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# ai-soco-c++-roberta-tiny
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do cod... | {"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco"], "datasets": ["ai-soco"], "metrics": ["perplexity"]} | aliosm/ai-soco-cpp-roberta-tiny | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"c++"
] | TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-tiny
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification an... | [
"# ai-soco-c++-roberta-tiny",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship i... | [
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"# ai-soco-c++-roberta-tiny",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from Co... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | alipsezzar/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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"# Harry Potter DialoGPT Model"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SH-persian-base | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| More information about models is available here. | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-100-persian-base | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-SS-80-persian-base | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/PEGASUS-persian-base | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-perkey-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-perkey-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-tebyan | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-voa-title | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/TRANSFORMER-persian-base-wiki-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| More information about models is available here. | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory. | {"tags": ["conversational"]} | alistair7/bbt-diagpt2-model | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory. | [
"# A conversational model based on the character of Sheldon Cooper from Big Bang Theory."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# A conversational model based on the character of Sheldon Cooper from Big Bang Theory."
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-pretrain-finetuned-coqa-falt
This model is a fine-tuned version of [alistvt/bert-base-uncased-pretrained-mlm-c... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrain-finetuned-coqa-falt", "results": []}]} | alistvt/bert-base-uncased-pretrain-finetuned-coqa-falt | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| bert-base-uncased-pretrain-finetuned-coqa-falt
==============================================
This model is a fine-tuned version of alistvt/bert-base-uncased-pretrained-mlm-coqa-stories on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8125
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* see... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-pretrain-finetuned-coqa-falttened
This model is a fine-tuned version of [alistvt/bert-base-uncased-pretrained-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrain-finetuned-coqa-falttened", "results": []}]} | alistvt/bert-base-uncased-pretrain-finetuned-coqa-falttened | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| bert-base-uncased-pretrain-finetuned-coqa-falttened
===================================================
This model is a fine-tuned version of alistvt/bert-base-uncased-pretrained-mlm-coqa-stories on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8655
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* see... |
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. -->
# bert-base-uncased-pretrained-clm-coqa-stories
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrained-clm-coqa-stories", "results": []}]} | alistvt/bert-base-uncased-pretrained-clm-coqa-stories | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-pretrained-clm-coqa-stories
=============================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0002
Model description
-----------------
More information needed
Intended ... | [
"### 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 #bert #text-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: 2e-05\n* train\\_batch\\_siz... |
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-pretrained-mlm-coqa-stories
This model was trained from scratch on the None dataset.
It achieves the following... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrained-mlm-coqa-stories", "results": []}]} | alistvt/bert-base-uncased-pretrained-mlm-coqa-stories | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-pretrained-mlm-coqa-stories
=============================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8310
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 #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
feature-extraction | transformers |
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transf... | {"language": "pl", "license": "cc-by-4.0", "tags": ["herbert"]} | allegro/herbert-base-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"herbert",
"pl",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
| HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.
Model tr... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# HerBERT tokenizer
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** tokenizer is a character level byte-pair encoding with
vocabulary size of 50k tokens. The tokenizer was trained on [Wolne Lektury](https://wolnelektury.pl/) and a publicly available subset of
[National Corpus of Polish](http://nkjp.pl/i... | {"language": "pl"} | allegro/herbert-klej-cased-tokenizer-v1 | null | [
"transformers",
"xlm",
"pl",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #xlm #pl #endpoints_compatible #region-us
|
# HerBERT tokenizer
HerBERT tokenizer is a character level byte-pair encoding with
vocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of
National Corpus of Polish with fastBPE library.
Tokenizer utilize 'XLMTokenizer' implementation from transformers.
## Tokeniz... | [
"# HerBERT tokenizer\n\nHerBERT tokenizer is a character level byte-pair encoding with\nvocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of\nNational Corpus of Polish with fastBPE library.\nTokenizer utilize 'XLMTokenizer' implementation from transformers.",
... | [
"TAGS\n#transformers #xlm #pl #endpoints_compatible #region-us \n",
"# HerBERT tokenizer\n\nHerBERT tokenizer is a character level byte-pair encoding with\nvocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of\nNational Corpus of Polish with fastBPE library.\... |
null | transformers |
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish Corpora
using only MLM objective with dynamic masking of whole words. For more details, please refer to:
[KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://arxiv.org/abs/200... | {"language": "pl"} | allegro/herbert-klej-cased-v1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"pl",
"arxiv:2005.00630",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.00630"
] | [
"pl"
] | TAGS
#transformers #pytorch #jax #roberta #pl #arxiv-2005.00630 #endpoints_compatible #region-us
| HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish Corpora
using only MLM objective with dynamic masking of whole words. For more details, please refer to:
KLEJ: Comprehensive Benchmark for Polish Language Understanding.
Dataset
-------
HerBERT training dataset is a combination of several pu... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #pl #arxiv-2005.00630 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transf... | {"language": "pl", "license": "cc-by-4.0", "tags": ["herbert"]} | allegro/herbert-large-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"herbert",
"pl",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
| HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.
Model tr... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers |
# plT5 Base
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
## Corpus
plT5 was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](https... | {"language": "pl", "license": "cc-by-4.0", "tags": ["T5", "translation", "summarization", "question answering", "reading comprehension"], "datasets": ["ccnet", "nkjp", "wikipedia", "open subtitles", "free readings"]} | allegro/plt5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"pl",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| plT5 Base
=========
plT5 models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
Corpus
------
plT5 was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords u... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
# plT5 Large
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
## Corpus
plT5 was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](http... | {"language": "pl", "license": "cc-by-4.0", "tags": ["T5", "translation", "summarization", "question answering", "reading comprehension"], "datasets": ["ccnet", "nkjp", "wikipedia", "open subtitles", "free readings"]} | allegro/plt5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"pl",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| plT5 Large
==========
plT5 models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
Corpus
------
plT5 was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
translation | transformers |
# plT5 Small
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
## Corpus
plT5 was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](http... | {"language": "pl", "license": "cc-by-4.0", "tags": ["T5", "translation", "summarization", "question answering", "reading comprehension"], "datasets": ["ccnet", "nkjp", "wikipedia", "open subtitles", "free readings"]} | allegro/plt5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"pl",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| plT5 Small
==========
plT5 models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
Corpus
------
plT5 was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
question-answering | allennlp |
This is an implementation of the BiDAF model with ELMo embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word represe... | {"language": "en", "tags": ["allennlp", "question-answering"]} | allenai/bidaf-elmo | null | [
"allennlp",
"tensorboard",
"question-answering",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#allennlp #tensorboard #question-answering #en #has_space #region-us
|
This is an implementation of the BiDAF model with ELMo embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word represe... | [] | [
"TAGS\n#allennlp #tensorboard #question-answering #en #has_space #region-us \n"
] |
question-answering | allennlp |
This is an implementation of the BiDAF model with GloVe embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word repres... | {"language": "en", "tags": ["allennlp", "question-answering"]} | allenai/bidaf | null | [
"allennlp",
"tensorboard",
"question-answering",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#allennlp #tensorboard #question-answering #en #has_space #region-us
|
This is an implementation of the BiDAF model with GloVe embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word repres... | [] | [
"TAGS\n#allennlp #tensorboard #question-answering #en #has_space #region-us \n"
] |
null | transformers |
# BioMed-RoBERTa-base
BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the [Semantic Scholar](https://www.semanticscholar.org) corpus via continued pretraining. This amounts to 7.55B tokens and 47GB of data.... | {"language": "en", "thumbnail": "https://huggingface.co/front/thumbnails/allenai.png"} | allenai/biomed_roberta_base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #en #endpoints_compatible #has_space #region-us
| BioMed-RoBERTa-base
===================
BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the Semantic Scholar corpus via continued pretraining. This amounts to 7.55B tokens and 47GB of data. We use the full ... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #en #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from [*bart-base*](https://... | {"language": "en", "license": "apache-2.0"} | allenai/led-base-16384 | null | [
"transformers",
"pytorch",
"tf",
"led",
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"en",
"arxiv:2004.05150",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #led #text2text-generation #en #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Introduction
Allenai's Longformer Encoder-Decoder (LED).
As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from *bart-base* since both models share the exact same architecture. To be able to process 16K tokens, *bart-base*'s p... | [
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nAs described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from *bart-base* since both models share the exact same architecture. To be able to process 16K tokens, *bart-b... | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #en #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nAs described in Longformer: The Long-Document Transformer by Iz Beltagy, ... |
text2text-generation | transformers |
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
This is the official *led-large-16384* checkpoint that is fine-tuned on the arXiv dataset.*led-large-16384-arxiv* is the official fine-tuned version of [led-large-16384](https://huggingface.co/allenai/led-... | {"language": "en", "license": "apache-2.0", "datasets": ["scientific_papers"]} | allenai/led-large-16384-arxiv | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"dataset:scientific_papers",
"arxiv:2004.05150",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #led #text2text-generation #en #dataset-scientific_papers #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Introduction
Allenai's Longformer Encoder-Decoder (LED).
This is the official *led-large-16384* checkpoint that is fine-tuned on the arXiv dataset.*led-large-16384-arxiv* is the official fine-tuned version of led-large-16384. As presented in the paper, the checkpoint achieves state-of-the-art results on arxiv
!m... | [
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nThis is the official *led-large-16384* checkpoint that is fine-tuned on the arXiv dataset.*led-large-16384-arxiv* is the official fine-tuned version of led-large-16384. As presented in the paper, the checkpoint achieves state-of-the-art results on a... | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #en #dataset-scientific_papers #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nThis is the official *led-large-16384* checkpo... |
text2text-generation | transformers |
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-large-16384* was initialized from [*bart-large*](https:... | {"language": "en", "license": "apache-2.0"} | allenai/led-large-16384 | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"arxiv:2004.05150",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #led #text2text-generation #en #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Introduction
Allenai's Longformer Encoder-Decoder (LED).
As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-large-16384* was initialized from *bart-large* since both models share the exact same architecture. To be able to process 16K tokens, *bart-large*'... | [
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nAs described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-large-16384* was initialized from *bart-large* since both models share the exact same architecture. To be able to process 16K tokens, *bart... | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #en #arxiv-2004.05150 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nAs described in Longformer: The Long-Document Transformer by Iz Beltagy, ... |
null | transformers |
# longformer-base-4096-extra.pos.embd.only
This model is similar to `longformer-base-4096` but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings.
### Citing
If you use `Longformer` in your research, please cite [Longformer: The Long-Doc... | {"language": "en"} | allenai/longformer-base-4096-extra.pos.embd.only | null | [
"transformers",
"pytorch",
"tf",
"longformer",
"en",
"arxiv:2004.05150",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #longformer #en #arxiv-2004.05150 #endpoints_compatible #region-us
|
# URL
This model is similar to 'longformer-base-4096' but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings.
### Citing
If you use 'Longformer' in your research, please cite Longformer: The Long-Document Transformer.
'Longformer' is a... | [
"# URL\n\nThis model is similar to 'longformer-base-4096' but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings.",
"### Citing\n\nIf you use 'Longformer' in your research, please cite Longformer: The Long-Document Transformer.\n\n\n'Lon... | [
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"# URL\n\nThis model is similar to 'longformer-base-4096' but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings.",
"### Citing\n... |
null | transformers |
# longformer-base-4096
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096.
Longformer uses a combination of a... | {"language": "en", "license": "apache-2.0"} | allenai/longformer-base-4096 | null | [
"transformers",
"pytorch",
"tf",
"rust",
"longformer",
"en",
"arxiv:2004.05150",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #rust #longformer #en #arxiv-2004.05150 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# longformer-base-4096
Longformer is a transformer model for long documents.
'longformer-base-4096' is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096.
Longformer uses a combination of a sliding window (local) attention an... | [
"# longformer-base-4096\nLongformer is a transformer model for long documents. \n\n'longformer-base-4096' is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096. \n \nLongformer uses a combination of a sliding window (local) att... | [
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"# longformer-base-4096\nLongformer is a transformer model for long documents. \n\n'longformer-base-4096' is a BERT-like model started from the RoBERTa checkpoint and pret... |
text-classification | transformers |
# Longformer for SciCo
This model is the `unified` model discussed in the paper [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021)](https://openreview.net/forum?id=OFLbgUP04nC) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass prob... | {"language": "en", "license": "apache-2.0", "tags": ["longformer", "longformer-scico"], "datasets": ["allenai/scico"], "inference": false} | allenai/longformer-scico | null | [
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"longformer-scico",
"en",
"dataset:allenai/scico",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #longformer #text-classification #longformer-scico #en #dataset-allenai/scico #license-apache-2.0 #autotrain_compatible #has_space #region-us
|
# Longformer for SciCo
This model is the 'unified' model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mentions 'm1'... | [
"# Longformer for SciCo\n\nThis model is the 'unified' model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mention... | [
"TAGS\n#transformers #pytorch #longformer #text-classification #longformer-scico #en #dataset-allenai/scico #license-apache-2.0 #autotrain_compatible #has_space #region-us \n",
"# Longformer for SciCo\n\nThis model is the 'unified' model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Sc... |
text2text-generation | transformers |
# macaw-11b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexible ... | {"language": "en", "license": "apache-2.0", "widget": [{"text": "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?"}]} | allenai/macaw-11b | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# macaw-11b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexible ... | [
"# macaw-11b",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of \ngeneral question answering, \nshowing robustness outside the domains it was trained on. It has been trained in \"multi-angle\" fashion, \nwhich means it can han... | [
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"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use... |
text2text-generation | transformers |
# macaw-3b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexible s... | {"language": "en", "license": "apache-2.0", "widget": [{"text": "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?"}]} | allenai/macaw-3b | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# macaw-3b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexible s... | [
"# macaw-3b",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of \ngeneral question answering, \nshowing robustness outside the domains it was trained on. It has been trained in \"multi-angle\" fashion, \nwhich means it can hand... | [
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"# macaw-3b",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use ... |
text2text-generation | transformers |
# macaw-answer-11b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a fl... | {"language": "en", "license": "apache-2.0", "widget": [{"text": "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?"}]} | allenai/macaw-answer-11b | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# macaw-answer-11b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a fl... | [
"# macaw-answer-11b",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of \ngeneral question answering, \nshowing robustness outside the domains it was trained on. It has been trained in \"multi-angle\" fashion, \nwhich means it ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# macaw-answer-11b",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready... |
text2text-generation | transformers |
# macaw-large
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexibl... | {"language": "en", "license": "apache-2.0", "widget": [{"text": "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?"}]} | allenai/macaw-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# macaw-large
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexibl... | [
"# macaw-large",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of \ngeneral question answering, \nshowing robustness outside the domains it was trained on. It has been trained in \"multi-angle\" fashion, \nwhich means it can h... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# macaw-large",
"## Model description\n\nMacaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>... |
question-answering | allennlp |
An augmented version of QANet that adds rudimentary numerical reasoning ability, trained on DROP (Dua et al., 2019), as published in the original DROP paper.
An augmented version of QANet model with some rudimentary numerical reasoning abilities. The main idea here is that instead of just predicting a passage span aft... | {"language": "en", "tags": ["allennlp", "question-answering"], "widget": [{"context": "A reusable launch system (RLS, or reusable launch vehicle, RLV) is a launch system which is capable of launching a payload into space more than once. This contrasts with expendable launch systems, where each launch vehicle is launche... | allenai/naqanet | null | [
"allennlp",
"tensorboard",
"question-answering",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#allennlp #tensorboard #question-answering #en #region-us
|
An augmented version of QANet that adds rudimentary numerical reasoning ability, trained on DROP (Dua et al., 2019), as published in the original DROP paper.
An augmented version of QANet model with some rudimentary numerical reasoning abilities. The main idea here is that instead of just predicting a passage span aft... | [] | [
"TAGS\n#allennlp #tensorboard #question-answering #en #region-us \n"
] |
null | transformers | # SciBERT
This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text.
The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.... | {"language": "en"} | allenai/scibert_scivocab_cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #endpoints_compatible #has_space #region-us
| # SciBERT
This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.
The training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just... | [
"# SciBERT\n\nThis is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.\n\nThe training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training,... | [
"TAGS\n#transformers #pytorch #jax #bert #en #endpoints_compatible #has_space #region-us \n",
"# SciBERT\n\nThis is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.\n\nThe training corpus was papers taken from Semantic Sc... |
null | transformers | # SciBERT
This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text.
The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.... | {"language": "en"} | allenai/scibert_scivocab_uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #endpoints_compatible #has_space #region-us
| # SciBERT
This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.
The training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just... | [
"# SciBERT\n\nThis is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.\n\nThe training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training,... | [
"TAGS\n#transformers #pytorch #jax #bert #en #endpoints_compatible #has_space #region-us \n",
"# SciBERT\n\nThis is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text.\n\nThe training corpus was papers taken from Semantic Sc... |
feature-extraction | transformers |
## SPECTER
SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fin... | {"language": "en", "license": "apache-2.0", "datasets": ["SciDocs"], "metrics": ["F1", "accuracy", "map", "ndcg"], "thumbnail": "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642... | allenai/specter | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"en",
"dataset:SciDocs",
"arxiv:2004.07180",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07180"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #en #dataset-SciDocs #arxiv-2004.07180 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## SPECTER
SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fin... | [
"## SPECTER\n\nSPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specif... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #en #dataset-SciDocs #arxiv-2004.07180 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## SPECTER\n\nSPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful sig... |
text2text-generation | transformers | Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-next-word-generator-qoogle"
tokenizer = T5Tokenizer.from_pretrained(model_name)
mod... | {"language": "en"} | allenai/t5-small-next-word-generator-qoogle | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:
which should result in the following:
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | SQuAD 1.1 question-answering based on T5-small.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-next-word-generator-qoogle"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_na... | {"language": "en"} | allenai/t5-small-squad11 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| SQuAD 1.1 question-answering based on T5-small.
Example use:
which should result in the following:
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-squad2-next-word-generator-squad"
tokenizer = T5Tokenizer.from_pretrained(model_nam... | {"language": "en"} | allenai/t5-small-squad2-next-word-generator-squad | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:
which should result in the following:
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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