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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).<br/>
This is an mt5-based Question Answering model for the Italian language. <br/>
Training is done on tr... | {"language": ["it"], "license": "apache-2.0", "tags": ["text2text_generation", "question_answering"], "widget": [{"text": "Quante torri ha Bologna? La torre degli Asinelli \u00e8 una delle cosiddette due torri di Bologna, simbolo della citt\u00e0, situate in piazza di porta Ravegnana, all'incrocio tra le antiche st... | bullmount/quanIta_t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text_generation",
"question_answering",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T15:37:10+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2text_generation #question_answering #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a fine-tuned version of gsarti/it5-base on Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB).<br/>
This is an mt5-based Question Answering model for the Italian language. <br/>
Training is done on translated subset of SQuAD 2.0 dataset (of about 100k questions).<br/>
Thus, this model not only at... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2text_generation #question_answering #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Killing Eve DialoGPT Model | {"tags": ["conversational"]} | xiaoGato/DialoGPT-small-villanelle | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T15:41:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Killing Eve DialoGPT Model | [
"# Killing Eve DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Killing Eve DialoGPT Model"
] |
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. -->
# data2vec-large-uk
This model is a fine-tuned version of [facebook/data2vec-audio-large-960h](https://huggingface.co/facebook/dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "data2vec-large-uk", "results": []}]} | robinhad/data2vec-large-uk | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-audio",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T16:22:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-audio #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# data2vec-large-uk
This model is a fine-tuned version of facebook/data2vec-audio-large-960h on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3472
- eval_wer: 0.3410
- eval_cer: 0.0832
- eval_runtime: 231.0008
- eval_samples_per_second: 25.108
- eval_steps_per_sec... | [
"# data2vec-large-uk\n\nThis model is a fine-tuned version of facebook/data2vec-audio-large-960h on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3472\n- eval_wer: 0.3410\n- eval_cer: 0.0832\n- eval_runtime: 231.0008\n- eval_samples_per_second: 25.108\n- eval_st... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-audio #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# data2vec-large-uk\n\nThis model is a fine-tuned version of facebook/data2vec-audio-large-960h on the common_voice data... |
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. -->
# reviews-generator
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "reviews-generator", "results": []}]} | maximedb/reviews-generator | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-25T16:30:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| reviews-generator
=================
This model is a fine-tuned version of facebook/bart-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3020
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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-finetuned2-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned2-imdb", "results": []}]} | Ghost1/distilbert-base-uncased-finetuned2-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T17:08:30+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-finetuned2-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:
* Loss: 2.4725
Model description
-----------------
More information needed
Intended uses &... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-960h-finetuned_common_voice
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-960h-finetuned_common_voice", "results": []}]} | obokkkk/wav2vec2-base-960h-finetuned_common_voice | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T17:27:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-960h-finetuned_common_voice
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# wav2vec2-base-960h-finetuned_common_voice\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-960h-finetuned_common_voice\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model ... |
text-classification | transformers |
This model is used to detect **abusive speech** in **Urdu**. It is finetuned on MuRIL model using Urdu abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more d... | {"language": "ur", "license": "afl-3.0"} | Hate-speech-CNERG/urdu-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ur",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T18:18:21+00:00 | [
"2204.12543"
] | [
"ur"
] | TAGS
#transformers #pytorch #bert #text-classification #ur #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used to detect abusive speech in Urdu. It is finetuned on MuRIL model using Urdu abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this url
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee a... | [
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages\". Accepted at ACM HT 2022.\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@ar... | [
"TAGS\n#transformers #pytorch #bert #text-classification #ur #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improve Low Resource Abusive L... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | maximedb/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T18:18:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | null |
## Overview
This model is based on [CLIP](https://openai.com/blog/clip) model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be def... | {"license": "mit"} | Haofeng/CLIP_animal_classification | null | [
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-25T19:36:44+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
## Overview
This model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be data... | [
"## Overview\n\nThis model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can b... | [
"TAGS\n#license-mit #has_space #region-us \n",
"## Overview\n\nThis model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Poss... |
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/1517600518167842816/OIgw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/unbridledbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T19:48:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
unbridled\_id\_bot
@unbridledbot
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"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-guarani-small-wb
This model is a fine-tuned version of [glob-asr/wav2vec2-large-xls-r-300m-guarani-sma... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-guarani-small-wb", "results": []}]} | jhonparra18/wav2vec2-large-xls-r-300m-guarani-small-wb | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T20:12:08+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-guarani-small-wb
==========================================
This model is a fine-tuned version of glob-asr/wav2vec2-large-xls-r-300m-guarani-small on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1622
* Wer: 0.2446
* Cer: 0.0368
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 0.0002\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "c... | nizamudma/t5-small-finetuned-cnn-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T20:21:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-2
========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6620
* Rouge1: 24.5085
* Rouge2: 11.7925
* Rougel: 20.2631
* Rougelsum: 23.1253
* Gen Len: 18.9996
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
text2text-generation | transformers |
# t5-eff-xl-8l-dutch-english-cased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5 eff** model has ... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-eff-xl-8l-dutch-english-cased | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T20:23:45+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl",
"en"
] | TAGS
#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-eff-xl-8l-dutch-english-cased
================================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4.
This t5 eff model has 1240M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective on ... | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #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-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": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "emotion", "type": "emotion... | jsoutherland/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T21:40:25+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1649
* Accuracy: 0.9325
* F1: 0.9327
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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/1513716426795855876/jWAK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gerardoalone/1650943909493/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gerardoalone | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T21:45:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
gay wedding technology
@gerardoalone
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 |
# RITA-S
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- | ... | {"language": "protein", "tags": ["protein"], "datasets": ["uniref-100"]} | lightonai/RITA_s | null | [
"transformers",
"pytorch",
"rita",
"text-generation",
"protein",
"custom_code",
"dataset:uniref-100",
"arxiv:2205.05789",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-04-25T21:55:50+00:00 | [
"2205.05789"
] | [
"protein"
] | TAGS
#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #has_space #region-us
| RITA-S
======
RITA is a family of autoregressive protein models, developed by a collaboration of Lighton, the OATML group at Oxford, and the Debbie Marks Lab at Harvard.
For full results see our preprint: URL
Usage
-----
Instantiate a model like so:
for generation we support pipelines:
How to cite
--------... | [] | [
"TAGS\n#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# RITA-M
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- | ... | {"language": "protein", "tags": ["protein"], "datasets": ["uniref-100"]} | lightonai/RITA_m | null | [
"transformers",
"pytorch",
"rita",
"text-generation",
"protein",
"custom_code",
"dataset:uniref-100",
"arxiv:2205.05789",
"autotrain_compatible",
"region:us"
] | null | 2022-04-25T22:08:53+00:00 | [
"2205.05789"
] | [
"protein"
] | TAGS
#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #region-us
| RITA-M
======
RITA is a family of autoregressive protein models, developed by a collaboration of Lighton, the OATML group at Oxford, and the Debbie Marks Lab at Harvard.
For full results see our preprint: URL
Usage
-----
Instantiate a model like so:
for generation we support pipelines:
How to cite
--------... | [] | [
"TAGS\n#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #region-us \n"
] |
text-generation | transformers |
# RITA-L
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- | ... | {"language": "protein", "tags": ["protein"], "datasets": ["uniref-100"]} | lightonai/RITA_l | null | [
"transformers",
"pytorch",
"rita",
"text-generation",
"protein",
"custom_code",
"dataset:uniref-100",
"arxiv:2205.05789",
"autotrain_compatible",
"region:us"
] | null | 2022-04-25T22:12:25+00:00 | [
"2205.05789"
] | [
"protein"
] | TAGS
#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #region-us
| RITA-L
======
RITA is a family of autoregressive protein models, developed by a collaboration of Lighton, the OATML group at Oxford, and the Debbie Marks Lab at Harvard.
For full results see our preprint: URL
Usage
-----
Instantiate a model like so:
for generation we support pipelines:
How to cite
--------... | [] | [
"TAGS\n#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #region-us \n"
] |
text-generation | transformers |
# RITA-XL
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- |... | {"language": "protein", "tags": ["protein"], "datasets": ["uniref-100"]} | lightonai/RITA_xl | null | [
"transformers",
"pytorch",
"rita",
"text-generation",
"protein",
"custom_code",
"dataset:uniref-100",
"arxiv:2205.05789",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-04-25T22:19:32+00:00 | [
"2205.05789"
] | [
"protein"
] | TAGS
#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #has_space #region-us
| RITA-XL
=======
RITA is a family of autoregressive protein models, developed by a collaboration of Lighton, the OATML group at Oxford, and the Debbie Marks Lab at Harvard.
For full results see our preprint: URL
Usage
-----
Instantiate a model like so:
for generation we support pipelines:
How to cite
------... | [] | [
"TAGS\n#transformers #pytorch #rita #text-generation #protein #custom_code #dataset-uniref-100 #arxiv-2205.05789 #autotrain_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1479992104306843648/e2XQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/femboi_canis/1650932783971/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/femboi_canis | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T23:25:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<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('URL
</div>
<div
style="display:no... | [
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on tweets from Ole Grim | Femboi | Cane | It/Its | Hy/Hym .\n\n| Data | Ole Grim | Femboi | Cane | It/Its | Hy/Hym |\n| -... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Tr... |
text-generation | transformers | # Family Guy DialoGPT Model v2
| {"tags": ["conversational"]} | Jonesy/DialoGPT-small_FG | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T00:49:19+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Family Guy DialoGPT Model v2
| [
"# Family Guy DialoGPT Model v2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Family Guy DialoGPT Model v2"
] |
summarization | transformers |
---
license: apache-2.0
tags:
- Summarization
metrics:
- rouge
model-index:
- name: best_model_test_0423_small
results: []
---
# best_model_test_0423_small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the... | {"language": ["zh"], "tags": ["summarization", "mT5"], "widget": [{"text": "\u5c08\u5bb6\u7a31\u7dad\u5eb7\u6851\u683c\u7814\u7a76\u6240(Wellcome Sanger Institute)\u7684\u4e0a\u8ff0\u7814\u7a76\u767c\u73fe\u300c\u4ee4\u4eba\u9707\u9a5a\u300d\u800c\u4e14\u300c\u767c\u4eba\u6df1\u7701\u300d\u3002\u57fa\u56e0\u8b8a\u7570\... | yihsuan/mt5_chinese_small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T01:03:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #zh #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
---
license: apache-2.0
tags:
* Summarization
metrics:
* rouge
model-index:
* name: best\_model\_test\_0423\_small
results: []
---
best\_model\_test\_0423\_small
==============================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Trainin... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #zh #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_b... |
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/1505089505757384712/M9eh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/spideythefifth/1650939169930/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/spideythefifth | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T01:09:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
️️️ Gandalf the Gay️️️️
@spideythefifth
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.
Tra... | [] | [
"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/1114620037300654082/KcWD... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lustfulliberal-pg13scottwatson/1661800282918/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lustfulliberal-pg13scottwatson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T01:13:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
The Loony Liberal - Tweets or GTFO & (18+ ONLY) - The Lustful Liberal - Scorny on Main
@lustfulliberal-pg13scottwatson
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.
!pipe... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | reader-generative bart_aug | {} | lhg/reader | null | [
"transformers",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T01:34:59+00:00 | [] | [] | TAGS
#transformers #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| reader-generative bart_aug | [] | [
"TAGS\n#transformers #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 786224257
- CO2 Emissions (in grams): 0.026027055434994496
## Validation Metrics
- Loss: 0.8348872065544128
- Accuracy: 0.7272727272727273
- Macro F1: 0.7230931630686932
- Micro F1: 0.7272727272727273
- Weighted F1: 0.72365994564... | {"language": "unk", "tags": "autotrain", "datasets": ["crcb/autotrain-data-isear_bert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.026027055434994496} | crcb/isear_bert | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:crcb/autotrain-data-isear_bert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T02:11:17+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-isear_bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 786224257
- CO2 Emissions (in grams): 0.026027055434994496
## Validation Metrics
- Loss: 0.8348872065544128
- Accuracy: 0.7272727272727273
- Macro F1: 0.7230931630686932
- Micro F1: 0.7272727272727273
- Weighted F1: 0.72365994564... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 786224257\n- CO2 Emissions (in grams): 0.026027055434994496",
"## Validation Metrics\n\n- Loss: 0.8348872065544128\n- Accuracy: 0.7272727272727273\n- Macro F1: 0.7230931630686932\n- Micro F1: 0.7272727272727273\n- Weighted... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-isear_bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 786224257\n- CO2 Emissions (... |
text2text-generation | transformers |
# Randeng-BART-139M
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
善于处理NLT任务，中文版的BART-base。
Good at solving NLT tasks, Chinese BART-base.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task ... | {"language": ["zh"], "license": "apache-2.0", "inference": true, "widget": [{"text": "\u6842\u6797\u5e02\u662f\u4e16\u754c\u95fb\u540d<mask> \uff0c\u5b83\u6709\u60a0\u4e45\u7684<mask>"}]} | IDEA-CCNL/Randeng-BART-139M | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"zh",
"arxiv:1910.13461",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T02:37:24+00:00 | [
"1910.13461",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-1910.13461 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Randeng-BART-139M
=================
* Github: Fengshenbang-LM
* Docs: Fengshenbang-Docs
简介 Brief Introduction
---------------------
善于处理NLT任务，中文版的BART-base。
Good at solving NLT tasks, Chinese BART-base.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
----------------------
参考论文：BART: Denoi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-1910.13461 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# KorSTS-dev
```
"eval_cosine_pearson": 0.8461074829101562
"eval_cosine_spearman": 0.8447369732456155
"eval_euclidean_pearson": 0.8401166200637817
"eval_euclidean_spearman": 0.8441547920405729
"eval_manhattan_pearson": 0.8404706120491028
"eval_manhattan_spearman": 0.8449217524976507
"eval_dot_pearson": 0.8457739353179... | {"language": ["ko"], "tags": ["simcse"]} | ddobokki/unsup-simcse-klue-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"simcse",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T03:40:53+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #roberta #simcse #ko #endpoints_compatible #region-us
|
# KorSTS-dev
# KorSTS-test
| [
"# KorSTS-dev",
"# KorSTS-test"
] | [
"TAGS\n#transformers #pytorch #roberta #simcse #ko #endpoints_compatible #region-us \n",
"# KorSTS-dev",
"# KorSTS-test"
] |
null | null | # Nissan Project
---
license: mit
---
## Overview
This model is based on [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model and [roberta-base-squad2 ](https://huggingface.co/deepset/roberta-base-squad2) model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to t... | {} | clevo570/Nissan_Project | null | [
"has_space",
"region:us"
] | null | 2022-04-26T03:47:11+00:00 | [] | [] | TAGS
#has_space #region-us
| # Nissan Project
---
license: mit
---
## Overview
This model is based on facebook/bart-large-mnli model and roberta-base-squad2 model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Questio... | [
"# Nissan Project\n\n---\nlicense: mit\n---",
"## Overview\n\nThis model is based on facebook/bart-large-mnli model and roberta-base-squad2 model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squa... | [
"TAGS\n#has_space #region-us \n",
"# Nissan Project\n\n---\nlicense: mit\n---",
"## Overview\n\nThis model is based on facebook/bart-large-mnli model and roberta-base-squad2 model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we... |
feature-extraction | transformers |
The SBERT model was trained on the dataset of UNO sustainable development goals. The total dataset size is 20000 records. 16000 were used for training and 4000 for evaluation.
The similarity between records was calculated based on the class similarity:
0 (case 1 - no common classes)
(number of common classes)/(numbe... | {} | Rodion/sbert_uno_sustainable_development_goals | null | [
"transformers",
"pytorch",
"mpnet",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T04:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #mpnet #feature-extraction #endpoints_compatible #region-us
|
The SBERT model was trained on the dataset of UNO sustainable development goals. The total dataset size is 20000 records. 16000 were used for training and 4000 for evaluation.
The similarity between records was calculated based on the class similarity:
0 (case 1 - no common classes)
(number of common classes)/(numbe... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#transformers #pytorch #mpnet #feature-extraction #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Tran... |
text2text-generation | transformers |
## AMRBART-large-finetuned-AMR3.0-AMRParsing
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a Smatch of 84.2 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https:/... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"AMRBART",
"en",
"arxiv:2203.07836",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T04:26:29+00:00 | [
"2203.07836"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #AMRBART #en #arxiv-2203.07836 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## AMRBART-large-finetuned-AMR3.0-AMRParsing
This model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a Smatch of 84.2 on the evaluation set: More details are introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022.
## Model description
Same w... | [
"## AMRBART-large-finetuned-AMR3.0-AMRParsing\n\nThis model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a Smatch of 84.2 on the evaluation set: More details are introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022.",
"## Model descript... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #AMRBART #en #arxiv-2203.07836 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## AMRBART-large-finetuned-AMR3.0-AMRParsing\n\nThis model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a Smatch of 84.2 ... |
text2text-generation | transformers |
## AMRBART-large-finetuned-AMR2.0-AMRParsing
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR2.0 dataset. It achieves a Smatch of 85.4 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https:/... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-large-finetuned-AMR2.0-AMRParsing | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"AMRBART",
"en",
"arxiv:2203.07836",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T04:27:20+00:00 | [
"2203.07836"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #AMRBART #en #arxiv-2203.07836 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## AMRBART-large-finetuned-AMR2.0-AMRParsing
This model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a Smatch of 85.4 on the evaluation set: More details are introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022.
## Model description
Same w... | [
"## AMRBART-large-finetuned-AMR2.0-AMRParsing\n\nThis model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a Smatch of 85.4 on the evaluation set: More details are introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022.",
"## Model descript... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #AMRBART #en #arxiv-2203.07836 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## AMRBART-large-finetuned-AMR2.0-AMRParsing\n\nThis model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a Smatch of 85.4 ... |
text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | deathknight67/DialoGPT-medium-joshua | null | [
"conversational",
"region:us"
] | null | 2022-04-26T04:38:47+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 786524275
- CO2 Emissions (in grams): 4.164757528958762
## Validation Metrics
- Loss: 0.16724252700805664
- Accuracy: 0.944234404536862
- Macro F1: 0.9437256923758108
- Micro F1: 0.9442344045368619
- Weighted F1: 0.94423683647498... | {"language": "unk", "tags": "autotrain", "datasets": ["crcb/autotrain-data-carer_5way"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.164757528958762} | crcb/carer_5way | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:crcb/autotrain-data-carer_5way",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T04:43:57+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-carer_5way #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 786524275
- CO2 Emissions (in grams): 4.164757528958762
## Validation Metrics
- Loss: 0.16724252700805664
- Accuracy: 0.944234404536862
- Macro F1: 0.9437256923758108
- Micro F1: 0.9442344045368619
- Weighted F1: 0.94423683647498... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 786524275\n- CO2 Emissions (in grams): 4.164757528958762",
"## Validation Metrics\n\n- Loss: 0.16724252700805664\n- Accuracy: 0.944234404536862\n- Macro F1: 0.9437256923758108\n- Micro F1: 0.9442344045368619\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-carer_5way #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 786524275\n- CO2 Emissions (... |
null | 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. -->
# nick_asr_COMBO_v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "nick_asr_COMBO_v2", "results": []}]} | ntoldalagi/nick_asr_COMBO_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T07:23:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us
| nick\_asr\_COMBO\_v2
====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4474
* Wer: 0.6535
* Cer: 0.2486
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\... |
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. -->
# test-mlm
This model is a fine-tuned version of [/root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt](https://huggingface.co... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test-mlm", "results": []}]} | ZZ99/deberta-v3-large-tapt | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T08:27:01+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# test-mlm
This model is a fine-tuned version of /root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3251
- Accuracy: 0.7285
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# test-mlm\n\nThis model is a fine-tuned version of /root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3251\n- Accuracy: 0.7285",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-mlm\n\nThis model is a fine-tuned version of /root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt on an unknown dataset.\nIt achieves the following results on the ev... |
object-detection | transformers |
# YOLOS (tiny-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](ht... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | hustvl/yolos-tiny | null | [
"transformers",
"pytorch",
"safetensors",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T08:28:47+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #safetensors #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# YOLOS (tiny-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.
Disclaimer: The team releasing YOLOS ... | [
"# YOLOS (tiny-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. \n\nDisclaimer: The team releasin... | [
"TAGS\n#transformers #pytorch #safetensors #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# YOLOS (tiny-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the pap... |
object-detection | transformers |
# YOLOS (base-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](ht... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | hustvl/yolos-base | null | [
"transformers",
"pytorch",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T08:30:39+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# YOLOS (base-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.
Disclaimer: The team releasing YOLOS ... | [
"# YOLOS (base-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. \n\nDisclaimer: The team releasin... | [
"TAGS\n#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# YOLOS (base-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only L... |
object-detection | transformers |
# YOLOS (small-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](h... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | hustvl/yolos-small | null | [
"transformers",
"pytorch",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T08:38:22+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# YOLOS (small-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.
Disclaimer: The team releasing YOLOS... | [
"# YOLOS (small-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. \n\nDisclaimer: The team releasi... | [
"TAGS\n#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# YOLOS (small-sized) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only ... |
object-detection | transformers |
# YOLOS (small-sized, fast model scaling) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | hustvl/yolos-small-dwr | null | [
"transformers",
"pytorch",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T09:15:57+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# YOLOS (small-sized, fast model scaling) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.
Disclaimer: The ... | [
"# YOLOS (small-sized, fast model scaling) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. \n\nDisclaim... | [
"TAGS\n#transformers #pytorch #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# YOLOS (small-sized, fast model scaling) model\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | sameearif88/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T09:31:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
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. -->
# english-filipino-wav2vec2-l-xls-r-test
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["filipino_voice"], "model-index": [{"name": "english-filipino-wav2vec2-l-xls-r-test", "results": []}]} | Khalsuu/english-filipino-wav2vec2-l-xls-r-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T09:37:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_voice #license-apache-2.0 #endpoints_compatible #region-us
| english-filipino-wav2vec2-l-xls-r-test
======================================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the filipino\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5795
* Wer: 0.3996
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_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: 0.001\n* ... |
object-detection | transformers |
# YOLOS (small-sized) model (300 pre-train epochs)
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | hustvl/yolos-small-300 | null | [
"transformers",
"pytorch",
"safetensors",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T09:49:56+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #safetensors #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# YOLOS (small-sized) model (300 pre-train epochs)
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.
Disclaimer: T... | [
"# YOLOS (small-sized) model (300 pre-train epochs)\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. \n\nDiscl... | [
"TAGS\n#transformers #pytorch #safetensors #yolos #object-detection #vision #dataset-coco #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# YOLOS (small-sized) model (300 pre-train epochs)\n\nYOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It w... |
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. -->
# model2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) o... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "model2", "results": []}]} | anablasi/financial_model | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:04:21+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# model2
This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
Th... | [
"# model2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"##... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# model2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"#... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | Sie-BERT/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:14:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | IneG/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:15:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | maretamasaeva/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:17:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | dimboump/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:22:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier_2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier_2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics"... | MonaA/glue_sst_classifier_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:24:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier\_2
========================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | Alassea/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:33:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": ... | Caroline-Vandyck/glue_sst_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T10:44:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier
=====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text2text-generation | transformers |
# Randeng-BART-139M-SUMMARY
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理摘要任务，在一个中文摘要数据集上微调后的，中文版的BART-base。
Good at solving text summarization tasks, after fine-tuning on a Chinese text summarization... | {"language": ["zh"], "license": "apache-2.0", "inference": true, "widget": [{"text": "summary: \u5728\u5317\u4eac\u51ac\u5965\u4f1a\u81ea\u7531\u5f0f\u6ed1\u96ea\u5973\u5b50\u5761\u9762\u969c\u788d\u6280\u5de7\u51b3\u8d5b\u4e2d\uff0c\u4e2d\u56fd\u9009\u624b\u8c37\u7231\u51cc\u593a\u5f97\u94f6\u724c\u3002\u795d\u8d3a\u8... | IDEA-CCNL/Randeng-BART-139M-SUMMARY | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T11:24:42+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Randeng-BART-139M-SUMMARY
=========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理摘要任务，在一个中文摘要数据集上微调后的，中文版的BART-base。
Good at solving text summarization tasks, after fine-tuning on a Chinese text summarization dataset, Chinese BART-base.
模型分... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-2209.02970 #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. -->
# glue_sst_classifier_
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "glue_sst_classifier_", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics":... | corvusMidnight/glue_sst_classifier_ | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T11:31:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue\_sst\_classifier\_
=======================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2359
* F1: 0.9034
* Accuracy: 0.9014
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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* lr\\_scheduler\\_warmup\\_rat... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# reviews-generator
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "reviews-generator", "results": []}]} | Caroline-Vandyck/reviews-generator | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T11:34:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| reviews-generator
=================
This model is a fine-tuned version of facebook/bart-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4990
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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. -->
# reviews-generator
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "reviews-generator", "results": []}]} | Alassea/reviews-generator | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T11:36:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reviews-generator
=================
This model is a fine-tuned version of facebook/bart-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4989
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-amazon_reviews_multi #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\\... |
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. -->
# electricidad-small-finetuned-restaurant-sentiment-analysis-usElectionTweets1Jul11Nov-spanish
This model is a fine-tuned version ... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electricidad-small-finetuned-restaurant-sentiment-analysis-usElectionTweets1Jul11Nov-spanish", "results": []}]} | dmjimenezbravo/electricidad-small-finetuned-restaurant-sentiment-analysis-usElectionTweets1Jul11Nov-spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T11:36:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electricidad-small-finetuned-restaurant-sentiment-analysis-usElectionTweets1Jul11Nov-spanish
============================================================================================
This model is a fine-tuned version of mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis on an unknown dataset.
It a... | [
"### 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: 60",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets where... | {"language": "en", "widget": [{"text": "I was just hired, yay!"}]} | manueltonneau/bert-twitter-en-is-hired | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T11:53:51+00:00 | [
"2203.09178"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize English tweets where... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: US \n- language: English\n- architecture: BERT base",
"## Model description \nThis model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize Engl... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: US \n- language: English\n- architect... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets ... | {"language": "en", "widget": [{"text": "Still unemployed..."}]} | manueltonneau/bert-twitter-en-is-unemployed | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T12:13:19+00:00 | [
"2203.09178"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize English tweets ... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: US \n- language: English\n- architecture: BERT base",
"## Model description \nThis model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: US \n- language: English\n- arch... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets cont... | {"language": "en", "widget": [{"text": "Software Engineer job at Amazon in Seattle, WA"}]} | manueltonneau/bert-twitter-en-job-offer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T12:23:03+00:00 | [
"2203.09178"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize English tweets cont... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: US \n- language: English\n- architecture: BERT base",
"## Model description \nThis model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize Eng... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: US \n- language: English\n- architec... |
text-generation | transformers |
# Glyph DialoGPT model | {"tags": ["conversational"]} | kyriinx/DialoGPT-small-glyph | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T12:31:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Glyph DialoGPT model | [
"# Glyph DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Glyph DialoGPT model"
] |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets where... | {"language": "en", "widget": [{"text": "Just lost my job..."}]} | manueltonneau/bert-twitter-en-lost-job | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T12:37:33+00:00 | [
"2203.09178"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize English tweets where... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: US \n- language: English\n- architecture: BERT base",
"## Model description \nThis model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize Engl... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: US \n- language: English\n- architect... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets whe... | {"language": "en", "widget": [{"text": "Job hunting!"}]} | manueltonneau/bert-twitter-en-job-search | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T12:46:49+00:00 | [
"2203.09178"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize English tweets whe... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: US \n- language: English\n- architecture: BERT base",
"## Model description \nThis model is a version of 'DeepPavlov/bert-base-cased-conversational' finetuned to recognize En... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: US \n- language: English\n- archite... |
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": []}]} | hbruce11216/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 | 2022-04-26T12:50:02+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:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
text2text-generation | transformers | # mt5-small-german-query-generation
## Model description:
This model was created with the purpose to generate possible queries for a german input article.
For this model, we finetuned a multilingual T5 model [mt5-small](https://huggingface.co/google/mt5-small) on the [MMARCO dataset](https://huggingface.co/datasets/... | {"language": ["de"], "license": "apache-2.0", "tags": ["pytorch", "query-generation"], "metrics": ["Rouge-Score"], "widget": [{"text": "Das Lama (Lama glama) ist eine Art der Kamele. Es ist in den s\u00fcdamerikanischen Anden verbreitet und eine vom Guanako abstammende Haustierform.", "example_title": "Article 1"}]} | ml6team/mt5-small-german-query-generation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"query-generation",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T12:51:02+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #query-generation #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-german-query-generation
=================================
Model description:
------------------
This model was created with the purpose to generate possible queries for a german input article.
For this model, we finetuned a multilingual T5 model mt5-small on the MMARCO dataset the machine translated ver... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #query-generation #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | flair |
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("Saisam/Inquirer_ner")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# pre... | {"language": "en", "license": "afl-3.0", "tags": ["flair"], "datasets": ["conll2003"]} | Saisam/Inquirer_ner | null | [
"flair",
"pytorch",
"en",
"dataset:conll2003",
"license:afl-3.0",
"region:us"
] | null | 2022-04-26T12:56:30+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #en #dataset-conll2003 #license-afl-3.0 #region-us
|
Requires: Flair ('pip install flair')
| [] | [
"TAGS\n#flair #pytorch #en #dataset-conll2003 #license-afl-3.0 #region-us \n"
] |
null | flair |
# Flair NER fine-tuned on Private Dataset
This is specifically Designed on locations. the tag is <unk>
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("Saisam/Inquirer_ner_loc")
# make example sentence
sentence = Sentence("George Washington... | {"language": "en", "tags": ["flair"], "datasets": ["conll2003"]} | Saisam/Inquirer_ner_loc | null | [
"flair",
"pytorch",
"en",
"dataset:conll2003",
"region:us"
] | null | 2022-04-26T13:09:35+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #en #dataset-conll2003 #region-us
|
# Flair NER fine-tuned on Private Dataset
This is specifically Designed on locations. the tag is <unk>
| [
"# Flair NER fine-tuned on Private Dataset\n\nThis is specifically Designed on locations. the tag is <unk>"
] | [
"TAGS\n#flair #pytorch #en #dataset-conll2003 #region-us \n",
"# Flair NER fine-tuned on Private Dataset\n\nThis is specifically Designed on locations. the tag is <unk>"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-opus_infopankki-en-zh
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_infopankk... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "model-index": [{"name": "t5-opus_infopankki-en-zh", "results": []}]} | 0x12/t5-opus_infopankki-en-zh | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T13:11:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-opus\_infopankki-en-zh
=========================
This model is a fine-tuned version of t5-small on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3548
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used d... |
fill-mask | transformers |
# Demo in a `fill-mask` task
```
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
model_name = 'sangjeedondrub/tibetan-roberta-base'
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
fill_mask_pipe = pipeline(
"fill-mask",
mo... | {"language": ["bo"], "license": "mit", "tags": ["tibetan", "pretrained language model", "roberta"], "widget": [{"text": "\u0f62\u0fab\u0f7c\u0f42\u0f66\u0f0b\u0f54\u0f60\u0f72\u0f0b <mask>"}, {"text": "\u0f46\u0f7c\u0f66\u0f0b\u0f40\u0fb1\u0f72\u0f0b<mask>\u0f0b\u0f56"}, {"text": "\u0f42\u0f44\u0f66\u0f0b\u0f62\u0f72\u... | sangjeedondrub/tibetan-roberta-base | null | [
"transformers",
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"roberta",
"fill-mask",
"tibetan",
"pretrained language model",
"bo",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T13:24:49+00:00 | [] | [
"bo"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tibetan #pretrained language model #bo #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Demo in a 'fill-mask' task
# Output
# About
This model is trained and released by Sangjee Dondrub [sangjeedondrub at live dot com], the mere purpose of conducting these experiments is to improve my familiarity with Transformers APIs. | [
"# Demo in a 'fill-mask' task",
"# Output",
"# About\n\nThis model is trained and released by Sangjee Dondrub [sangjeedondrub at live dot com], the mere purpose of conducting these experiments is to improve my familiarity with Transformers APIs."
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tibetan #pretrained language model #bo #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Demo in a 'fill-mask' task",
"# Output",
"# About\n\nThis model is trained and released by Sangjee Dondrub [sangjeedondrub at live do... |
text-classification | transformers | # cross-encoder-mmarco-german-distilbert-base
## Model description:
This model is a fine-tuned [cross-encoder](https://www.sbert.net/examples/training/cross-encoder/README.html) on the [MMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco) which is the machine translated version of the MS MARCO dataset.
A... | {"language": ["de"], "license": "apache-2.0", "tags": ["cross-encoder"], "metrics": ["Rouge-Score"], "widget": [{"text": "Was sind Lamas. Das Lama (Lama glama) ist eine Art der Kamele. Es ist in den s\u00fcdamerikanischen Anden verbreitet und eine vom Guanako abstammende Haustierform.", "example_title": "Example Query ... | ml6team/cross-encoder-mmarco-german-distilbert-base | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"cross-encoder",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T13:28:42+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #distilbert #text-classification #cross-encoder #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| cross-encoder-mmarco-german-distilbert-base
===========================================
Model description:
------------------
This model is a fine-tuned cross-encoder on the MMARCO dataset which is the machine translated version of the MS MARCO dataset.
As base model for the fine-tuning we use distilbert-base-multi... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #cross-encoder #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets where a u... | {"language": "es", "widget": [{"text": "Hoy me contrataron!"}]} | manueltonneau/bert-twitter-es-is-hired | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T13:37:34+00:00 | [
"2203.09178"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish tweets where a u... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: MX \n- language: Spanish\n- architecture: BERT base",
"## Model description \nThis model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: MX \n- language: Spanish\n- architect... |
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-OTTO
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-OTTO", "results": []}]} | hbruce11216/distilbert-base-uncased-finetuned-OTTO | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T13:54:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-OTTO
======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2745
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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\\_siz... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets where a u... | {"language": "es", "widget": [{"text": "Hoy perd\u00ed mi trabajo..."}]} | manueltonneau/bert-twitter-es-lost-job | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T14:00:35+00:00 | [
"2203.09178"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish tweets where a u... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: MX \n- language: Spanish\n- architecture: BERT base",
"## Model description \nThis model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: MX \n- language: Spanish\n- architect... |
text-generation | transformers |
# GPT-2 - reviewspanish
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of text data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an autom... | {"language": "es", "license": "mit", "tags": ["GPT-2", "Spanish", "review", "fake"], "datasets": ["amazon_reviews_multi"], "widget": [{"text": "Me ha gustado su", "example_title": "Positive review"}, {"text": "No quiero", "example_title": "Negative review"}]} | Amloii/gpt2-reviewspanish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"GPT-2",
"Spanish",
"review",
"fake",
"es",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T14:11:07+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #GPT-2 #Spanish #review #fake #es #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT-2 - reviewspanish
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of text data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an autom... | [
"# GPT-2 - reviewspanish",
"## Model description\n\n\nGPT-2 is a transformers model pretrained on a very large corpus of text data in a self-supervised fashion. This\nmeans it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots\nof publicly available data) ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #GPT-2 #Spanish #review #fake #es #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 - reviewspanish",
"## Model description\n\n\nGPT-2 is a transformers model pretrained ... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets wher... | {"language": "es", "widget": [{"text": "No tengo trabajo"}]} | manueltonneau/bert-twitter-es-is-unemployed | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T14:42:12+00:00 | [
"2203.09178"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish tweets wher... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: MX \n- language: Spanish\n- architecture: BERT base",
"## Model description \nThis model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spa... | [
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"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: MX \n- language: Spanish\n- arch... |
text-generation | transformers | # Family Guy DialoGPT Model v3 (Medium output)
| {"tags": ["conversational"]} | Jonesy/DialoGPT-medium_FG | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T15:19:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Family Guy DialoGPT Model v3 (Medium output)
| [
"# Family Guy DialoGPT Model v3 (Medium output)"
] | [
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"# Family Guy DialoGPT Model v3 (Medium output)"
] |
text-generation | transformers | > THIS MODEL IS IN PUBLIC BETA, PLEASE DO NOT EXPECT ANY FORM OF STABILITY IN ITS CURRENT STATE.
# Art Union server chatbot
Based on a DialoGPT-medium model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.
### Current issues
(Which hopefully will be fixed in future... | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["conversational"], "co2_eq_emissions": {"emissions": "660", "source": "mlco2.github.io", "training_type": "fine-tuning", "geographical_location": "West Java, Indonesia", "hardware_used": "1 Tesla P100"}, "widget": [{"text": "Hey kekbot! What's up?", "example_t... | spuun/kekbot-beta-3-medium | null | [
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"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T16:03:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| > THIS MODEL IS IN PUBLIC BETA, PLEASE DO NOT EXPECT ANY FORM OF STABILITY IN ITS CURRENT STATE.
# Art Union server chatbot
Based on a DialoGPT-medium model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.
### Current issues
(Which hopefully will be fixed in future... | [
"# Art Union server chatbot\n\nBased on a DialoGPT-medium model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.",
"### Current issues \n(Which hopefully will be fixed in future iterations) Include, but not limited to:\n- Limited turns, after ~17 turns output may ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Art Union server chatbot\n\nBased on a DialoGPT-medium model, fine-tuned to a select subset (65k<= messages) of Art Union's ge... |
text-generation | transformers |
# Connor DialoGPT Model | {"tags": ["conversational"]} | Lisia/DialoGPT-small-connor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T16:23:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Connor DialoGPT Model | [
"# Connor DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Connor DialoGPT Model"
] |
text-classification | transformers |
# bigbird-base-health-fact
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the health_fact dataset.
It achieves the following results on the VALIDATION set:
- Overall Accuracy: 0.8228995057660626
- Macro F1: 0.6979224830442152
- False Accura... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["health_fact"], "model-index": [{"name": "bigbird-base-health-fact", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "health_fact", "type": "health_fact", "split": "tes... | nbroad/bigbird-base-health-fact | null | [
"transformers",
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"big_bird",
"text-classification",
"generated_from_trainer",
"en",
"dataset:health_fact",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T16:55:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #big_bird #text-classification #generated_from_trainer #en #dataset-health_fact #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bigbird-base-health-fact
========================
This model is a fine-tuned version of google/bigbird-roberta-base on the health\_fact dataset.
It achieves the following results on the VALIDATION set:
* Overall Accuracy: 0.8228995057660626
* Macro F1: 0.6979224830442152
* False Accuracy: 0.8289473684210527
* Mixtu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 32\n* seed: 18\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #big_bird #text-classification #generated_from_trainer #en #dataset-health_fact #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\\_r... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-tapt-acl-arc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dat... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-tapt-acl-arc", "results": []}]} | Amrendra/roberta-tapt-acl-arc | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T17:09:56+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-tapt-acl-arc
====================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3472
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More... | [
"### 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: 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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | luccazen/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T17:16:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3026
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3026\n- Accuracy: 0.8667\n- F1: 0.8667",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
token-classification | transformers | Model for itemisation fo receipts | {} | renjithks/expense-ner | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T17:23:19+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Model for itemisation fo receipts | [] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Clickbait1
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0257
## Model descripti... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait1", "results": []}]} | caush/Clickbait1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T17:25:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Clickbait1
==========
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: 0.0257
Model description
-----------------
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attentio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188
## Validation Metrics
- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.564050972501... | {"language": "unk", "tags": "autotrain", "datasets": ["Rem59/autotrain-data-Test_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.0134443204822188} | Rem59/autotrain-Test_2-789524315 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:10:14+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188
## Validation Metrics
- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.564050972501... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 789524315\n- CO2 Emissions (in grams): 2.0134443204822188",
"## Validation Metrics\n\n- Loss: 0.8042349815368652\n- Accuracy: 0.6904761904761905\n- Macro F1: 0.27230046948356806\n- Micro F1: 0.6904761904761905\n- Weighted ... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Rem59/autotrain-data-Test_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 789524315\n- CO2 Emissions (i... |
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. -->
# Clickbait2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- L... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait2", "results": []}]} | caush/Clickbait2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:11:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Clickbait2
==========
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0212
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and ev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_... |
text-classification | transformers |
This model is used detecting **abusive speech** in **English**. It is finetuned on MuRIL model using English abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For m... | {"language": "en", "license": "afl-3.0"} | Hate-speech-CNERG/english-abusive-MuRIL | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
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"en",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T18:19:05+00:00 | [
"2204.12543"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This model is used detecting abusive speech in English. It is finetuned on MuRIL model using English abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this url
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Baner... | [
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages\". Accepted at ACM HT 2022.\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@ar... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improv... |
text-classification | transformers |
This model is used detecting **abusive speech** in **Code-Mixed Urdu**. It is finetuned on MuRIL model using code-mixed Urdu abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abu... | {"language": "ur-en", "license": "afl-3.0"} | Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:23:50+00:00 | [
"2204.12543"
] | [
"ur-en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used detecting abusive speech in Code-Mixed Urdu. It is finetuned on MuRIL model using code-mixed Urdu abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this url
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Da... | [
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages\". Accepted at ACM HT 2022.\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@ar... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nMithun Das, Somnath Banerjee and Animesh Mukherjee. \"Data Bootstrapping Approaches to Improve Low Resource ... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets containi... | {"language": "es", "widget": [{"text": "Difunde a contactos: #trabajo: Cajeros Zona Taxque\u00f1a- Turnos fijos. Oaxaca"}]} | manueltonneau/bert-twitter-es-job-offer | null | [
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"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:26:19+00:00 | [
"2203.09178"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish tweets containi... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: MX \n- language: Spanish\n- architecture: BERT base",
"## Model description \nThis model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish... | [
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"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: MX \n- language: Spanish\n- architec... |
automatic-speech-recognition | transformers |
# wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a collection of audio from [Dedi... | {"language": ["cak"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kaqchikel-with-bloom", "results": []}]} | sil-ai/w2v2-kaqchikel | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"cak",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:42:09+00:00 | [] | [
"cak"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #cak #license-mit #endpoints_compatible #region-us
| !sil-ai logo
wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
==============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on a collection of audio from Deditos videos in Kaqchikel provided by Viña Studios and Kaqchikel audio from audiobooks on Bloom Library.
It ac... | [
"### 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: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets where a... | {"language": "es", "widget": [{"text": "Busco trabajo"}]} | manueltonneau/bert-twitter-es-job-search | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T18:50:58+00:00 | [
"2203.09178"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: MX
- language: Spanish
- architecture: BERT base
## Model description
This model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanish tweets where a... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: MX \n- language: Spanish\n- architecture: BERT base",
"## Model description \nThis model is a version of 'dccuchile/bert-base-spanish-wwm-cased' finetuned to recognize Spanis... | [
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"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: MX \n- language: Spanish\n- archite... |
text-classification | transformers |
# longformer-base-health-fact2
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the health_fact dataset.
It achieves the following results on the VALIDATION set:
- Loss: 0.5858
- Micro F1: 0.8122
- Macro F1: 0.6830
- False F1: 0.7941
- Mixtur... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["health_fact"], "base_model": "allenai/longformer-base-4096", "model-index": [{"name": "longformer-base-health-fact2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "health_fact", "type": "he... | nbroad/longformer-base-health-fact | null | [
"transformers",
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"safetensors",
"longformer",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-26T19:30:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #longformer #text-classification #generated_from_trainer #en #dataset-health_fact #base_model-allenai/longformer-base-4096 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| longformer-base-health-fact2
============================
This model is a fine-tuned version of allenai/longformer-base-4096 on the health\_fact dataset.
It achieves the following results on the VALIDATION set:
* Loss: 0.5858
* Micro F1: 0.8122
* Macro F1: 0.6830
* False F1: 0.7941
* Mixture F1: 0.5015
* True F1: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 18\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distil-bert-finetuned-log-parser-winlogbeat
This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distil-bert-finetuned-log-parser-winlogbeat", "results": []}]} | Slavka/distil-bert-finetuned-log-parser-winlogbeat | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T20:43:08+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# distil-bert-finetuned-log-parser-winlogbeat
This model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# distil-bert-finetuned-log-parser-winlogbeat\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informatio... | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distil-bert-finetuned-log-parser-winlogbeat\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on an unknown dataset.\nIt achieves the ... |
text2text-generation | transformers |
In-BoXBART
=============
An instruction-based unified model for performing various biomedical tasks.
You may want to check out
* Our paper (NAACL 2022 Findings): [In-BoXBART: Get Instructions into Biomedical Multi-Task Learning](https://aclanthology.org/2022.findings-naacl.10/)
* GitHub: [Click Here](https://github... | {"language": ["en"], "license": "mit", "tags": ["biology", "medical", "language models", "BioNLP"]} | cogint/in-boxbart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"biology",
"medical",
"language models",
"BioNLP",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-26T21:58:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #biology #medical #language models #BioNLP #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
In-BoXBART
=============
An instruction-based unified model for performing various biomedical tasks.
You may want to check out
* Our paper (NAACL 2022 Findings): In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
* GitHub: Click Here
This work explores the impact of instructional prompts on biomedica... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #biology #medical #language models #BioNLP #en #license-mit #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. -->
# t5-small-finetuned-cnn-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "c... | nizamudma/t5-small-finetuned-cnn-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T22:06:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-3
========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6633
* Rouge1: 24.5495
* Rouge2: 11.8286
* Rougel: 20.2968
* Rougelsum: 23.1682
* Gen Len: 18.9993
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
null | null |
# 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, blue, in... | {"title": "Neural Style Transfer", "emoji": "\ud83d\udcca", "colorFrom": "green", "colorTo": "green", "sdk": "gradio", "app_file": "app.py", "pinned": false} | martinlmedina/tf_hub_Fast_Style_Transfer_for_Arbitrary_Styles | null | [
"region:us"
] | null | 2022-04-26T22:06:42+00:00 | [] | [] | TAGS
#region-us
|
# 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, blue, in... | [
"# 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,... | [
"TAGS\n#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 Thumbna... |
text-generation | transformers | # My Awesome Model | {"tags": ["conversational"]} | awvik360/DialoGPT-medium-plemons-04262022 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-26T22:58:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
null | null | ## Identificación de retinopatías
El Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en Coursera. Las notas medicas se en... | {} | ceciliamacias/prueba | null | [
"region:us"
] | null | 2022-04-27T00:32:02+00:00 | [] | [] | TAGS
#region-us
| ## Identificación de retinopatías
El Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en Coursera. Las notas medicas se en... | [
"## Identificación de retinopatías\n\nEl Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en Coursera. Las notas medica... | [
"TAGS\n#region-us \n",
"## Identificación de retinopatías\n\nEl Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en C... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ko-en-finetuned-en-to-ko
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ko-en-finetuned-en-to-ko", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "args... | obokkkk/opus-mt-ko-en-finetuned-en-to-ko | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T02:18:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ko-en-finetuned-en-to-ko
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ko-en on the kde4 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1606
* Bleu: 17.4129
* Gen Len: 10.8989
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-kde4 #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\\_... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | espejelomar/cool_synth_learner | null | [
"fastai",
"region:us"
] | null | 2022-04-27T02:37:39+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
text-generation | transformers | # DialoGPT-medium Model of Simpsons Episode s8e6 "Lisa On Ice"
| {"tags": ["conversational"]} | Jonesy/LisaOnIce | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T03:49:46+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT-medium Model of Simpsons Episode s8e6 "Lisa On Ice"
| [
"# DialoGPT-medium Model of Simpsons Episode s8e6 \"Lisa On Ice\""
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-medium Model of Simpsons Episode s8e6 \"Lisa On Ice\""
] |
text2text-generation | transformers | This is https://huggingface.co/sberbank-ai/ruT5-base model, fine-tuned to answer ChGK questions (Что? Где? Когда? https://db.chgk.info/)
Dataset: 75 000 questions from 2000-2019
Trained for 10 epochs | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "widget": [{"text": "\u041e\u0442\u0432\u0435\u0442\u044c\u0442\u0435 \u0434\u0432\u0443\u043c\u044f \u0441\u043b\u043e\u0432\u0430\u043c\u0438, \u0447\u0442\u043e \u043c\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u043b\u0438 \u043d\u0430 \u0418\u041a\u0421?"}],... | mary905el/ruT5_neuro_chgk_answering | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T04:06:23+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is URL model, fine-tuned to answer ChGK questions (Что? Где? Когда? URL
Dataset: 75 000 questions from 2000-2019
Trained for 10 epochs | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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