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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:22:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT\_FINAL\_ctxSentence\_TRAIN\_essays\_TEST\_NULL\_second\_train\_set\_null\_False
==========================================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following res... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:24:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT\_FINAL\_ctxSentence\_TRAIN\_webDiscourse\_TEST\_NULL\_second\_train\_set\_null\_False
================================================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the f... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:27:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT\_FINAL\_ctxSentence\_TRAIN\_editorials\_TEST\_NULL\_second\_train\_set\_null\_False
==============================================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the follo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:30:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT\_FINAL\_ctxSentence\_TRAIN\_all\_TEST\_NULL\_second\_train\_set\_null\_False
=======================================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# paraphraser-spanish-t5-small
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax... | {"language": ["es"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["paws-x", "tapaco"], "model-index": [{"name": "paraphraser-spanish-t5-small", "results": []}]} | milyiyo/paraphraser-spanish-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"es",
"dataset:paws-x",
"dataset:tapaco",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T17:30:29+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #es #dataset-paws-x #dataset-tapaco #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# paraphraser-spanish-t5-small
This model is a fine-tuned version of flax-community/spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1079
- eval_runtime: 4.9573
- eval_samples_per_second: 365.924
- eval_steps_per_second: 36.713
- epoch: 0.83
- step: 43141... | [
"# paraphraser-spanish-t5-small\n\nThis model is a fine-tuned version of flax-community/spanish-t5-small on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1079\n- eval_runtime: 4.9573\n- eval_samples_per_second: 365.924\n- eval_steps_per_second: 36.713\n- epoch: 0.83\n- ... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #es #dataset-paws-x #dataset-tapaco #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# paraphraser-spanish-t5-small\n\nThis model is a fine-tuned version of flax-communi... |
null | null |
> From <https://github.com/bilibili/ailab/tree/main/Real-CUGAN>
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
C... | {"license": "mit", "title": "Real Cascade U-Nets for Anime Image Super Resolution", "emoji": "\ud83d\udc40", "colorFrom": "blue", "colorTo": "green", "sdk": "gradio", "app_file": "app.py", "pinned": true} | JacksonYan/Real-CUGAN | null | [
"license:mit",
"region:us"
] | null | 2022-05-02T17:31:09+00:00 | [] | [] | TAGS
#license-mit #region-us
|
> From <URL
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blu... | [
"# Configuration\n\n'title': _string_\nDisplay title for the Space\n\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_\nColor for Thumbnail gradient (red, yellow, green, ... | [
"TAGS\n#license-mit #region-us \n",
"# Configuration\n\n'title': _string_\nDisplay title for the Space\n\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_\nColor for Th... |
text-classification | transformers |
[Mona Allaert](https://github.com/MonaDT) •
[Leonardo Grotti](https://github.com/corvusMidnight) •
[Patrick Quick](https://github.com/patrickquick)
## Model description
BERTicelli is an English pre-trained BERT model obtained by fine-tuning the [English BERT base cased model](https://github.com/google-research/bert)... | {"language": ["en"], "license": "apache-2.0", "tags": ["BERTicelli", "text classification", "abusive language", "hate speech", "offensive language"], "datasets": ["OLID"], "widget": [{"text": "If Jamie Oliver fucks with my \u00a33 meal deals at Tesco I'll kill the cunt.", "example_title": "Example 1"}, {"text": "Keep u... | patrickquick/BERTicelli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"BERTicelli",
"text classification",
"abusive language",
"hate speech",
"offensive language",
"en",
"dataset:OLID",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:36:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #BERTicelli #text classification #abusive language #hate speech #offensive language #en #dataset-OLID #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Mona Allaert •
Leonardo Grotti •
Patrick Quick
## Model description
BERTicelli is an English pre-trained BERT model obtained by fine-tuning the English BERT base cased model with the training data from Offensive Language Identification Dataset (OLID).
This model was developed for the NLP Shared Task in the Digital ... | [
"## Model description\n\nBERTicelli is an English pre-trained BERT model obtained by fine-tuning the English BERT base cased model with the training data from Offensive Language Identification Dataset (OLID).\n\nThis model was developed for the NLP Shared Task in the Digital Text Analysis program at the University ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #BERTicelli #text classification #abusive language #hate speech #offensive language #en #dataset-OLID #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\nBERTicelli is an English pre-trained BERT model ob... |
text-generation | transformers | ## GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays
Generated texts are not very cohesive yet but I'm working on it. <br />
The Hosted inference API outputs (on the right) are too short for some reason. Trying to fix it. <br />
Use the code from the example below. The model takes "ZNOTITLE: your ... | {"language": "uk", "license": "afl-3.0"} | kyryl0s/gpt2-uk-zno-edition | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"uk",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T17:41:02+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #uk #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays
Generated texts are not very cohesive yet but I'm working on it. <br />
The Hosted inference API outputs (on the right) are too short for some reason. Trying to fix it. <br />
Use the code from the example below. The model takes "ZNOTITLE: your ... | [
"## GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays\n\nGenerated texts are not very cohesive yet but I'm working on it. <br />\nThe Hosted inference API outputs (on the right) are too short for some reason. Trying to fix it. <br />\nUse the code from the example below. The model takes \"ZNOTI... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #uk #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays\n\nGenerated texts are not very cohesive yet but I'm working on it. <br />\nTh... |
text-generation | null |
# Sheldon Cooper DialoGPT | {"tags": ["conversational"]} | atomsspawn/DialoGPT-small-sheldon | null | [
"conversational",
"region:us"
] | null | 2022-05-02T17:56:55+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Sheldon Cooper DialoGPT | [
"# Sheldon Cooper DialoGPT"
] | [
"TAGS\n#conversational #region-us \n",
"# Sheldon Cooper DialoGPT"
] |
null | keras |
# PerceptNet
PercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.
Link to the run: https://wandb.ai/jorgvt/PerceptNet/runs/28m2cnzj?workspace=user-jorgvt
# Usage
There are two alternatives to use the model: install our development repo and load the ... | {"license": "afl-3.0", "tags": ["feature_extraction", "image", "perceptual_metric"], "datasets": ["tid2008", "tid2013"], "metrics": ["pearsonr"], "model-index": [{"name": "PerceptNet", "results": [{"task": {"type": "feature_extraction", "name": "Perceptual Distance"}, "dataset": {"name": "tid2013", "type": "image"}, "m... | Jorgvt/PerceptNet | null | [
"keras",
"tf",
"feature_extraction",
"image",
"perceptual_metric",
"dataset:tid2008",
"dataset:tid2013",
"license:afl-3.0",
"model-index",
"region:us"
] | null | 2022-05-02T18:04:59+00:00 | [] | [] | TAGS
#keras #tf #feature_extraction #image #perceptual_metric #dataset-tid2008 #dataset-tid2013 #license-afl-3.0 #model-index #region-us
|
# PerceptNet
PercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.
Link to the run: URL
# Usage
There are two alternatives to use the model: install our development repo and load the pretrained weights manually, and load the model using 'from_pretrai... | [
"# PerceptNet\n\nPercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.\n\nLink to the run: URL",
"# Usage\n\nThere are two alternatives to use the model: install our development repo and load the pretrained weights manually, and load the model using... | [
"TAGS\n#keras #tf #feature_extraction #image #perceptual_metric #dataset-tid2008 #dataset-tid2013 #license-afl-3.0 #model-index #region-us \n",
"# PerceptNet\n\nPercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.\n\nLink to the run: URL",
"# Usa... |
question-answering | transformers | ---
license: apache-2.0
---
**Exact Match** 92.68
**F1** 86.5
Checkout [linkbert-base-finetuned-squad](https://huggingface.co/niklaspm/linkbert-base-finetuned-squad)
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827) | {"license": "apache-2.0"} | niklaspm/linkbert-large-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T18:06:30+00:00 | [
"2203.15827"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us
| ---
license: apache-2.0
---
Exact Match 92.68
F1 86.5
Checkout linkbert-base-finetuned-squad
See LinkBERT Paper | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Ukrainian STT model (with the Big Language Model formed on News Dataset)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https:/... | {"language": ["uk"], "license": "cc-by-nc-sa-4.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "uk"], "xdatasets": ["mozilla-foundation/common_voice_7_0"]} | Yehor/wav2vec2-xls-r-1b-uk-with-binary-news-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"uk",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T18:24:55+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| Ukrainian STT model (with the Big Language Model formed on News Dataset)
========================================================================
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model is a fine-tuned version of faceboo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/859423506592808961/VurGQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/angelinacho-stillconor-touchofray/1658260354212/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/angelinacho-stillconor-touchofray | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T18:59:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
nacho // 조혜미 & conor & ray
@angelinacho-stillconor-touchofray
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# doc2query/msmarco-14langs-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It was trained on all 14 languages of [mMARCO dataset](https://github.com/unicamp-dl/m... | {"language": ["en", "ar", "zh", "nl", "fr", "de", "hi", "in", "it", "ja", "pt", "ru", "es", "vi"], "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"], "widget": [{"text": "Python ist eine universelle, \u00fcblicherweise interpretierte, h\u00f6here Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, kn... | doc2query/msmarco-14langs-mt5-base-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"ar",
"zh",
"nl",
"fr",
"de",
"hi",
"in",
"it",
"ja",
"pt",
"ru",
"es",
"vi",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compat... | null | 2022-05-02T19:08:06+00:00 | [
"1904.08375",
"2104.08663",
"2112.07577"
] | [
"en",
"ar",
"zh",
"nl",
"fr",
"de",
"hi",
"in",
"it",
"ja",
"pt",
"ru",
"es",
"vi"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #en #ar #zh #nl #fr #de #hi #in #it #ja #pt #ru #es #vi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-14langs-mt5-base-v1
This is a doc2query model based on mT5 (also known as docT5query). It was trained on all 14 languages of mMARCO dataset, i.e. you can input a passage in any of the 14 languages, and it will generate a query in the same language.
It can be used for:
- Document expansion: You ge... | [
"# doc2query/msmarco-14langs-mt5-base-v1\n\nThis is a doc2query model based on mT5 (also known as docT5query). It was trained on all 14 languages of mMARCO dataset, i.e. you can input a passage in any of the 14 languages, and it will generate a query in the same language.\n\nIt can be used for:\n- Document expansio... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #en #ar #zh #nl #fr #de #hi #in #it #ja #pt #ru #es #vi #dataset-unicamp-dl/mmarco #arxiv-1904.08375 #arxiv-2104.08663 #arxiv-2112.07577 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/... |
text-classification | transformers | This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the Webis-Clickbait-17 dataset. It achieves the following results on the evaluation set:
Loss: 0.0261
The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Squared Error... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait1", "results": []}]} | caush/Clickbait4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T19:24:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the Webis-Clickbait-17 dataset. It achieves the following results on the evaluation set:
```
Loss: 0.0261
```
The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Square... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Willow DialoGPT Model | {"tags": ["conversational"]} | Willow/DialoGPT-medium-willow | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T21:14:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Willow DialoGPT Model | [
"# Willow DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Willow DialoGPT Model"
] |
text2text-generation | transformers | AfriMBART
### Citation Information
```
@inproceedings{adelani-etal-2022-thousand,
title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
author = "Adelani, David and
Alabi, Jesujoba and
Fan, Angela and
Kreutzer, Julia and
... | {"license": "afl-3.0"} | masakhane/afri-mbart50 | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T21:19:48+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| AfriMBART
| [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.
Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail.
We use the initialized weights from [BE... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "masked-token-prediction", "bertweet", "election2020", "politics"]} | kornosk/polibertweet-political-twitter-roberta-mlm | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"twitter",
"masked-token-prediction",
"bertweet",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T21:20:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #twitter #masked-token-prediction #bertweet #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.
Please see the official repository for more detail.
We use the initialized weights from BERTweet or 'vinai/bertweet-base'.
# Training Da... | [
"# Pre-trained BERT on Twitter US Political Election 2020\n\nPre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.\n\nPlease see the official repository for more detail.\n\nWe use the initialized weights from BERTweet or 'vinai/bertweet-base'.",
... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #twitter #masked-token-prediction #bertweet #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Political Election 2020\n\nPre-trained weights for PoliBERTweet: A Pre-trained La... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1520487753896665088/lO1P... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/usrsistakenhelp/1651530363067/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/usrsistakenhelp | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T21:25:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rosa - I miss tgamm
@usrsistakenhelp
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#dapprf | {"tags": ["conversational"]} | IsekaiMeta/dapprf | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T23:34:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#dapprf | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2 base model trained of 1.5K hours of Vietnamese speech
The base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 1.5K hours of reading and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should... | {"language": "vi", "license": "cc-by-sa-4.0", "tags": ["speech", "automatic-speech-recognition"]} | dragonSwing/viwav2vec2-base-1.5k | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"automatic-speech-recognition",
"vi",
"arxiv:2006.11477",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T23:56:33+00:00 | [
"2006.11477"
] | [
"vi"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #arxiv-2006.11477 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| # Wav2Vec2 base model trained of 1.5K hours of Vietnamese speech
The base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 1.5K hours of reading and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should... | [
"# Wav2Vec2 base model trained of 1.5K hours of Vietnamese speech\nThe base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 1.5K hours of reading and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #arxiv-2006.11477 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2 base model trained of 1.5K hours of Vietnamese speech\nThe base model is pre-trained on 16kHz sampled speech audio from Vietnames... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1487593747760103427/Khwk... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/alessandramakes/1651540241058/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/alessandramakes | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T00:09:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Alessandra (Taylor’s Version)
@alessandramakes
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2 base model trained of 3K hours of Vietnamese speech
The base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 3K hours of spontaneous, reading, and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this mo... | {"language": "vi", "license": "cc-by-sa-4.0", "tags": ["speech", "automatic-speech-recognition"]} | dragonSwing/viwav2vec2-base-3k | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"pretraining",
"speech",
"automatic-speech-recognition",
"vi",
"arxiv:2006.11477",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T00:16:58+00:00 | [
"2006.11477"
] | [
"vi"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #arxiv-2006.11477 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| # Wav2Vec2 base model trained of 3K hours of Vietnamese speech
The base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 3K hours of spontaneous, reading, and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this mo... | [
"# Wav2Vec2 base model trained of 3K hours of Vietnamese speech\nThe base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 3K hours of spontaneous, reading, and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that t... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #arxiv-2006.11477 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2 base model trained of 3K hours of Vietnamese speech\nThe base model is pre-trained on 16kHz sampled speech audio fro... |
text2text-generation | transformers | this is a Questions generating mode
| {} | pfactorial/checkpoint-22500-epoch-20 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T02:25:44+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| this is a Questions generating mode
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# snunlp/KR-SBERT-V40K-klueNLI-augSTS
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this m... | {"language": ["ko"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "\uadf8 \uc2dd\ub2f9\uc740 \ud30c\ub9ac\ub97c \ub0a0\ub9b0\ub2e4", "sentences": ["\uadf8 \uc2dd\ub2f9\uc5d0\ub294 \uc190\ub2d8\uc774 ... | snunlp/KR-SBERT-V40K-klueNLI-augSTS | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-03T02:34:16+00:00 | [] | [
"ko"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #has_space #region-us
| snunlp/KR-SBERT-V40K-klueNLI-augSTS
===================================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretraine... | {"license": "apache-2.0"} | laituan245/molt5-base-caption2smiles | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T03:08:16+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate a SMILES string from an input caption.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488171735174238211/4Y7Y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lonelythey18/1651554075248/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lonelythey18 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T03:59:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Cara
@lonelythey18
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1490143959540133891/C-DL... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/irenegellar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T04:26:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Random Small Streamer Chick
@irenegellar
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Traini... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-nostop
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2-nostop", "results": []}]} | DioLiu/distilbert-base-uncased-finetuned-sst2-nostop | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T05:31:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2-nostop
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0701
* Accuracy: 0.9888
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
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. -->
# door_inner_with_SA-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "door_inner_with_SA-bert-base-uncased", "results": []}]} | Davincilee/door_inner_with_SA-bert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T05:38:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| door\_inner\_with\_SA-bert-base-uncased
=======================================
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: 2.1513
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 12",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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\\_size: 6\n... |
text2text-generation | transformers | # it5-efficient-small-lfqa
It is a T5 ([IT5](https://huggingface.co/stefan-it/it5-efficient-small-el32)) efficient small model trained on a lfqa dataset.
<p align="center">
<img src="https://www.marcorossiartecontemporanea.net/wp-content/uploads/2021/04/MARCTM0413-9CFBn1gs-scaled.jpg" width="400"> </br>
Mir... | {"language": ["it"], "license": "apache-2.0", "datasets": ["custom"]} | efederici/it5-efficient-small-lfqa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"it",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T06:11:53+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #it #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # it5-efficient-small-lfqa
It is a T5 (IT5) efficient small model trained on a lfqa dataset.
<p align="center">
<img src="URL width="400"> </br>
Mirco Marchelli, Voce in capitolo, 2019
</p>
## Training Data
This model was trained on a lfqa dataset. The model provides long-form answers to open domain quest... | [
"# it5-efficient-small-lfqa\n\nIt is a T5 (IT5) efficient small model trained on a lfqa dataset. \n\n<p align=\"center\">\n <img src=\"URL width=\"400\"> </br>\n Mirco Marchelli, Voce in capitolo, 2019\n</p>",
"## Training Data\n\nThis model was trained on a lfqa dataset. The model provides long-form answe... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #it #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# it5-efficient-small-lfqa\n\nIt is a T5 (IT5) efficient small model trained on a lfqa dataset. \n\n<p align=\"center\">\n <im... |
text-classification | transformers |
# Discourse marker prediction / discourse connective prediction pretrained model
`roberta-base` pretrained on discourse marker prediction on the Discovery dataset with a validation accuracy of 30.93% (majority class is 0.57%)
https://github.com/sileod/discovery
https://huggingface.co/datasets/discovery
This model ... | {"language": ["en"], "license": "apache-2.0", "tags": ["discourse-marker-prediction", "discourse-connective-prediction", "discourse-connective", "discourse-marker", "discourse-relation-prediction", "pragmatics", "discourse"], "datasets": ["discovery"], "metrics": ["accuracy"], "widget": [{"text": "But no, Amazon sellin... | sileod/roberta-base-discourse-marker-prediction | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"discourse-marker-prediction",
"discourse-connective-prediction",
"discourse-connective",
"discourse-marker",
"discourse-relation-prediction",
"pragmatics",
"discourse",
"en",
"dataset:discovery",
"license:apache... | null | 2022-05-03T06:51:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #discourse-marker-prediction #discourse-connective-prediction #discourse-connective #discourse-marker #discourse-relation-prediction #pragmatics #discourse #en #dataset-discovery #license-apache-2.0 #autotrain_compatible #endpoints_compatible #regio... |
# Discourse marker prediction / discourse connective prediction pretrained model
'roberta-base' pretrained on discourse marker prediction on the Discovery dataset with a validation accuracy of 30.93% (majority class is 0.57%)
URL
URL
This model can also be used as a pretrained model for NLU, pragmatics and discour... | [
"# Discourse marker prediction / discourse connective prediction pretrained model\n\n'roberta-base' pretrained on discourse marker prediction on the Discovery dataset with a validation accuracy of 30.93% (majority class is 0.57%)\n\nURL\n\nURL\n\nThis model can also be used as a pretrained model for NLU, pragmatics... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #discourse-marker-prediction #discourse-connective-prediction #discourse-connective #discourse-marker #discourse-relation-prediction #pragmatics #discourse #en #dataset-discovery #license-apache-2.0 #autotrain_compatible #endpoints_compatible ... |
token-classification | transformers |
# Estonian NER model based on EstBERT
This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT) on the Estonian NER dataset. The model was trained by tartuNLP, the NLP research group at the institute of Computer Science at the University of Tartu.
It achieves the following re... | {"language": "et", "license": "cc-by-4.0", "widget": [{"text": "Eesti President on Alar Karis."}], "base_model": "tartuNLP/EstBERT"} | tartuNLP/EstBERT_NER_v2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"et",
"base_model:tartuNLP/EstBERT",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T06:54:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #et #base_model-tartuNLP/EstBERT #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Estonian NER model based on EstBERT
===================================
This model is a fine-tuned version of tartuNLP/EstBERT on the Estonian NER dataset. The model was trained by tartuNLP, the NLP research group at the institute of Computer Science at the University of Tartu.
It achieves the following results on ... | [
"### 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: 1024\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06\n* lr\\_scheduler\\_type: polynomial\n* max num\\_epochs: 150\n* e... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #et #base_model-tartuNLP/EstBERT #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | alla1101/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T06:54:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2236
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
# German Hotel Review Sentiment Classification
A model trained on German Hotel Reviews from Switzerland. The base model is the [bert-base-german-cased](https://huggingface.co/bert-base-german-cased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then t... | {"language": "de", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "Das Fr\u00fchst\u00fcck ist sehr gut, es gibt auch Laktosefreie Produkte.", "example_title": "Example 1"}, {"text": "Das Personal ist sehr kompetent und sehr freundlich.", "example_title": "Example 2"}, {"text": "Die Zimmer sind wie besc... | Tobias/bert-base-german-cased_German_Hotel_sentiment | null | [
"transformers",
"tf",
"bert",
"text-classification",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T08:21:49+00:00 | [] | [
"de"
] | TAGS
#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| German Hotel Review Sentiment Classification
============================================
A model trained on German Hotel Reviews from Switzerland. The base model is the bert-base-german-cased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then traine... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-moreShake
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2-moreShake", "results": []}]} | DioLiu/distilbert-base-uncased-finetuned-sst2-moreShake | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T08:29:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2-moreShake
================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1864
* Accuracy: 0.9739
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
text2text-generation | transformers |
The aim is to compress the mT5-base model to leave only the Ukrainian language and some basic English.
Reproduced the similar result (but with another language) from [this](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) medium article.
Results:
- 582M params... | {"language": ["uk", "en"], "tags": ["t5"]} | kravchenko/uk-mt5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"t5",
"uk",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T08:41:33+00:00 | [] | [
"uk",
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #t5 #uk #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
The aim is to compress the mT5-base model to leave only the Ukrainian language and some basic English.
Reproduced the similar result (but with another language) from this medium article.
Results:
- 582M params -> 244M params (58%)
- 250K tokens -> 30K tokens
- 2.2GB size model -> 0.95GB size model | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #t5 #uk #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | farjvr/DialoGPT-small-Mortyfar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T10:07:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
text-classification | transformers |
# German Hotel Review Sentiment Classification
A model trained on German Hotel Reviews from Switzerland. The base model is the [bert-base-german-cased](https://huggingface.co/bert-base-german-cased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then t... | {"language": "de", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "Das Fr\u00fchst\u00fcck ist sehr gut, es gibt auch Laktosefreie Produkte.", "example_title": "Example 1"}, {"text": "Das Personal ist sehr kompetent und sehr freundlich.", "example_title": "Example 2"}, {"text": "Die Zimmer sind wie besc... | Tobias/bert-base-german-cased_German_Hotel_classification | null | [
"transformers",
"tf",
"bert",
"text-classification",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T10:19:49+00:00 | [] | [
"de"
] | TAGS
#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| German Hotel Review Sentiment Classification
============================================
A model trained on German Hotel Reviews from Switzerland. The base model is the bert-base-german-cased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then traine... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | datauma/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T10:24:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0630
* Precision: 0.9313
* Recall: 0.9483
* F1: 0.9397
* Accuracy: 0.9856
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
# German Hotel Review Sentiment Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained fo... | {"language": "eng", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "The hotel is very nicely located", "example_title": "Example 1"}, {"text": "The reception staff were extremely helpful and very welcoming", "example_title": "Example 2"}, {"text": "There is no balcony in the rooms on the mountain side",... | Tobias/bert-base-uncased_English_Hotel_classification | null | [
"transformers",
"tf",
"bert",
"text-classification",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T10:33:24+00:00 | [] | [
"eng"
] | TAGS
#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| German Hotel Review Sentiment Classification
============================================
A model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained fo... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NL_BERT_michelin_finetuned
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/be... | {"tags": ["generated_from_trainer"], "datasets": "cmotions/NL_restaurant_reviews", "metrics": ["accuracy", "recall", "precision", "f1"], "widget": [{"text": "Wat een geweldige ervaring. Wij gebruikte de lunch bij de Librije. 10 gangen met in overleg hierbij gekozen wijnen. Alles klopt. De aandacht, de timing, prachtige... | wvangils/NL_BERT_michelin_finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:cmotions/NL_restaurant_reviews",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T10:39:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-cmotions/NL_restaurant_reviews #autotrain_compatible #endpoints_compatible #region-us
| NL\_BERT\_michelin\_finetuned
=============================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on a Dutch restaurant reviews dataset. Provide Dutch review text to the API on the right and receive a score that indicates whether this restaurant is eligible for a Michelin star ;)
It achie... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-cmotions/NL_restaurant_reviews #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-0... |
summarization | transformers | # it5-efficient-small-fanpage
It is a T5 ([IT5](https://huggingface.co/stefan-it/it5-efficient-small-el32)) efficient small model trained on [Fanpage](https://huggingface.co/datasets/ARTeLab/fanpage).
<p align="center">
<img src="https://compass-media.vogue.it/photos/61e574067f70d15c08312807/master/w_1600%2Cc_l... | {"language": ["it"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"]} | efederici/it5-efficient-small-fanpage | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T10:49:15+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # it5-efficient-small-fanpage
It is a T5 (IT5) efficient small model trained on Fanpage.
<p align="center">
<img src="URL width="400"> </br>
Davide Balliano, Untitled
</p>
## Usage and Performance
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.... | [
"# it5-efficient-small-fanpage\n\nIt is a T5 (IT5) efficient small model trained on Fanpage. \n\n<p align=\"center\">\n <img src=\"URL width=\"400\"> </br>\n Davide Balliano, Untitled \n</p>",
"## Usage and Performance",
"### Framework versions\n\n- Transformers 4.19.0.dev0\n- Pytorch 1.11.0+cu113\n- Dat... | [
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"# it5-efficient-small-fanpage\n\nIt is a T5 (IT5) efficient small model trained on Fanpage. ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# madatnlp/ke-t5-math-py
This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) o... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/ke-t5-math-py", "results": []}]} | madatnlp/ke-t5-math-py | null | [
"transformers",
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"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T10:50:49+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| madatnlp/ke-t5-math-py
======================
This model is a fine-tuned version of KETI-AIR/ke-t5-base-ko on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1203
* Validation Loss: 0.4336
* Epoch: 47
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
text-classification | transformers |
# English Hotel Review Sentiment Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained f... | {"language": "eng", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "The hotel is very nicely located", "example_title": "Example 1"}, {"text": "The reception staff were extremely helpful and very welcoming", "example_title": "Example 2"}, {"text": "There is no balcony in the rooms on the mountain side",... | Tobias/bert-base-uncased_English_Hotel_sentiment | null | [
"transformers",
"tf",
"bert",
"text-classification",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T10:58:18+00:00 | [] | [
"eng"
] | TAGS
#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# English Hotel Review Sentiment Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset. | [
"# English Hotel Review Sentiment Classification\nA model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset."
] | [
"TAGS\n#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# English Hotel Review Sentiment Classification\nA model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of th... |
automatic-speech-recognition | transformers | 2.5% WER on dev.clean: https://wandb.ai/sanchit-gandhi/flax-wav2vec2-2-bart-large-960h/runs/2lhazd5v | {} | sanchit-gandhi/flax-wav2vec2-2-bart-large-960h | null | [
"transformers",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T11:07:42+00:00 | [] | [] | TAGS
#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us
| 2.5% WER on URL: URL | [] | [
"TAGS\n#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1477268531561517057/Mhgi... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/joejoinerr/1655553718810/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/joejoinerr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T11:31:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Joe
@joejoinerr
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | null |
test | {"tags": ["image-classification", "pytorch"], "metrics": ["accuracy"], "model-index": [{"name": "llama-horse-zebra", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "accuracy", "value": 1.0, "name":... | osanseviero/test_metrics | null | [
"image-classification",
"pytorch",
"model-index",
"region:us"
] | null | 2022-05-03T11:45:48+00:00 | [] | [] | TAGS
#image-classification #pytorch #model-index #region-us
|
test | [] | [
"TAGS\n#image-classification #pytorch #model-index #region-us \n"
] |
null | transformers |
# ClimateErnieV2
ClimateErnieV2 is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 97.97% with test dataset "mwong/climate-evidence-related". Using pretrained ernie-v2-base model, the classifier head is trained on Climate Fever dataset. | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/climate-evidence-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe... | mwong/ernie-v2-climate-evidence-related | null | [
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"pytorch",
"bert",
"text classification",
"fact checking",
"en",
"dataset:mwong/climate-evidence-related",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T12:10:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text classification #fact checking #en #dataset-mwong/climate-evidence-related #license-mit #endpoints_compatible #region-us
|
# ClimateErnieV2
ClimateErnieV2 is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 97.97% with test dataset "mwong/climate-evidence-related". Using pretrained ernie-v2-base model, the classifier head is trained on Climate Fever dataset. | [
"# ClimateErnieV2\n\nClimateErnieV2 is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 97.97% with test dataset \"mwong/climate-evidence-related\". Using pretrained ernie-v2-base model, the classifier head is trained on Climate Fever dataset."
] | [
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fill-mask | transformers |
# Model MedRuRobertaLarge
# Model Description
This model is fine-tuned version of [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large).
The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_ru_roberta_... | {"language": ["ru"], "license": "apache-2.0"} | DmitryPogrebnoy/MedRuRobertaLarge | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T12:26:14+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model MedRuRobertaLarge
# Model Description
This model is fine-tuned version of ruRoberta-large.
The code for the fine-tuned process can be found here.
The model is fine-tuned on a specially collected dataset of over 30,000 medical anamneses in Russian.
The collected dataset can be found here.
This model was cr... | [
"# Model MedRuRobertaLarge",
"# Model Description\n\nThis model is fine-tuned version of ruRoberta-large. \nThe code for the fine-tuned process can be found here.\nThe model is fine-tuned on a specially collected dataset of over 30,000 medical anamneses in Russian. \nThe collected dataset can be found here.\n\nTh... | [
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text-generation | transformers |
# Harry Potter DialoGPT-small Model | {"tags": ["conversational"]} | InSaiyan/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T12:36:31+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT-small Model | [
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"# Harry Potter DialoGPT-small Model"
] |
text-generation | transformers |
#dapprf3 | {"tags": ["conversational"]} | IsekaiMeta/dapprf3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T12:55:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#dapprf3 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
[**GitHub Homepage**](https://github.com/wonrax/phobert-base-vietnamese-sentiment)
A model fine-tuned for sentiment analysis based on [vinai/phobert-base](https://huggingface.co/vinai/phobert-base).
Labels:
- NEG: Negative
- POS: Positive
- NEU: Neutral
Dataset: [30K e-commerce reviews](https://www.kaggle.com/datas... | {"language": ["vi"], "license": "mit", "tags": ["sentiment", "classification"], "widget": [{"text": "Kh\u00f4ng th\u1ec3 n\u00e0o \u0111\u1eb9p h\u01a1n"}, {"text": "Qu\u00e1 ph\u00ed ti\u1ec1n, m\u00e0 kh\u00f4ng \u0111\u1eb9p"}, {"text": "C\u00e1i n\u00e0y gi\u00e1 \u1ed5n kh\u00f4ng nh\u1ec9?"}]} | wonrax/phobert-base-vietnamese-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment",
"classification",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-03T13:03:13+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #roberta #text-classification #sentiment #classification #vi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
GitHub Homepage
A model fine-tuned for sentiment analysis based on vinai/phobert-base.
Labels:
- NEG: Negative
- POS: Positive
- NEU: Neutral
Dataset: 30K e-commerce reviews
## Usage
| [
"## Usage"
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] |
text-classification | transformers | ## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.
from transformers import AutoModelforSequenceClassification
model = AutoModelforSequenceClassification("henry931007/mfma")
```
@inproceedings{lee2022mfma,
title={Masked Summarization to Gene... | {} | henry931007/mfma | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T13:20:27+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.
from transformers import AutoModelforSequenceClassification
model = AutoModelforSequenceClassification("henry931007/mfma")
| [
"## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.\nfrom transformers import AutoModelforSequenceClassification\n\nmodel = AutoModelforSequenceClassification(\"henry931007/mfma\")"
] | [
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"## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.\nfrom transformers import AutoModelforSequenceClassification\n\nmodel = A... |
text2text-generation | transformers | ## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it.
```yaml
Experiment configurations
├── datasets ... | {} | pietrolesci/t5v1_1-base-mnli_snli_anli | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T13:33:00+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it.
| [
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] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | netoass/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T13:50:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1334
* F1: 0.8654
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers | ## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it
```yaml
Experiment configurations ... | {} | pietrolesci/t5v1_1-base-mnli | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T13:50:42+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it
| [
"## Overview\nT5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it"
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-base-finetuned-cola
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "data2vec-text-base-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}... | mrm8488/data2vec-text-base-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T13:51:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| data2vec-text-base-finetuned-cola
=================================
This model is a fine-tuned version of facebook/data2vec-text-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5254
* Matthews Correlation: 0.5215
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.160701759709141e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 30\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
token-classification | transformers |
## Model Specification
- This is a **baseline Twitter POS tagging model (with 95.21\% Accuracy)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the Tweebank-NER training data.
- **If you are looking for the SOTA Twitter POS tagger**, please go to this [HuggingFace hub link](https://huggingfac... | {"license": "cc-by-nc-4.0"} | TweebankNLP/bertweet-tb2-pos-tagging | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"arxiv:2201.07281",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T14:42:44+00:00 | [
"2201.07281"
] | [] | TAGS
#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- This is a baseline Twitter POS tagging model (with 95.21\% Accuracy) on Tweebank V2's NER benchmark (also called 'Tweebank-NER'), trained on the Tweebank-NER training data.
- If you are looking for the SOTA Twitter POS tagger, please go to this HuggingFace hub link.
- For more details about th... | [
"## Model Specification\n- This is a baseline Twitter POS tagging model (with 95.21\\% Accuracy) on Tweebank V2's NER benchmark (also called 'Tweebank-NER'), trained on the Tweebank-NER training data.\n- If you are looking for the SOTA Twitter POS tagger, please go to this HuggingFace hub link.\n- For more details ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- This is a baseline Twitter POS tagging model (with 95.21\\% Accuracy) on Tweebank V2's NER benchmark (also called 'Tweebank-NE... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebo... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-2", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T14:43:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-2
==================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2605
* Rouge1: 49.3582
* Rouge2: 29.7017
* Rougel: 30.6996
* Rougelsum: 46.3736
* Gen Len: 142... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mak109/distilgpt2-finetuned-lyrics
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "mak109/distilgpt2-finetuned-lyrics", "results": []}]} | mak109/distilgpt2-finetuned-lyrics | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T14:48:21+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mak109/distilgpt2-finetuned-lyrics
==================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.0226
* Validation Loss: 3.0275
* Epoch: 4
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'nam... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-base-finetuned-stsb
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "data2vec-text-base-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "stsb"}, "metrics"... | mrm8488/data2vec-text-base-finetuned-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T14:51:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| data2vec-text-base-finetuned-stsb
=================================
This model is a fine-tuned version of facebook/data2vec-text-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5530
* Pearson: 0.8732
* Spearmanr: 0.8717
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.725353773731373e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 5\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5"... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text2text-generation | transformers |
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrain... | {"license": "apache-2.0"} | laituan245/molt5-large-caption2smiles | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T14:58:10+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate a SMILES string from an input caption.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-4
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebo... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-4", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T15:09:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-4
==================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2573
* Rouge1: 49.0193
* Rouge2: 28.6311
* Rougel: 31.3363
* Rougelsum: 46.1408
* Gen Len: 142... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
token-classification | transformers |
## Model Specification
- This is the **state-of-the-art Twitter POS tagging model (with 95.38\% Accuracy)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the corpus combining both Tweebank-NER and English-EWT training data.
- For more details about the `TweebankNLP` project, please refer to t... | {"license": "cc-by-nc-4.0"} | TweebankNLP/bertweet-tb2_ewt-pos-tagging | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"arxiv:2201.07281",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T15:15:03+00:00 | [
"2201.07281"
] | [] | TAGS
#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- This is the state-of-the-art Twitter POS tagging model (with 95.38\% Accuracy) on Tweebank V2's NER benchmark (also called 'Tweebank-NER'), trained on the corpus combining both Tweebank-NER and English-EWT training data.
- For more details about the 'TweebankNLP' project, please refer to this ... | [
"## Model Specification\n- This is the state-of-the-art Twitter POS tagging model (with 95.38\\% Accuracy) on Tweebank V2's NER benchmark (also called 'Tweebank-NER'), trained on the corpus combining both Tweebank-NER and English-EWT training data.\n- For more details about the 'TweebankNLP' project, please refer t... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- This is the state-of-the-art Twitter POS tagging model (with 95.38\\% Accuracy) on Tweebank V2's NER benchmark (also called 'T... |
text2text-generation | transformers |
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrai... | {"license": "apache-2.0"} | laituan245/molt5-small-smiles2caption | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T15:29:59+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate an input caption from a SMILES string.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "model", "results": []}]} | ebonazza2910/model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T15:38:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| model
=====
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2220
* Wer: 0.1301
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | gbennett/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T15:38:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1334
* F1: 0.8654
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrain... | {"license": "apache-2.0"} | laituan245/molt5-large-smiles2caption | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T15:50:08+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate an input caption from a SMILES string.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji* | [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-base-finetuned-mrpc
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "data2vec-text-base-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "met... | mrm8488/data2vec-text-base-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T15:59:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| data2vec-text-base-finetuned-mrpc
=================================
This model is a fine-tuned version of facebook/data2vec-text-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4087
* Accuracy: 0.8627
* F1: 0.8993
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.486061628311107e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 16\n* seed: 19\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2"... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text2text-generation | transformers |
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrai... | {"license": "apache-2.0"} | laituan245/molt5-small-caption2smiles | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T16:03:20+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate a SMILES string from an input caption.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 1... | {"tags": ["generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["bleu"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/xtreme_s_xlsr_2_bart_covost2_fr_en_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:xtreme_s",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T16:12:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-xtreme_s #endpoints_compatible #region-us
|
This model was trained from scratch on the xtreme\_s dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7768
* Bleu: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-xtreme_s #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_bat... |
text2text-generation | transformers |
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrain... | {"license": "apache-2.0"} | laituan245/molt5-base-smiles2caption | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T16:12:55+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can be used to generate an input caption from a SMILES string.
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-8
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebo... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-8", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-8 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T16:16:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-8
==================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4519
* Rouge1: 49.5671
* Rouge2: 27.0118
* Rougel: 30.8538
* Rougelsum: 45.5503
* Gen Len: 141... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-large", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large')
```
## Paper
For more information, please take a look a... | {"license": "apache-2.0"} | laituan245/molt5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T16:20:12+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text2text-generation | transformers | ## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-base", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base')
```
## Paper
For more information, please take a look at o... | {"license": "apache-2.0"} | laituan245/molt5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T16:40:19+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
text2text-generation | transformers | ## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-small", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small')
```
## Paper
For more information, please take a look at... | {"license": "apache-2.0"} | laituan245/molt5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T16:45:46+00:00 | [
"2204.11817"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Example Usage
## Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
| [
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and Natural Language\n\nAuthors: *Carl Edwards\\*, Tuan Lai\\*, Kevin Ros, Garrett Honke, Heng Ji*"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.11817 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Example Usage",
"## Paper\n\nFor more information, please take a look at our paper.\n\nPaper: Translation between Molecules and... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad-pytorch
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad-pytorch", "results": []}]} | stevemobs/bert-finetuned-squad-pytorch | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T16:49:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad-pytorch
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# bert-finetuned-squad-pytorch\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad-pytorch\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore inf... |
summarization | transformers |
Citation
```
@article{DBLP:journals/corr/abs-2110-07166,
author = {Prafulla Kumar Choubey and
Jesse Vig and
Wenhao Liu and
Nazneen Fatema Rajani},
title = {MoFE: Mixture of Factual Experts for Controlling Hallucinations in
Abstractive Summarizatio... | {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "datasets": ["xsum"]} | praf-choub/bart-mofe-rl-xsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"arxiv:2110.07166",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T17:08:08+00:00 | [
"2110.07166"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-xsum #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
|
Citation
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-xsum #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-16
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-16", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T17:14:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-16
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8957
* Rouge1: 49.4097
* Rouge2: 29.3516
* Rougel: 31.527
* Rougelsum: 46.4241
* Gen Len: 14... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-base-finetuned-sst2
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "data2vec-text-base-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics":... | mrm8488/data2vec-text-base-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T17:18:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| data2vec-text-base-finetuned-sst2
=================================
This model is a fine-tuned version of facebook/data2vec-text-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3600
* Accuracy: 0.9232
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.1519343408010398e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text2text-generation | transformers | Placeholder for North-T5x | {"license": "cc-by-nc-4.0"} | pere/north | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T17:54:37+00:00 | [] | [] | TAGS
#transformers #jax #t5 #text2text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Placeholder for North-T5x | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-32
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-32", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T18:23:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-32
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2324
* Rouge1: 46.462
* Rouge2: 25.9506
* Rougel: 29.4584
* Rougelsum: 44.1863
* Gen Len: 14... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 32\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers | how to start prompt:
```
wordy:
```
example:
```
wordy: the ndp has turned into the country's darling of the young.
```
output:
```
the ndp is youth-driven.
```
OR
```
informal english:
```
example:
```
informal english: corn fields are all across illinois, visible once you leave chicago.
```
output:
```
corn fie... | {} | BigSalmon/ConciseAndFormal | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T18:34:00+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| how to start prompt:
example:
output:
OR
example:
output:
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | SebastianS/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T18:56:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0122
- eval_runtime: 27.9861
- eval_samples_per_second: 35.732
- eval_steps_per_second: 0.572
- epoch: 2.13
- step: 334
... | [
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0122\n- eval_runtime: 27.9861\n- eval_samples_per_second: 35.732\n- eval_steps_per_second: 0.572\n- epoch: 2.13\n-... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nI... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln41")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln41 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-03T18:57:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3`
This model was trained by simpleoier using chime6 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b757b89d45d5574cebf44e225cbe32e3e9e4f522
pip install -e .
cd egs2... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["chime6"]} | espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:chime6",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-03T19:52:40+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-chime6 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/simpleoier\_chime6\_asr\_transformer\_wavlm\_lr1e-3'
This model was trained by simpleoier using chime6 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue May 3 16:47:10 EDT 2022'
* python version: '3.9.12 (... | [
"### 'espnet/simpleoier\\_chime6\\_asr\\_transformer\\_wavlm\\_lr1e-3'\n\n\nThis model was trained by simpleoier using chime6 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue May 3 16:47:10 EDT 2022'\n* python version: '3.9.12 (main, Apr ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-chime6 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/simpleoier\\_chime6\\_asr\\_transformer\\_wavlm\\_lr1e-3'\n\n\nThis model was trained by simpleoier using chime6 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRES... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-64
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-roundup-64", "results": []}]} | theojolliffe/bart-large-cnn-finetuned-roundup-64 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T20:34:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-roundup-64
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4772
* Rouge1: 46.5444
* Rouge2: 27.4056
* Rougel: 29.6779
* Rougelsum: 44.0905
* Gen Len: 1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 64\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
null | null | XLM-R pre-pretrained with MLM on GLUECoS, CMU DoG and EN-HI codemixed corpus. Further pretrained with NLI on MNLI corpus and finetuned on GLUECoS | {} | shubhamphal/GLUECoS-XLM-R-with-MNLI-and-MLM-pretraining | null | [
"region:us"
] | null | 2022-05-03T20:55:48+00:00 | [] | [] | TAGS
#region-us
| XLM-R pre-pretrained with MLM on GLUECoS, CMU DoG and EN-HI codemixed corpus. Further pretrained with NLI on MNLI corpus and finetuned on GLUECoS | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | ## Swedish parliamentary motions party classifier
A model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a given text. | {} | Lauler/motions-classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T21:56:48+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## Swedish parliamentary motions party classifier
A model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a given text. | [
"## Swedish parliamentary motions party classifier\n\nA model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a given text."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## Swedish parliamentary motions party classifier\n\nA model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a g... |
text-classification | transformers | ## Sentiment classifier
Sentiment classifier for Swedish trained on ScandiSent dataset. | {} | Lauler/sentiment-classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:25:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## Sentiment classifier
Sentiment classifier for Swedish trained on ScandiSent dataset. | [
"## Sentiment classifier\n\nSentiment classifier for Swedish trained on ScandiSent dataset."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## Sentiment classifier\n\nSentiment classifier for Swedish trained on ScandiSent dataset."
] |
text-classification | transformers |
# albert-base-v2_pub_section
- original model file name: textclassifer_albert-base-v2_pubmed_full
- This is a fine-tuned checkpoint of `albert-base-v2` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_par... | {"language": ["en"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory ac... | ml4pubmed/albert-base-v2_pub_section | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:25:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
|
# albert-base-v2_pub_section
- original model file name: textclassifer_albert-base-v2_pubmed_full
- This is a fine-tuned checkpoint of 'albert-base-v2' for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_par... | [
"# albert-base-v2_pub_section\n- original model file name: textclassifer_albert-base-v2_pubmed_full\n- This is a fine-tuned checkpoint of 'albert-base-v2' for document section text classification\n- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,",
"## metadata",
"###... | [
"TAGS\n#transformers #pytorch #albert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert-base-v2_pub_section\n- original model file name: textclassifer_albert-base-v2_pubmed_full\n- This is a fine-tuned checkpoint of 'albert-base-v2' for document section... |
text-classification | transformers | # scibert-scivocab-cased_pub_section
- original model file name: textclassifer_scibert_scivocab_cased_pubmed_20k
- This is a fine-tuned checkpoint of `allenai/scibert_scivocab_cased` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
... | {"language": ["en"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory ac... | ml4pubmed/scibert-scivocab-cased_pub_section | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:27:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
| # scibert-scivocab-cased_pub_section
- original model file name: textclassifer_scibert_scivocab_cased_pubmed_20k
- This is a fine-tuned checkpoint of 'allenai/scibert_scivocab_cased' for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
... | [
"# scibert-scivocab-cased_pub_section\n- original model file name: textclassifer_scibert_scivocab_cased_pubmed_20k\n- This is a fine-tuned checkpoint of 'allenai/scibert_scivocab_cased' for document section text classification\n- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RES... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# scibert-scivocab-cased_pub_section\n- original model file name: textclassifer_scibert_scivocab_cased_pubmed_20k\n- This is a fine-tuned checkpoint of 'allenai/sc... |
text-classification | transformers |
# biobert-v1.1_pub_section
- original model file name: textclassifer_biobert-v1.1_pubmed_20k
- This is a fine-tuned checkpoint of `dmis-lab/biobert-v1.1` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_m... | {"language": ["en"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory ac... | ml4pubmed/biobert-v1.1_pub_section | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:35:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
|
# biobert-v1.1_pub_section
- original model file name: textclassifer_biobert-v1.1_pubmed_20k
- This is a fine-tuned checkpoint of 'dmis-lab/biobert-v1.1' for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_m... | [
"# biobert-v1.1_pub_section\n- original model file name: textclassifer_biobert-v1.1_pubmed_20k\n- This is a fine-tuned checkpoint of 'dmis-lab/biobert-v1.1' for document section text classification\n- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,",
"## metadata",
"#... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# biobert-v1.1_pub_section\n- original model file name: textclassifer_biobert-v1.1_pubmed_20k\n- This is a fine-tuned checkpoint of 'dmis-lab/biobert-v1.1' for doc... |
text-classification | transformers |
# scibert-scivocab-uncased_pub_section
- original model file name: textclassifer_scibert_scivocab_uncased_pubmed_full
- This is a fine-tuned checkpoint of `allenai/scibert_scivocab_uncased` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RES... | {"language": ["en"], "tags": ["text-classification", "document sections", "sentence classification", "document classification", "medical", "health", "biomedical"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "many pathogenic processes and diseases are the result... | ml4pubmed/scibert-scivocab-uncased_pub_section | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"document sections",
"sentence classification",
"document classification",
"medical",
"health",
"biomedical",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:44:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #document sections #sentence classification #document classification #medical #health #biomedical #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
|
# scibert-scivocab-uncased_pub_section
- original model file name: textclassifer_scibert_scivocab_uncased_pubmed_full
- This is a fine-tuned checkpoint of 'allenai/scibert_scivocab_uncased' for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RES... | [
"# scibert-scivocab-uncased_pub_section\n- original model file name: textclassifer_scibert_scivocab_uncased_pubmed_full\n- This is a fine-tuned checkpoint of 'allenai/scibert_scivocab_uncased' for document section text classification\n- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTI... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #document sections #sentence classification #document classification #medical #health #biomedical #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# scibert-scivocab-uncased_pub_section\n- original model file nam... |
text-classification | transformers |
# bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section
- original model file name: textclassifer_bluebert_pubmed_uncased_L-12_H-768_A-12_pubmed_20k
- This is a fine-tuned checkpoint of `bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12` for document section text classification
- possible document section classes are:BACKG... | {"language": ["en"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory ac... | ml4pubmed/bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T22:52:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
|
# bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section
- original model file name: textclassifer_bluebert_pubmed_uncased_L-12_H-768_A-12_pubmed_20k
- This is a fine-tuned checkpoint of 'bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12' for document section text classification
- possible document section classes are:BACKG... | [
"# bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section\n- original model file name: textclassifer_bluebert_pubmed_uncased_L-12_H-768_A-12_pubmed_20k\n- This is a fine-tuned checkpoint of 'bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12' for document section text classification\n- possible document section classes ar... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section\n- original model file name: textclassifer_bluebert_pubmed_uncased_L-12_H-768_A-12_pubmed_20k\n- This is a fine-tuned chec... |
text-classification | transformers |
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section
- original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_20k
- This is a fine-tuned checkpoint of `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext` for document section text classification
- poss... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "document sections", "sentence classification", "document classification", "medical", "health", "biomedical"], "datasets": ["pubmed", "ml4pubmed/pubmed-classification-20k"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": ... | ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"text-classification",
"document sections",
"sentence classification",
"document classification",
"medical",
"health",
"biomedical",
"en",
"dataset:pubmed",
"dataset:ml4pubmed/pubmed-classification-20k",
"license:apache-2.0",
... | null | 2022-05-03T23:14:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #safetensors #bert #text-classification #document sections #sentence classification #document classification #medical #health #biomedical #en #dataset-pubmed #dataset-ml4pubmed/pubmed-classification-20k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section
- original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_20k
- This is a fine-tuned checkpoint of 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' for document section text classification
- poss... | [
"# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section\n- original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_20k\n- This is a fine-tuned checkpoint of 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' for document section text classification\... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #bert #text-classification #document sections #sentence classification #document classification #medical #health #biomedical #en #dataset-pubmed #dataset-ml4pubmed/pubmed-classification-20k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n... |
text-classification | transformers |
# BioM-BERT-PubMed-PMC-Large_pub_section
- original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k
- This is a fine-tuned checkpoint of `sultan/BioM-BERT-PubMed-PMC-Large` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE,... | {"language": ["en"], "datasets": ["pubmed"], "metrics": ["f1"], "pipeline_tag": "text-classification", "widget": [{"text": "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory ac... | ml4pubmed/BioM-BERT-PubMed-PMC-Large_pub_section | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-03T23:23:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us
|
# BioM-BERT-PubMed-PMC-Large_pub_section
- original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k
- This is a fine-tuned checkpoint of 'sultan/BioM-BERT-PubMed-PMC-Large' for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE,... | [
"# BioM-BERT-PubMed-PMC-Large_pub_section\n- original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k\n- This is a fine-tuned checkpoint of 'sultan/BioM-BERT-PubMed-PMC-Large' for document section text classification\n- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJ... | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #en #dataset-pubmed #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioM-BERT-PubMed-PMC-Large_pub_section\n- original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k\n- This is a fine-tuned checkpoint of ... |
fill-mask | transformers |
RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization.
## Citation
```bibtex
@article{chambon_cook_langlotz_2022,
title={Improved fine-tuning of in-domain transformer model for inferring COVID-19 presence in multi-institutional radiology reports},
DOI={10.1007/s10278-022-0071... | {"language": ["en"], "license": "mit", "tags": ["fill-mask", "pytorch", "transformers", "bert", "biobert", "radbert", "language-model", "uncased", "radiology", "biomedical"], "datasets": ["wikipedia", "bookscorpus", "pubmed", "radreports"], "widget": [{"text": "low lung volumes, [MASK] pulmonary vascularity."}]} | StanfordAIMI/RadBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"biobert",
"radbert",
"language-model",
"uncased",
"radiology",
"biomedical",
"en",
"dataset:wikipedia",
"dataset:bookscorpus",
"dataset:pubmed",
"dataset:radreports",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"... | null | 2022-05-03T23:48:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #biobert #radbert #language-model #uncased #radiology #biomedical #en #dataset-wikipedia #dataset-bookscorpus #dataset-pubmed #dataset-radreports #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization.
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #biobert #radbert #language-model #uncased #radiology #biomedical #en #dataset-wikipedia #dataset-bookscorpus #dataset-pubmed #dataset-radreports #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Mandy Bot DialoGPT Model | {"tags": ["conversational"]} | emolyscheisse/DialoGPT-small-mandybot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-04T00:20:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mandy Bot DialoGPT Model | [
"# Mandy Bot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mandy Bot DialoGPT Model"
] |
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