"it",
"en",
"de",
"fr",
"es",
"multilingual",
"dataset:xtreme",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T10:15:55+00:00 | [] | [
"it",
"en",
"de",
"fr",
"es",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #it #en #de #fr #es #multilingual #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us
| gunghio/xlm-roberta-base-finetuned-panx-ner
===========================================
This model was trained starting from xlm-roberta-base on a subset of xtreme dataset.
'xtreme' datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish.
On... | [
"### Training results\n\n\nIt achieves the following results on the evaluation set:\n\n\n* Precision: 0.8744154472771157\n* Recall: 0.8791424269015351\n* F1: 0.8767725659462058\n* Accuracy: 0.9432040948504613\n\n\nDetails:\n\n\n\nUsage\n-----\n\n\nSet aggregation stragey according to documentation."
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #it #en #de #fr #es #multilingual #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training results\n\n\nIt achieves the following results on the evaluation set:\n\n\n* Precision: 0.8744154472771157\n* Re... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-powo_all
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-powo_all", "results": []}]} | ViktorDo/distilbert-base-uncased-finetuned-powo_all | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T10:39:55+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-powo_all
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# distilbert-base-uncased-finetuned-powo_all\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-powo_all\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the foll... |
text2text-generation | transformers |
# doc2query/msmarco-arabic-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 2... | {"language": "ar", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "\u0628\u0627\u064a\u062b\u0648\u0646 (\u0628\u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a\u0629: Python)\u200f \u0647\u064a \u0644\u063a\u0629 \u0628\u0631\u0645\u062c\u0629\u060c \u0639\u0627\u0644\u064a\u0629 ... | doc2query/msmarco-arabic-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ar",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:42:40+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"ar"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #ar #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-arabic-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucen... | [
"# doc2query/msmarco-arabic-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSea... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #ar #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-arabic-mt5-base-v1\r\n\r\nThis is a doc2quer... |
text2text-generation | transformers |
# doc2query/msmarco-chinese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs ... | {"language": "zh", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python\uff08\u82f1\u570b\u767c\u97f3\uff1a/\u02c8pa\u026a\u03b8\u0259n/ \u7f8e\u570b\u767c\u97f3\uff1a/\u02c8pa\u026a\u03b8\u0251\u02d0n/\uff09\uff0c\u662f\u4e00\u79cd\u5e7f\u6cdb\u4f7f\u7528\u7684\u89e3\u91ca\u578b\u300... | doc2query/msmarco-chinese-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"zh",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:47:33+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #zh #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-chinese-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luce... | [
"# doc2query/msmarco-chinese-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSe... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #zh #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-chinese-mt5-base-v1\r\n\r\nThis is a doc2que... |
text2text-generation | transformers |
# doc2query/msmarco-dutch-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20... | {"language": "nl", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python is een programmeertaal die begin jaren 90 ontworpen en ontwikkeld werd door Guido van Rossum, destijds verbonden aan het Centrum voor Wiskunde en Informatica (daarvoor Mathematisch Centrum) in Amsterdam. De taal i... | doc2query/msmarco-dutch-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"nl",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:49:58+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"nl"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #nl #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-dutch-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene... | [
"# doc2query/msmarco-dutch-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSear... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #nl #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-dutch-mt5-base-v1\r\n\r\nThis is a doc2query... |
text2text-generation | transformers |
# doc2query/msmarco-french-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 2... | {"language": "fr", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python (prononc\u00e9 /pi.t\u0254\u0303/) est un langage de programmation interpr\u00e9t\u00e9, multi-paradigme et multiplateformes. Il favorise la programmation imp\u00e9rative structur\u00e9e, fonctionnelle et orient\u... | doc2query/msmarco-french-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:52:40+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"fr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #fr #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-french-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucen... | [
"# doc2query/msmarco-french-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSea... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #fr #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-french-mt5-base-v1\r\n\r\nThis is a doc2quer... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# maxime7770/model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "maxime7770/model", "results": []}]} | maxime7770/model | null | [
"transformers",
"tf",
"camembert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T10:54:14+00:00 | [] | [] | TAGS
#transformers #tf #camembert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| maxime7770/model
================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1211
* Validation Loss: 0.4812
* Epoch: 49
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 650, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #camembert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class... |
text2text-generation | transformers |
# doc2query/msmarco-hindi-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20... | {"language": "hi", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "\u092a\u093e\u0907\u0925\u0928 \u090f\u0915 \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0915\u093e\u0930\u094d\u092f\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u092f\u0941\u0915\u094d\u0924, \u0909\u091a\u... | doc2query/msmarco-hindi-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"hi",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:55:47+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"hi"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #hi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-hindi-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene... | [
"# doc2query/msmarco-hindi-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSear... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #hi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-hindi-mt5-base-v1\r\n\r\nThis is a doc2query... |
text2text-generation | transformers |
# doc2query/msmarco-indonesian-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragrap... | {"language": "id", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python adalah bahasa pemrograman tujuan umum yang ditafsirkan, tingkat tinggi. Dibuat oleh Guido van Rossum dan pertama kali dirilis pada tahun 1991, filosofi desain Python menekankan keterbacaan kode dengan penggunaan s... | doc2query/msmarco-indonesian-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"id",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T10:58:44+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"id"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #id #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# doc2query/msmarco-indonesian-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or L... | [
"# doc2query/msmarco-indonesian-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, Ope... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #id #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# doc2query/msmarco-indonesian-mt5-base-v1\r\n\r\nThi... |
text2text-generation | transformers |
# doc2query/msmarco-italian-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs ... | {"language": "it", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python \u00e8 un linguaggio di programmazione di alto livello, orientato a oggetti, adatto, tra gli altri usi, a sviluppare applicazioni distribuite, scripting, computazione numerica e system testing."}]} | doc2query/msmarco-italian-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"it",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T11:00:49+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"it"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #it #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-italian-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luce... | [
"# doc2query/msmarco-italian-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSe... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #it #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-italian-mt5-base-v1\r\n\r\nThis is a doc2que... |
text2text-generation | transformers |
# doc2query/msmarco-japanese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs... | {"language": "ja", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python\uff08\u30d1\u30a4\u30bd\u30f3\uff09\u306f\u30a4\u30f3\u30bf\u30fc\u30d7\u30ea\u30bf\u578b\u306e\u9ad8\u6c34\u6e96\u6c4e\u7528\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u3067\u3042\u308b\u3002\u30b0\u30... | doc2query/msmarco-japanese-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ja",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T11:05:21+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #ja #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-japanese-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luc... | [
"# doc2query/msmarco-japanese-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenS... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #ja #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-japanese-mt5-base-v1\r\n\r\nThis is a doc2qu... |
text2text-generation | transformers |
# doc2query/msmarco-portuguese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragrap... | {"language": "pt", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python \u00e9 uma linguagem de programa\u00e7\u00e3o de alto n\u00edvel, interpretada de script, imperativa, orientada a objetos, funcional, de tipagem din\u00e2mica e forte. Foi lan\u00e7ada por Guido van Rossum em 1991... | doc2query/msmarco-portuguese-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"pt",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T11:07:58+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #pt #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-portuguese-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or L... | [
"# doc2query/msmarco-portuguese-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, Ope... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #pt #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-portuguese-mt5-base-v1\r\n\r\nThis is a doc2... |
text2text-generation | transformers |
# doc2query/msmarco-russian-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs ... | {"language": "ru", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python (\u041c\u0424\u0410: [\u02c8p\u028c\u026a\u03b8(\u0259)n]; \u0432 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435 \u0432\u0441\u0442\u0440\u0435\u0447\u0430\u044e\u0442\u0441\u044f \u043d... | doc2query/msmarco-russian-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ru",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T11:10:14+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"ru"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #ru #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-russian-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luce... | [
"# doc2query/msmarco-russian-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSe... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #ru #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-russian-mt5-base-v1\r\n\r\nThis is a doc2que... |
text2text-generation | transformers |
# doc2query/msmarco-spanish-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs ... | {"language": "es", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python es un lenguaje de alto nivel de programaci\u00f3n interpretado cuya filosof\u00eda hace hincapi\u00e9 en la legibilidad de su c\u00f3digo, se utiliza para desarrollar aplicaciones de todo tipo, ejemplos: Instagram... | doc2query/msmarco-spanish-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"es",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T11:11:43+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #es #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-spanish-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luce... | [
"# doc2query/msmarco-spanish-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSe... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #es #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-spanish-mt5-base-v1\r\n\r\nThis is a doc2que... |
sentence-similarity | sentence-transformers |
# deepset/all-mpnet-base-v2-table
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | deepset/all-mpnet-base-v2-table | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-29T11:28:50+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #has_space #region-us
|
# deepset/all-mpnet-base-v2-table
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed... | [
"# deepset/all-mpnet-base-v2-table\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #has_space #region-us \n",
"# deepset/all-mpnet-base-v2-table\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... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | Ansh/keras-demo | null | [
"keras",
"bert",
"region:us"
] | null | 2022-04-29T11:55:31+00:00 | [] | [] | TAGS
#keras #bert #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #bert #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were use... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ko-en-finetuned-ko-to-en4
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ko-en-finetuned-ko-to-en4", "results": []}]} | astrojihye/opus-mt-ko-en-finetuned-ko-to-en4 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T13:09:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ko-en-finetuned-ko-to-en4
=================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ko-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9824
* Bleu: 0.5767
* Gen Len: 13.1529
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 512\n* total\\_train\\_batch\\_size: 2048\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batc... |
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. -->
# nb-bert-base-target-group
This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-ba... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "NbAiLab/nb-bert-base", "model-index": [{"name": "nb-bert-base-target-group", "results": []}]} | thusken/nb-bert-base-target-group | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:NbAiLab/nb-bert-base",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T13:24:17+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #base_model-NbAiLab/nb-bert-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| nb-bert-base-target-group
=========================
This model is a fine-tuned version of NbAiLab/nb-bert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2820
* Accuracy: 0.8822
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #base_model-NbAiLab/nb-bert-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T13:46:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
fill-mask | transformers |
# AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders
This model comes from the following GitHub repository: [https://github.com/neulab/awesome-align](https://github.com/neulab/awesome-align)
It corresponds to this paper: [https://arxiv.org/abs/2101.08231](https://arxiv.org/abs/2101.08231)
Please cite ... | {"language": ["de", "fr", "en", "ro", "zh"], "license": "bsd-3-clause", "tags": ["sentence alignment"]} | aneuraz/awesome-align-with-co | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"sentence alignment",
"de",
"fr",
"en",
"ro",
"zh",
"arxiv:2101.08231",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T13:55:54+00:00 | [
"2101.08231"
] | [
"de",
"fr",
"en",
"ro",
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #sentence alignment #de #fr #en #ro #zh #arxiv-2101.08231 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
|
# AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders
This model comes from the following GitHub repository: URL
It corresponds to this paper: URL
Please cite the original paper if you decide to use the model:
'awesome-align' is a tool that can extract word alignments from multilingual BERT (mBERT... | [
"# AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders\n\nThis model comes from the following GitHub repository: URL\n\nIt corresponds to this paper: URL\n\nPlease cite the original paper if you decide to use the model: \n\n\n\n\n'awesome-align' is a tool that can extract word alignments from multiling... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #sentence alignment #de #fr #en #ro #zh #arxiv-2101.08231 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n",
"# AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders\n\nThis model comes from the following GitHub repository: U... |
text-classification | transformers | Pytorch Port of [EmoRoberta model](https://huggingface.co/arpanghoshal/EmoRoBERTa). | {} | Sindhu/emo_roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T14:09:03+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Pytorch Port of EmoRoberta model. | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab1", "results": []}]} | sameearif88/wav2vec2-base-timit-demo-colab1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T14:31:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab1
===============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7411
* Wer: 0.5600
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
null | null | ## Common voice release generator
1. Copy the latest release id from the `RELEASES` dict in https://github.com/common-voice/common-voice/blob/main/web/src/components/pages/datasets/releases.ts
to the `VERSIONS` variable in `generate_datasets.py`.
2. Copy the languages from https://github.com/common-voice/common-voice... | {} | anton-l/common_voice_generator | null | [
"region:us"
] | null | 2022-04-29T14:53:56+00:00 | [] | [] | TAGS
#region-us
| ## Common voice release generator
1. Copy the latest release id from the 'RELEASES' dict in URL
to the 'VERSIONS' variable in 'generate_datasets.py'.
2. Copy the languages from URL
(replacing 'release-v1.78.0' with the latest version tag) to the 'URL' file.
3. Run 'python generate_datasets.py' to generate the data... | [
"## Common voice release generator\n\n1. Copy the latest release id from the 'RELEASES' dict in URL \nto the 'VERSIONS' variable in 'generate_datasets.py'.\n2. Copy the languages from URL\n (replacing 'release-v1.78.0' with the latest version tag) to the 'URL' file.\n3. Run 'python generate_datasets.py' to genera... | [
"TAGS\n#region-us \n",
"## Common voice release generator\n\n1. Copy the latest release id from the 'RELEASES' dict in URL \nto the 'VERSIONS' variable in 'generate_datasets.py'.\n2. Copy the languages from URL\n (replacing 'release-v1.78.0' with the latest version tag) to the 'URL' file.\n3. Run 'python genera... |
image-classification | transformers |
# skin_type
Aiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.resear... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | driboune/skin_type | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T14:59:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# skin_type
Aiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the d... | [
"# skin_type\nAiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# skin_type\nAiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly ... |
text-generation | transformers |
# gpt2-large-wechsel-ukrainian
[`gpt2-large`](https://huggingface.co/gpt2-large) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://arxiv.org/abs/2112.065989). | {"language": "uk", "license": "mit"} | benjamin/gpt2-large-wechsel-ukrainian | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"uk",
"arxiv:2112.06598",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T15:23:50+00:00 | [
"2112.06598"
] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #uk #arxiv-2112.06598 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# gpt2-large-wechsel-ukrainian
'gpt2-large' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. | [
"# gpt2-large-wechsel-ukrainian\n\n'gpt2-large' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models."
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #uk #arxiv-2112.06598 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# gpt2-large-wechsel-ukrainian\n\n'gpt2-large' transferred to Ukrainian using the method from the NAACL2022 paper ... |
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. -->
# data2vec-text-base-finetuned-mnli
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "data2vec-text-base-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metrics":... | mrm8488/data2vec-text-base-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T15:27:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| data2vec-text-base-finetuned-mnli
=================================
This model is a fine-tuned version of facebook/data2vec-text-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5521
* Accuracy: 0.7862
Model description
-----------------
More information needed
Inte... | [
"### 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 #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
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. -->
# HiNER-collapsed-muril-base-cased
This model was trained from scratch on the cfilt/HiNER-collapsed dataset.
## Model description... | {"tags": ["generated_from_trainer"], "datasets": ["cfilt/HiNER-collapsed"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "HiNER-collapsed-muril-base-cased", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "HiNER Collapsed", "type": "cfilt/... | cfilt/HiNER-collapsed-muril-base-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:cfilt/HiNER-collapsed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T16:19:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-cfilt/HiNER-collapsed #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# HiNER-collapsed-muril-base-cased
This model was trained from scratch on the cfilt/HiNER-collapsed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpar... | [
"# HiNER-collapsed-muril-base-cased\n\nThis model was trained from scratch on the cfilt/HiNER-collapsed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proced... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-cfilt/HiNER-collapsed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# HiNER-collapsed-muril-base-cased\n\nThis model was trained from scratch on the cfilt/HiNER-collapsed dataset.",
... |
text-generation | transformers |
# gpt2-wechsel-ukrainian
[`gpt2`](https://huggingface.co/gpt2) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://arxiv.org/abs/2112.065989). | {"language": "uk", "license": "mit"} | benjamin/gpt2-wechsel-ukrainian | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"uk",
"arxiv:2112.06598",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T16:35:16+00:00 | [
"2112.06598"
] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #uk #arxiv-2112.06598 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-wechsel-ukrainian
'gpt2' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. | [
"# gpt2-wechsel-ukrainian\n\n'gpt2' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models."
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #uk #arxiv-2112.06598 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-wechsel-ukrainian\n\n'gpt2' transferred to Ukrainian using the method from the NAACL2022 paper WECHSEL: Effective init... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-trainings
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It a... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-trainings", "results": []}]} | umarkhalid96/t5-small-trainings | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T17:27:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-trainings
==================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2580
* Rouge1: 41.5251
* Rouge2: 19.8842
* Rougel: 36.4895
* Rougelsum: 37.2565
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
fill-mask | transformers |
# Pile of Law BERT large model 2 (uncased)
Pretrained model on English language legal and administrative text using the [RoBERTa](https://arxiv.org/abs/1907.11692) pretraining objective. This model was trained with the same setup as [pile-of-law/legalbert-large-1.7M-1](https://huggingface.co/pile-of-law/legalbert-larg... | {"language": ["en"], "tags": ["legal"], "datasets": ["pile-of-law/pile-of-law"], "pipeline_tag": "fill-mask"} | pile-of-law/legalbert-large-1.7M-2 | null | [
"transformers",
"pytorch",
"bert",
"legal",
"fill-mask",
"en",
"dataset:pile-of-law/pile-of-law",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:2110.00976",
"arxiv:2207.00220",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-29T17:27:57+00:00 | [
"1907.11692",
"1810.04805",
"2110.00976",
"2207.00220"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #legal #fill-mask #en #dataset-pile-of-law/pile-of-law #arxiv-1907.11692 #arxiv-1810.04805 #arxiv-2110.00976 #arxiv-2207.00220 #endpoints_compatible #has_space #region-us
|
# Pile of Law BERT large model 2 (uncased)
Pretrained model on English language legal and administrative text using the RoBERTa pretraining objective. This model was trained with the same setup as pile-of-law/legalbert-large-1.7M-1, but with a different seed.
## Model description
Pile of Law BERT large 2 is a transfo... | [
"# Pile of Law BERT large model 2 (uncased)\nPretrained model on English language legal and administrative text using the RoBERTa pretraining objective. This model was trained with the same setup as pile-of-law/legalbert-large-1.7M-1, but with a different seed.",
"## Model description\nPile of Law BERT large 2 is... | [
"TAGS\n#transformers #pytorch #bert #legal #fill-mask #en #dataset-pile-of-law/pile-of-law #arxiv-1907.11692 #arxiv-1810.04805 #arxiv-2110.00976 #arxiv-2207.00220 #endpoints_compatible #has_space #region-us \n",
"# Pile of Law BERT large model 2 (uncased)\nPretrained model on English language legal and administra... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_voxpopuli_en
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["en"], "license": "apache-2.0", "tags": ["voxpopuli", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "model-index": [{"name": "xtreme_s_xlsr_300m_voxpopuli_en", "results": []}]} | anton-l/xtreme_s_xlsr_300m_voxpopuli_en | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"voxpopuli",
"google/xtreme_s",
"generated_from_trainer",
"en",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T17:58:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #voxpopuli #google/xtreme_s #generated_from_trainer #en #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_voxpopuli\_en
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - VOXPOPULI.EN dataset.
It achieves the following results on the evaluation set:
* Cer: 0.0966
* Loss: 0.3127
* Wer: 0.1549
* Predict Samples: 1842
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 8\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #voxpopuli #google/xtreme_s #generated_from_trainer #en #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu", "results": []}]} | omar47/wav2vec2-large-xls-r-300m-urdu | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T18:05:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-urdu
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m.
It achieves the following results on the evaluation set:
* Loss: 0.5285
* Wer: 0.1702
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
fill-mask | transformers |
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | {"license": "gpl-3.0"} | snowood1/ConfliBERT-scr-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T19:52:24+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | {"license": "gpl-3.0"} | snowood1/ConfliBERT-cont-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T19:54:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | {"license": "gpl-3.0"} | snowood1/ConfliBERT-scr-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-29T20:00:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | {"license": "gpl-3.0"} | snowood1/ConfliBERT-cont-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T20:01:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/french_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
.... | {"language": "fr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/french_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"fr",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-29T20:22:08+00:00 | [
"1804.00015"
] | [
"fr"
] | TAGS
#espnet #audio #automatic-speech-recognition #fr #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/french\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Apr 29 17:20:37 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 1... | [
"### 'espnet/french\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Apr 29 17:20:37 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #fr #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/french\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nE... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlsr-53-bemba-5hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-53-bemba-5hrs", "results": []}]} | csikasote/xlsr-53-bemba-5hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T20:24:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-53-bemba-5hrs
==================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3414
* Wer: 0.4867
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/arabic_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
.... | {"language": "ar", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/arabic_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ar",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-29T20:28:42+00:00 | [
"1804.00015"
] | [
"ar"
] | TAGS
#espnet #audio #automatic-speech-recognition #ar #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/arabic\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Apr 16 17:11:01 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 1... | [
"### 'espnet/arabic\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Apr 16 17:11:01 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ar #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/arabic\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nE... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/turkish_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
... | {"language": "tr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/turkish_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"tr",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-29T20:32:59+00:00 | [
"1804.00015"
] | [
"tr"
] | TAGS
#espnet #audio #automatic-speech-recognition #tr #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/turkish\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Apr 16 17:16:06 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, ... | [
"### 'espnet/turkish\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Apr 16 17:16:06 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #tr #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/turkish\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\n... |
text2text-generation | transformers |
# doc2query/msmarco-vietnamese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragrap... | {"language": "vi", "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python (ph\u00e1t \u00e2m ti\u1ebfng Anh: /\u02c8pa\u026a\u03b8\u0251\u02d0n/) l\u00e0 m\u1ed9t ng\u00f4n ng\u1eef l\u1eadp tr\u00ecnh b\u1eadc cao cho c\u00e1c m\u1ee5c \u0111\u00edch l\u1eadp tr\u00ecnh \u0111a n\u0103... | doc2query/msmarco-vietnamese-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"vi",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T21:05:47+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"vi"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #vi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-vietnamese-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or L... | [
"# doc2query/msmarco-vietnamese-mt5-base-v1\r\n\r\nThis is a doc2query model based on mT5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, Ope... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #vi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-vietnamese-mt5-base-v1\r\n\r\nThis is a doc2... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetune
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetune", "results": []}]} | dhlanm/distilbert-base-uncased-finetune | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T21:16:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetune
================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1315
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.9715
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | Percival/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T21:34:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
image-classification | transformers |
# ALL-3
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/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Ahmed9275/ALL-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T22:42:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ALL-3
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 | [
"# ALL-3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images"
] | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALL-3\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 t... |
text2text-generation | transformers |
# Tiny M2M100 model
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful beyond functional testing.
Do not try to use it for anything that requires quality.
The model is indeed 4MB in size.
You can see how it was created [here](https://huggingface.co/stas/tiny-m2m_100/b... | {"language": ["en"], "license": "apache-2.0", "tags": ["testing"]} | stas/tiny-m2m_100 | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"testing",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T22:50:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #testing #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Tiny M2M100 model
This is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful beyond functional testing.
Do not try to use it for anything that requires quality.
The model is indeed 4MB in size.
You can see how it was created here
If you're looking for the real model, plea... | [
"# Tiny M2M100 model\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful beyond functional testing.\n\nDo not try to use it for anything that requires quality.\n\nThe model is indeed 4MB in size.\n\nYou can see how it was created here\n\n\nIf you're looking for the r... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #testing #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tiny M2M100 model\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful beyond functional testing.\n\nDo not t... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | Siddhart/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T22:51:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | tonydiana1/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-29T23:08:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6425
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | moaiz237/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-29T23:22:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4769
* Wer: 0.4305
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
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