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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
# T5-deshuffle
Bag Of Words (BOW) is a simple and typical encoding for making statistical models discover patterns in language
However BOW is a lossy compression that eliminates a very important feature of text: order
This model is trained to learn the most probable order of an unordered token sequence,
using a su... | {"language": "en", "datasets": ["stas/c4-en-10k"], "widget": [{"text": " brown dog fox jumped lazy over quick the the "}]} | marksverdhei/t5-deshuffle | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:stas/c4-en-10k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T19:57:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-stas/c4-en-10k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-deshuffle
Bag Of Words (BOW) is a simple and typical encoding for making statistical models discover patterns in language
However BOW is a lossy compression that eliminates a very important feature of text: order
This model is trained to learn the most probable order of an unordered token sequence,
using a su... | [
"# T5-deshuffle \n\nBag Of Words (BOW) is a simple and typical encoding for making statistical models discover patterns in language\nHowever BOW is a lossy compression that eliminates a very important feature of text: order\n\nThis model is trained to learn the most probable order of an unordered token sequence,\n... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-stas/c4-en-10k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-deshuffle \n\nBag Of Words (BOW) is a simple and typical encoding for making statistical models discover patterns in langua... |
text-generation | transformers |
# Warden Ingo DialoGPT Model | {"tags": ["conversational"]} | Wavepaw/DialoGPT-medium-WardenIngo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T19:58:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Warden Ingo DialoGPT Model | [
"# Warden Ingo DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Warden Ingo DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | mrosinski/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T20:03:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2317
* Accuracy: 0.923
* F1: 0.9233
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-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. -->
# nbme-xlnet-large-cased
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "nbme-xlnet-large-cased", "results": []}]} | smeoni/nbme-xlnet-large-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T20:47:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nbme-xlnet-large-cased
======================
This model is a fine-tuned version of xlnet-large-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7151
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: 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=1... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n... |
image-classification | transformers | # Garbage Classification
## Overview
### Backgroud
Garbage classification refers to the separation of several types of different categories in accordance with the environmental impact of the use of the value of the composition of garbage components and the requirements of existing treatment methods.
The significance... | {} | yangy50/garbage-classification | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"arxiv:2010.11929",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-23T21:04:50+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #vit #image-classification #arxiv-2010.11929 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Garbage Classification
## Overview
### Backgroud
Garbage classification refers to the separation of several types of different categories in accordance with the environmental impact of the use of the value of the composition of garbage components and the requirements of existing treatment methods.
The significance... | [
"# Garbage Classification",
"## Overview",
"### Backgroud\nGarbage classification refers to the separation of several types of different categories in accordance with the environmental impact of the use of the value of the composition of garbage components and the requirements of existing treatment methods.\n\n... | [
"TAGS\n#transformers #pytorch #vit #image-classification #arxiv-2010.11929 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Garbage Classification",
"## Overview",
"### Backgroud\nGarbage classification refers to the separation of several types of different categories in accordance wi... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-contradiction
This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["snli"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-contradiction", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "snli", "ty... | domenicrosati/t5-small-finetuned-contradiction | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:snli",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T22:12:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-contradiction
================================
This model is a fine-tuned version of domenicrosati/t5-small-finetuned-contradiction on the snli dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0458
* Rouge1: 34.4237
* Rouge2: 14.5442
* Rougel: 32.5483
* Rougelsum: 32.57... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were us... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln40")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln40 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T22:24:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 778023879
- CO2 Emissions (in grams): 43.26533004662002
## Validation Metrics
- Loss: 5.475859779835446e-06
- Accuracy: 0.9999996519918594
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ c... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-ner"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 43.26533004662002} | Lucifermorningstar011/autotrain-ner-778023879 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-ner",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T22:29:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-ner #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 778023879
- CO2 Emissions (in grams): 43.26533004662002
## Validation Metrics
- Loss: 5.475859779835446e-06
- Accuracy: 0.9999996519918594
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Py... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 778023879\n- CO2 Emissions (in grams): 43.26533004662002",
"## Validation Metrics\n\n- Loss: 5.475859779835446e-06\n- Accuracy: 0.9999996519918594\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acc... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-ner #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 778023879\n- CO2 Emissio... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | PdF/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T22:41:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1348
* F1: 0.8658
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
This model is a fine-tuned version of [mrm8488/elec... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish", "results": []}]} | dmjimenezbravo/electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T23:08:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
============================================================================
This model is a fine-tuned version of mrm8488/electricidad-small-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* L... | [
"### 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 #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\\_size: 16\n* eval... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-contradiction-local-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["snli"], "model-index": [{"name": "t5-small-finetuned-contradiction-local-test", "results": []}]} | domenicrosati/t5-small-finetuned-contradiction-local-test | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T23:22:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-contradiction-local-test
===========================================
This model is a fine-tuned version of t5-small on the snli dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were us... |
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/1519998754425872385/VoEO... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/c8ohe2cqqe092cq/1674158643905/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/c8ohe2cqqe092cq | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T00:37:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
A kind Face
@c8ohe2cqqe092cq
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"
] |
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. -->
# nbme-deberta-v2-xlarge
This model is a fine-tuned version of [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/debe... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "nbme-deberta-v2-xlarge", "results": []}]} | smeoni/nbme-deberta-v2-xlarge | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T01:43:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nbme-deberta-v2-xlarge
======================
This model is a fine-tuned version of microsoft/deberta-v2-xlarge on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.5986
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #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: 4\n*... |
text2text-generation | transformers | # Yuyuan-Bart-139M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
生物医疗领域的生成语言模型，英文的BioBART-base。
A generative language model for biomedicine, BioBART-base in English.
## 模型分类 Model Taxonomy
| 需求 Demand |... | {"language": ["en"], "license": "apache-2.0", "tags": ["bart", "biobart", "biomedical"], "inference": true, "widget": [{"text": "Influenza is a <mask> disease."}, {"type": "text-generation"}]} | IDEA-CCNL/Yuyuan-Bart-139M | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"biobart",
"biomedical",
"en",
"arxiv:2204.03905",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T02:40:33+00:00 | [
"2204.03905",
"2209.02970"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Yuyuan-Bart-139M
================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
生物医疗领域的生成语言模型，英文的BioBART-base。
A generative language model for biomedicine, BioBART-base in English.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
--------------... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | naomiyjchen/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T03:08:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2208
* Accuracy: 0.9215
* F1: 0.9217
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational", "chatBot"]} | Akarsh3053/potter-chat-bot | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"chatBot",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T05:18:28+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #chatBot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #chatBot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
null | transformers | ## NeZha-Pytorch
pytorch版NEZHA，适配transformers
### 安装
> pip install git+https://github.com/yanqiangmiffy/Nezha-Pytorch.git
### 权重下载地址
https://github.com/lonePatient/NeZha_Chinese_PyTorch
### torch使用样例
```
import torch
from transformers import BertTokenizer
from nezha import NeZhaModel, NeZhaConfig
text = "今天[MASK]... | {} | quincyqiang/nezha-cn-base | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T06:53:43+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ## NeZha-Pytorch
pytorch版NEZHA，适配transformers
### 安装
> pip install git+URL
### 权重下载地址
URL
### torch使用样例
| [
"## NeZha-Pytorch\n\npytorch版NEZHA，适配transformers",
"### 安装\n> pip install git+URL",
"### 权重下载地址\n\nURL",
"### torch使用样例"
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"## NeZha-Pytorch\n\npytorch版NEZHA，适配transformers",
"### 安装\n> pip install git+URL",
"### 权重下载地址\n\nURL",
"### torch使用样例"
] |
text2text-generation | transformers |
# Yuyuan-Bart-400M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
生物医疗领域的生成语言模型，英文的BioBART-large。
A generative language model for biomedicine, BioBART-large in English.
## 模型分类 Model Taxonomy
| 需求 Deman... | {"language": ["en"], "license": "apache-2.0", "tags": ["bart", "biobart", "biomedical"], "inference": true, "widget": [{"text": "Influenza is a <mask> disease."}, {"types": "text-generation"}]} | IDEA-CCNL/Yuyuan-Bart-400M | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"biobart",
"biomedical",
"en",
"arxiv:2204.03905",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T08:48:05+00:00 | [
"2204.03905",
"2209.02970"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Yuyuan-Bart-400M
================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
生物医疗领域的生成语言模型，英文的BioBART-large。
A generative language model for biomedicine, BioBART-large in English.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
------------... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nbme-electra-large-generator
This model is a fine-tuned version of [google/electra-large-generator](https://huggingface.co/googl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "nbme-electra-large-generator", "results": []}]} | smeoni/nbme-electra-large-generator | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T09:34:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nbme-electra-large-generator
============================
This model is a fine-tuned version of google/electra-large-generator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0122
* Accuracy: 0.9977
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nbme-gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the follo... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "nbme-gpt2", "results": []}]} | smeoni/nbme-gpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T09:49:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nbme-gpt2
=========
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3684
* Accuracy: 0.5070
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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. -->
# wav2vec-speech-project
This model is a fine-tuned version of [kingabzpro/wav2vec2-large-xls-r-300m-Urdu](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-speech-project", "results": []}]} | M-junaid-A/wav2vec-speech-project | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T10:24:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec-speech-project
This model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# wav2vec-speech-project\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu 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",
"# wav2vec-speech-project\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu on the None dataset.",
"## Model descr... |
text-classification | transformers |
# Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
##... | {"license": "apache-2.0"} | jy46604790/Fake-News-Bert-Detect | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-24T10:25:53+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
##... | [
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).\n\nLABEL_0: Fake news\n\nLABEL_1: Real news",
"## Qu... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by sim... |
null | transformers |
# EleCzech-LC model
THe `eleczech-lc-small` is a monolingual small Electra language representation
model trained on lowercased Czech data (but with diacritics kept in place).
It is trained on the same data as the
[RobeCzech model](https://huggingface.co/ufal/robeczech-base).
| {"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "Electra", "\u00daFAL"]} | ufal/eleczech-lc-small | null | [
"transformers",
"pytorch",
"tf",
"electra",
"Czech",
"Electra",
"ÚFAL",
"cs",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T10:32:43+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #tf #electra #Czech #Electra #ÚFAL #cs #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# EleCzech-LC model
THe 'eleczech-lc-small' is a monolingual small Electra language representation
model trained on lowercased Czech data (but with diacritics kept in place).
It is trained on the same data as the
RobeCzech model.
| [
"# EleCzech-LC model\n\nTHe 'eleczech-lc-small' is a monolingual small Electra language representation\nmodel trained on lowercased Czech data (but with diacritics kept in place).\n\nIt is trained on the same data as the\nRobeCzech model."
] | [
"TAGS\n#transformers #pytorch #tf #electra #Czech #Electra #ÚFAL #cs #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# EleCzech-LC model\n\nTHe 'eleczech-lc-small' is a monolingual small Electra language representation\nmodel trained on lowercased Czech data (but with diacritics kept in place).\n\... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 778623908
- CO2 Emissions (in grams): 1.0568409665060605
## Validation Metrics
- Loss: 2.4664785861968994
- SacreBLEU: 1.6168
- Gen len: 17.645 | {"language": ["en", "hi"], "tags": ["autotrain", "translation"], "datasets": ["singhajeet13/autotrain-data-NMT"], "co2_eq_emissions": 1.0568409665060605} | abusiddik/autotrain-NMT-778623908 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"en",
"hi",
"dataset:singhajeet13/autotrain-data-NMT",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T10:37:30+00:00 | [] | [
"en",
"hi"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #hi #dataset-singhajeet13/autotrain-data-NMT #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 778623908
- CO2 Emissions (in grams): 1.0568409665060605
## Validation Metrics
- Loss: 2.4664785861968994
- SacreBLEU: 1.6168
- Gen len: 17.645 | [
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"## Validation Metrics\n\n- Loss: 2.4664785861968994\n- SacreBLEU: 1.6168\n- Gen len: 17.645"
] | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 77... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["few_nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification", "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Cla... | vikasaeta/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:few_nerd",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T11:23:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the few\_nerd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3136
* Precision: 0.6424
* Recall: 0.6854
* F1: 0.6632
* Accuracy: 0.9075
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #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* lea... |
image-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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | jemole/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T12:02:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0800
* Accuracy: 0.9759
Model description
----... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-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 small subset (52k< messages) of Art Union's general-chat channel.
### Current issues
(Which hopefully will be fixed in future iterations) In... | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["conversational"], "co2_eq_emissions": {"emissions": "370", "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-1-medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T12:31:50+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 small subset (52k< messages) of Art Union's general-chat channel.
### Current issues
(Which hopefully will be fixed in future iterations) In... | [
"# Art Union server chatbot\n\nBased on a DialoGPT-medium model, fine-tuned to a small subset (52k< messages) of Art Union's general-chat channel.",
"### Current issues \n(Which hopefully will be fixed in future iterations) Include, but not limited to:\n- Limited turns, after ~11 turns output may break for no ap... | [
"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 small subset (52k< messages) of Art Union's gene... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-ner-conll2003
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-ner-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"},... | kamalkraj/bert-base-cased-ner-conll2003 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T13:45:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-cased-ner-conll2003
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911
## Model description
More information needed
## Intended use... | [
"# bert-base-cased-ner-conll2003\n\nThis model is a fine-tuned version of bert-base-cased on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0355\n- Precision: 0.9438\n- Recall: 0.9525\n- F1: 0.9482\n- Accuracy: 0.9911",
"## Model description\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-cased-ner-conll2003\n\nThis model is a fine-tuned version of bert-... |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | MEDT/Chatbot_Medium | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T14:13:58+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #tf #jax #rust #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
"TAGS\n#transformers #pytorch #tf #jax #rust #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | mldev/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T14:36:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0595
* Precision: 0.9343
* Recall: 0.9504
* F1: 0.9423
* Accuracy: 0.9866
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
## DialoGPT_MWOZ_Idioms
This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system.
The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphemism, parallelism, pers... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["multi_woz_v22 and PIE-English idioms corpus"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "Does that mean Jane is off the hook?"}]} | tosin/dialogpt_mwoz_idioms | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T14:46:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DialoGPT\_MWOZ\_Idioms
----------------------
This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system.
The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphem... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\ntokenizer = AutoTokenizer.from\\_pretrained(\"tosin/dialogpt\\_mwoz\\_idioms\")\nmodel = AutoModelForCausalLM.from\\_pretrained(\"to... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import Au... |
text-generation | transformers |
## DialoGPT_AfriWOZ
This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Wolof language.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking.
The perplexity achieved ... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["multi_woz_v22 and AfriWOZ"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "dedet li rek la soxla. jerejef. ba benen yoon."}]} | tosin/dialogpt_afriwoz_wolof | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"arxiv:2204.08083",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T14:57:53+00:00 | [
"2204.08083"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #arxiv-2204.08083 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DialoGPT\_AfriWOZ
-----------------
This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Wolof language.
The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking.
The per... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\ntokenizer = AutoTokenizer.from\\_pretrained(\"tosin/dialogpt\\_afriwoz\\_wolof\")\nmodel = AutoModelForCausalLM.from\\_pretrained(\"... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #arxiv-2204.08083 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\n'''python\nfrom tran... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# adtabora/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "adtabora/distilgpt2-finetuned-wikitext2", "results": []}]} | adtabora/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T15:11:18+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| adtabora/distilgpt2-finetuned-wikitext2
=======================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.8581
* Validation Loss: 3.6738
* Epoch: 0
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'nam... |
null | transformers | ## Taglish-Electra
Our Taglish-Electra model was pretrained with two Filipino training datasets and one English dataset to increase improvement against Filipino text with English where speakers may code-switch between the two languages.
1) Openwebtext (English)
2) WikiText-TL-39 (Filipino)
3) [TLUnified Large Scale... | {} | charityking2358/taglish-electra | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T15:51:41+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ## Taglish-Electra
Our Taglish-Electra model was pretrained with two Filipino training datasets and one English dataset to increase improvement against Filipino text with English where speakers may code-switch between the two languages.
1) Openwebtext (English)
2) WikiText-TL-39 (Filipino)
3) TLUnified Large Scale ... | [
"## Taglish-Electra\n\nOur Taglish-Electra model was pretrained with two Filipino training datasets and one English dataset to increase improvement against Filipino text with English where speakers may code-switch between the two languages. \n\n1) Openwebtext (English) \n2) WikiText-TL-39 (Filipino)\n3) TLUnified L... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"## Taglish-Electra\n\nOur Taglish-Electra model was pretrained with two Filipino training datasets and one English dataset to increase improvement against Filipino text with English where speakers may code-switch between the two languages. \n\n1... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Zia/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T16:13:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1707
* Accuracy: 0.9365
* F1: 0.9367
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 #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_harpervalley_train_asr_hubert_raw_en_word`
This model was trained by YushiUeda using harpervalley recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0b1d15ebe0c36efcdf06d1b2e32361e3c8846cf6
pip install -e... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["harpervalley"]} | espnet/YushiUeda_harpervalley_train_asr_hubert_raw_en_word | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:harpervalley",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-24T17:04:08+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-harpervalley #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/YushiUeda\_harpervalley\_train\_asr\_hubert\_raw\_en\_word'
This model was trained by YushiUeda using harpervalley recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Apr 23 12:20:53 EDT 2022'
* python versi... | [
"### 'espnet/YushiUeda\\_harpervalley\\_train\\_asr\\_hubert\\_raw\\_en\\_word'\n\n\nThis model was trained by YushiUeda using harpervalley recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Apr 23 12:20:53 EDT 2022'\n* python version: '3.7... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-harpervalley #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_harpervalley\\_train\\_asr\\_hubert\\_raw\\_en\\_word'\n\n\nThis model was trained by YushiUeda using harpervalley recipe in espnet.",
"### Demo: How to use... |
fill-mask | transformers |
### Welcome to ParlBERT-German!
🏷 **Model description**:
**ParlBERT-German** is a domain-specific language model. The model was created through a process of continuous pre-training, which involved using a generic German language model (GermanBERT) as the foundation and further enhancing it with domain-specific know... | {"language": "de", "widget": [{"text": "Diese Themen geh\u00f6ren nicht ins [MASK]."}]} | chkla/parlbert-german-v1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T17:08:46+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #fill-mask #de #autotrain_compatible #endpoints_compatible #region-us
|
### Welcome to ParlBERT-German!
Model description:
ParlBERT-German is a domain-specific language model. The model was created through a process of continuous pre-training, which involved using a generic German language model (GermanBERT) as the foundation and further enhancing it with domain-specific knowledge. We ... | [
"### Welcome to ParlBERT-German!\n\n Model description:\n\nParlBERT-German is a domain-specific language model. The model was created through a process of continuous pre-training, which involved using a generic German language model (GermanBERT) as the foundation and further enhancing it with domain-specific knowle... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #de #autotrain_compatible #endpoints_compatible #region-us \n",
"### Welcome to ParlBERT-German!\n\n Model description:\n\nParlBERT-German is a domain-specific language model. The model was created through a process of continuous pre-training, which involved using a ... |
text-classification | transformers |
This model is used detecting **abusive speech** in **Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages**. The allInOne in the name refers to the Joint training/Cross-lingual training, where the model is trained... | {"language": ["bn", "hi", "hi-en", "ka-en", "ma-en", "mr", "ta-en", "ur", "ur-en", "en"], "license": "afl-3.0"} | Hate-speech-CNERG/indic-abusive-allInOne-MuRIL | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T17:40:29+00:00 | [
"2204.12543"
] | [
"bn",
"hi",
"hi-en",
"ka-en",
"ma-en",
"mr",
"ta-en",
"ur",
"ur-en",
"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 Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages. The allInOne in the name refers to the Joint training/Cross-lingual training, where the model is trained using 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 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]} | selen/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T17:55:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# distilbert-base-uncased-finetuned-cola\n\nThis model is a fine-tuned version of distilbert-base-uncased on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-cola\n\nThis model is a fine-tuned version of distilbert-base-uncased on the glue d... |
text-classification | transformers |
This model is used detecting **abusive speech** in **Bengali**. It is finetuned on MuRIL model using bengali 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 ... | {"language": ["bn"], "license": "afl-3.0"} | Hate-speech-CNERG/bengali-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"bn",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T17:59:53+00:00 | [
"2204.12543"
] | [
"bn"
] | TAGS
#transformers #pytorch #bert #text-classification #bn #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used detecting abusive speech in Bengali. It is finetuned on MuRIL model using bengali 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 Bane... | [
"### 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 #bn #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 is used detecting **abusive speech** in **Devanagari Hindi**. It is finetuned on MuRIL model using Hindi 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
#... | {"language": ["hi"], "license": "afl-3.0"} | Hate-speech-CNERG/hindi-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"hi",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-24T18:18:54+00:00 | [
"2204.12543"
] | [
"hi"
] | TAGS
#transformers #pytorch #bert #text-classification #hi #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This model is used detecting abusive speech in Devanagari Hindi. It is finetuned on MuRIL model using Hindi 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, Somna... | [
"### 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 #hi #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 Improve Low Resourc... |
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. -->
# v1_speech_processing_project_wav2vec2
This model is a fine-tuned version of [kingabzpro/wav2vec2-large-xls-r-300m-Urdu](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "v1_speech_processing_project_wav2vec2", "results": []}]} | Raffay/v1_speech_processing_project_wav2vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T18:40:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# v1_speech_processing_project_wav2vec2
This model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# v1_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# v1_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-large-xls-r-300m-Urdu on the None dataset.",
... |
unconditional-image-generation | null |
This model is based on [CycleGAN](https://arxiv.org/abs/1703.10593) architecture. It takes images, and generates a futuristic neon image for the image provided.Hope this model neonifies your images.
# Dataset
The model is trained on 256x256 high contrasted neon images as style images, and ... | {"license": "mit", "tags": ["gan", "unconditional image generation", "huggan", "style-transfer", "cyclegan", "Pytorch", "unconditional-image-generation"]} | huggan/NeonGAN | null | [
"gan",
"unconditional image generation",
"huggan",
"style-transfer",
"cyclegan",
"Pytorch",
"unconditional-image-generation",
"arxiv:1703.10593",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-24T18:46:41+00:00 | [
"1703.10593"
] | [] | TAGS
#gan #unconditional image generation #huggan #style-transfer #cyclegan #Pytorch #unconditional-image-generation #arxiv-1703.10593 #license-mit #has_space #region-us
|
This model is based on CycleGAN architecture. It takes images, and generates a futuristic neon image for the image provided.Hope this model neonifies your images.
!URL
# Dataset
The model is trained on 256x256 high contrasted neon images as style images, and normal images (including people,scenery etc.) as base imag... | [
"# Dataset\n\nThe model is trained on 256x256 high contrasted neon images as style images, and normal images (including people,scenery etc.) as base images.",
"#### Dataset - URL",
"# Model\n\nAll details regarding how to use the model, fine-tune it, are added to GitHub.",
"#### Github - URL",
"# Spaces Dem... | [
"TAGS\n#gan #unconditional image generation #huggan #style-transfer #cyclegan #Pytorch #unconditional-image-generation #arxiv-1703.10593 #license-mit #has_space #region-us \n",
"# Dataset\n\nThe model is trained on 256x256 high contrasted neon images as style images, and normal images (including people,scenery et... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-train
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achie... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-train", "results": []}]} | umarkhalid96/t5-small-train | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T18:52:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-train
==============
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2669
* Rouge1: 43.2372
* Rouge2: 21.6755
* Rougel: 38.1637
* Rougelsum: 38.5444
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-tf-db-resnet50 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T19:19:48+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": ["en", "fr", "multilingual"], "pipeline_tag": "image-to-text"} | Felix92/doctr-tf-crnn-vgg16-bn-french | null | [
"transformers",
"image-to-text",
"en",
"fr",
"multilingual",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-24T19:23:08+00:00 | [] | [
"en",
"fr",
"multilingual"
] | TAGS
#transformers #image-to-text #en #fr #multilingual #endpoints_compatible #has_space #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #image-to-text #en #fr #multilingual #endpoints_compatible #has_space #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-torch-db-mobilenet-v3-large | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T19:25:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": ["en", "fr", "multilingual"], "pipeline_tag": "image-to-text"} | Felix92/doctr-torch-crnn-mobilenet-v3-large-french | null | [
"transformers",
"pytorch",
"image-to-text",
"en",
"fr",
"multilingual",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-24T19:26:21+00:00 | [] | [
"en",
"fr",
"multilingual"
] | TAGS
#transformers #pytorch #image-to-text #en #fr #multilingual #endpoints_compatible #has_space #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #image-to-text #en #fr #multilingual #endpoints_compatible #has_space #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
token-classification | transformers |
## Model Details
This is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts
in a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, french and Spanish from a period ranging from 11... | {"language": ["lat", "fra", "spa", "multilingual"], "license": "cc-by-nc-4.0", "tags": ["text", "named entity recognition", "roberta", "historical languages", "precision", "recall"], "inference": {"parameters": {"aggregation_strategy": "simple"}}, "widget": [{"text": "In nomine sanct\u00e6 et individu\u00e6 Trinitatis.... | magistermilitum/roberta-multilingual-medieval-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"text",
"named entity recognition",
"roberta",
"historical languages",
"precision",
"recall",
"lat",
"fra",
"spa",
"multilingual",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible"... | null | 2022-04-24T19:34:45+00:00 | [] | [
"lat",
"fra",
"spa",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #text #named entity recognition #roberta #historical languages #precision #recall #lat #fra #spa #multilingual #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
## Model Details
This is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts
in a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, french and Spanish from a period ranging from 11... | [
"## Model Details\n\nThis is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts\nin a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, french and Spanish from a period ranging ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #text #named entity recognition #roberta #historical languages #precision #recall #lat #fra #spa #multilingual #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Details\n\nThis is a Fine-tuned v... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Nadhiya/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T19:58:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.6023
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text-classification | transformers | # roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
This is a enhanced version of 'Manauu17/roberta_sentiments_es' following the BERT's SOAT to acquire be... | {} | Manauu17/enhanced_roberta_sentiments_es | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T20:52:42+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
This is a enhanced version of 'Manauu17/roberta_sentiments_es' following the BERT's SOAT to acquire be... | [
"# roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences\n\nThis is a enhanced version of 'Manauu17/roberta_sentiments_es' following the BERT's SOAT to a... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently s... |
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": []}]} | akashsingh123/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 | 2022-04-24T22:04:48+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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Shashidhar/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-24T22:23:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1080
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-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: 1.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 7e-05\n* train\\_batch\\_s... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-train
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achie... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-train", "results": []}]} | Miranda/t5-small-train | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T22:34:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-train
==============
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2367
* Rouge1: 43.9525
* Rouge2: 22.3403
* Rougel: 38.7683
* Rougelsum: 39.2056
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.6e-05\n* train\\_batch\\_size: 9\n* eval\\_batch\\_size: 9\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
null | null |
# Overview
This model is based on [Tapas](https://huggingface.co/docs/transformers/model_doc/tapas), and I fine-tuned it on medical flowsheet dataset. This is for doctors and nurses who track patient's record by scrolling the mouse; instead, they can ask the question by natural language and the model will look throug... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"]} | yilye/tapas_medi_flowsheet | null | [
"tapas",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-04-24T23:35:00+00:00 | [] | [
"en"
] | TAGS
#tapas #en #license-apache-2.0 #region-us
|
# Overview
This model is based on Tapas, and I fine-tuned it on medical flowsheet dataset. This is for doctors and nurses who track patient's record by scrolling the mouse; instead, they can ask the question by natural language and the model will look through the table and find the answer for them.
| [
"# Overview\n\nThis model is based on Tapas, and I fine-tuned it on medical flowsheet dataset. This is for doctors and nurses who track patient's record by scrolling the mouse; instead, they can ask the question by natural language and the model will look through the table and find the answer for them."
] | [
"TAGS\n#tapas #en #license-apache-2.0 #region-us \n",
"# Overview\n\nThis model is based on Tapas, and I fine-tuned it on medical flowsheet dataset. This is for doctors and nurses who track patient's record by scrolling the mouse; instead, they can ask the question by natural language and the model will look thro... |
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/1624345765836619776/8X5U... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/plasma_node | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-24T23:39:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Plasmanode
@plasma\_node
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"
] |
fill-mask | transformers |
<b>(BERT base) Language modeling in Portuguese (C-corpus)</b>
<b>bert-base-cased-pt-c-corpus</b> is a Language Model in Portuguese that was finetuned on 24/04/2022 in Google Colab from the model BERTimbau base on the dataset C-Corpus, a dataset with user-generated texts.
| {"license": "apache-2.0"} | rosimeirecosta/bert-base-cased-pt-c-corpus | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T00:10:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
<b>(BERT base) Language modeling in Portuguese (C-corpus)</b>
<b>bert-base-cased-pt-c-corpus</b> is a Language Model in Portuguese that was finetuned on 24/04/2022 in Google Colab from the model BERTimbau base on the dataset C-Corpus, a dataset with user-generated texts.
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# mT5-m2o-chinese_simplified-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **chinese_simplified**, i.e. this model tries to **summarize text wri... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ... | csebuetnlp/mT5_m2o_chinese_simplified_crossSum | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
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"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",... | null | 2022-04-25T00:32:53+00:00 | [
"2112.08804"
] | [
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
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"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz... | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-gene... |
# mT5-m2o-chinese_simplified-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in chinese_simplified, i.e. this model tries to summarize text written in any language in Chinese(Simplified). For finetuning d... | [
"# mT5-m2o-chinese_simplified-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in chinese_simplified, i.e. this model tries to summarize text written in any language in Chinese(Simplified). For finetu... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #tex... |
text-generation | transformers |
# Tony Stark DialoGPT Model | {"tags": ["conversational"]} | aakhilv/tonystark | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T00:46:28+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT Model"
] |
text-classification | transformers | # HCAHPS survey comments multilabel classification
This model is a fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on a dataset of HCAHPS survey comments.
It achieves the following results on the evaluation set:
precision recal... | {} | joniponi/multilabel_inpatient_comments_16labels | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T02:22:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # HCAHPS survey comments multilabel classification
This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.
It achieves the following results on the evaluation set:
precision recall f1-score support
medical 0.87 0.81... | [
"# HCAHPS survey comments multilabel classification\n\nThis model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.\n\nIt achieves the following results on the evaluation set:\n \n precision recall f1-score support\n\n medical 0... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# HCAHPS survey comments multilabel classification\n\nThis model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.\n\nIt achieves the following resul... |
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-dbpedia
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-dbpedia", "results": []}]} | Danni/distilbert-base-uncased-finetuned-dbpedia | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T03:12:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-dbpedia
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4338
- eval_matthews_correlation: 0.7817
- eval_runtime: 1094.9103
- eval_samples_per_second: 60.777
- eval_steps_per... | [
"# distilbert-base-uncased-finetuned-dbpedia\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4338\n- eval_matthews_correlation: 0.7817\n- eval_runtime: 1094.9103\n- eval_samples_per_second: 60.777\n- eval... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-dbpedia\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt... |
text-classification | transformers |
This model is used detecting **abusive speech** in **Code-Mixed Hindi**. It is finetuned on MuRIL model using code-mixed hindi 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 :-> ... | {"language": "hi-en", "license": "afl-3.0"} | Hate-speech-CNERG/hindi-codemixed-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T04:12:26+00:00 | [
"2204.12543"
] | [
"hi-en"
] | TAGS
#transformers #pytorch #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 Hindi. It is finetuned on MuRIL model using code-mixed hindi 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... | [
"### 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 #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 Langu... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | ankitkupadhyay/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T04:59:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0255
* Rouge1: 17.469
* Rouge2: 8.5134
* Rougel: 17.1167
* Rougelsum: 17.2481
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
text-classification | transformers |
## deberta-v3-large-snli_mnli_fever_anli_R1_R2_R3-nli
#### Datasets
This model was trained on the snli-v1.0, multi-nli-1.0, nli-fever and anli-1.0-r1/anli-1.0-r2/anli-1.0-r3 datasets with the training weights of 1,1,1,10,20,10 respectively.
The training codes are mostly referenced from: https://github.com/facebookr... | {"language": ["en"], "tags": ["text-classification"], "datasets": ["snli-1.0", "multi-nli-1.0", "nli-fever", "anli-v1.0"], "metrics": ["accuracy"], "widget": [{"text": "British mountaineer Alison Hargreaves becomes the first woman to climb Mount Everest alone and without oxygen tanks. [SEP] Alison is a female."}, {"tex... | Joelzhang/deberta-v3-large-snli_mnli_fever_anli_R1_R2_R3-nli | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:snli-1.0",
"dataset:multi-nli-1.0",
"dataset:nli-fever",
"dataset:anli-v1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T05:58:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #dataset-snli-1.0 #dataset-multi-nli-1.0 #dataset-nli-fever #dataset-anli-v1.0 #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-snli\_mnli\_fever\_anli\_R1\_R2\_R3-nli
--------------------------------------------------------
#### Datasets
This model was trained on the snli-v1.0, multi-nli-1.0, nli-fever and anli-1.0-r1/anli-1.0-r2/anli-1.0-r3 datasets with the training weights of 1,1,1,10,20,10 respectively.
The training... | [
"#### Datasets\n\n\nThis model was trained on the snli-v1.0, multi-nli-1.0, nli-fever and anli-1.0-r1/anli-1.0-r2/anli-1.0-r3 datasets with the training weights of 1,1,1,10,20,10 respectively. \n\nThe training codes are mostly referenced from: URL",
"#### Hyperparameters\n\n\nlearning\\_rate: 1e-5 \n\nmax\\_len... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #en #dataset-snli-1.0 #dataset-multi-nli-1.0 #dataset-nli-fever #dataset-anli-v1.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Datasets\n\n\nThis model was trained on the snli-v1.0, multi-nli-1.0, nli-fever and anli-1.0-r1/anli-1... |
text-classification | transformers |
This model is used to detect **abusive speech** in **Code-Mixed Kannada**. It is finetuned on MuRIL model using Code-Mixed Kannada 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 ... | {"language": "ka-en", "license": "afl-3.0"} | Hate-speech-CNERG/kannada-codemixed-abusive-MuRIL | null | [
"transformers",
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"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T06:44:08+00:00 | [
"2204.12543"
] | [
"ka-en"
] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used to detect abusive speech in Code-Mixed Kannada. It is finetuned on MuRIL model using Code-Mixed Kannada 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
Mi... | [
"### 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 #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 Langu... |
text2text-generation | transformers |
## AMRBART (large-sized model)
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-large | null | [
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"en",
"arxiv:2203.07836",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:05:17+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-sized model)
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022 and first released in this repository.
## Model description
AMRBART follows... | [
"## AMRBART (large-sized model)\n\nAMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022 and first released in this repository.",
"## Model description\n\nAMRB... | [
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"## AMRBART (large-sized model)\n\nAMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduce... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 781623992
- CO2 Emissions (in grams): 3.9861818439722594
## Validation Metrics
- Loss: 0.1639203429222107
- Accuracy: 0.9389179755671903
- Macro F1: 0.9055551236566716
- Micro F1: 0.9389179755671903
- Weighted F1: 0.9379300009988... | {"language": "unk", "tags": "autotrain", "datasets": ["crcb/autotrain-data-carer_new"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.9861818439722594} | crcb/carer_new | null | [
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"unk",
"dataset:crcb/autotrain-data-carer_new",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:06:04+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 781623992
- CO2 Emissions (in grams): 3.9861818439722594
## Validation Metrics
- Loss: 0.1639203429222107
- Accuracy: 0.9389179755671903
- Macro F1: 0.9055551236566716
- Micro F1: 0.9389179755671903
- Weighted F1: 0.9379300009988... | [
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"## Validation Metrics\n\n- Loss: 0.1639203429222107\n- Accuracy: 0.9389179755671903\n- Macro F1: 0.9055551236566716\n- Micro F1: 0.9389179755671903\n- Weighted F... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 781623992\n- CO2 Emissions (i... |
text2text-generation | transformers |
## AMRBART-large-finetuned-AMR3.0-AMR2Text
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a sacre-bleu score of 45.0 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation]... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-large-finetuned-AMR3.0-AMR2Text | null | [
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"pytorch",
"bart",
"text2text-generation",
"AMRBART",
"en",
"arxiv:2203.07836",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:11:20+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-AMR3.0-AMR2Text
This model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a sacre-bleu score of 45.0 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 descriptio... | [
"## AMRBART-large-finetuned-AMR3.0-AMR2Text\n\nThis model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a sacre-bleu score of 45.0 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 ... | [
"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-AMR2Text\n\nThis model is a fine-tuned version of AMRBART-large on an AMR3.0 dataset. It achieves a sacre-bleu score ... |
text2text-generation | transformers |
## AMRBART-large-finetuned-AMR2.0-AMR2Text
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR2.0 dataset. It achieves a sacre-bleu score of 45.7 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation]... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text | null | [
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"pytorch",
"bart",
"text2text-generation",
"AMRBART",
"en",
"arxiv:2203.07836",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:12:08+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-AMR2Text
This model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a sacre-bleu score of 45.7 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 descriptio... | [
"## AMRBART-large-finetuned-AMR2.0-AMR2Text\n\nThis model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a sacre-bleu score of 45.7 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 ... | [
"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-AMR2Text\n\nThis model is a fine-tuned version of AMRBART-large on an AMR2.0 dataset. It achieves a sacre-bleu score ... |
text2text-generation | transformers |
## AMRBART (base-sized model)
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository]... | {"language": "en", "license": "mit", "tags": ["AMRBART"]} | xfbai/AMRBART-base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"AMRBART",
"en",
"arxiv:2203.07836",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:13:46+00:00 | [
"2203.07836"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #AMRBART #en #arxiv-2203.07836 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## AMRBART (base-sized model)
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022 and first released in this repository.
## Model description
AMRBART follows ... | [
"## AMRBART (base-sized model)\n\nAMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: Graph Pre-training for AMR Parsing and Generation by bai et al. in ACL 2022 and first released in this repository.",
"## Model description\n\nAMRBA... | [
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"## AMRBART (base-sized model)\n\nAMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It w... |
table-question-answering | transformers |
# TABLE QUESTION ANSWERING
## TAPAS model
TAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refu... | {"language": ["en"], "license": "apache-2.0", "tags": ["table-question-answering"], "datasets": ["sqa"], "metrics": ["bleu"]} | Meena/table-question-answering-tapas | null | [
"transformers",
"pytorch",
"tapas",
"table-question-answering",
"en",
"dataset:sqa",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-25T07:26:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tapas #table-question-answering #en #dataset-sqa #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# TABLE QUESTION ANSWERING
## TAPAS model
TAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refu... | [
"# TABLE QUESTION ANSWERING",
"## TAPAS model\nTAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entaile... | [
"TAGS\n#transformers #pytorch #tapas #table-question-answering #en #dataset-sqa #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# TABLE QUESTION ANSWERING",
"## TAPAS model\nTAPAS, the model learns an inner representation of the English language used in tables and associated texts, which c... |
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. -->
# wav2vec-speech-project
This model was trained from scratch on the None dataset.
## Model description
More information needed
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-speech-project", "results": []}]} | maryam359/wav2vec-speech-project | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T07:47:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec-speech-project
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hype... | [
"# wav2vec-speech-project\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyper... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec-speech-project\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limita... |
text-classification | transformers | xlm-RoBERTa-base fine-tuned for MuSeRC task. | {} | accelotron/xlm-roberta-finetune-muserc | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T08:46:39+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| xlm-RoBERTa-base fine-tuned for MuSeRC task. | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
This model is used to detect **abusive speech** in **Code-Mixed Malayalam**. It is finetuned on MuRIL model using Code-Mixed Malayalam 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
LABE... | {"language": "ma-en", "license": "afl-3.0"} | Hate-speech-CNERG/malayalam-codemixed-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-25T09:00:23+00:00 | [
"2204.12543"
] | [
"ma-en"
] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This model is used to detect abusive speech in Code-Mixed Malayalam. It is finetuned on MuRIL model using Code-Mixed Malayalam 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... | [
"### 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 #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 Improve Low Resource Ab... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | abhiGOAT/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T09:12:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
null | null | simcse test env | {} | alexhong/simcse | null | [
"region:us"
] | null | 2022-04-25T09:41:35+00:00 | [] | [] | TAGS
#region-us
| simcse test env | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
This model is used to detect **abusive speech** in **Marathi**. It is finetuned on MuRIL model using Marathi 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 ... | {"language": "mr", "license": "afl-3.0"} | Hate-speech-CNERG/marathi-codemixed-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"mr",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T09:50:34+00:00 | [
"2204.12543"
] | [
"mr"
] | TAGS
#transformers #pytorch #bert #text-classification #mr #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used to detect abusive speech in Marathi. It is finetuned on MuRIL model using Marathi 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 Bane... | [
"### 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 #mr #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-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 small subset (115k<= messages) of Art Union's general-chat channel.
### Current issues
(Which hopefully will be fixed in future iterations) ... | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["conversational"], "co2_eq_emissions": {"emissions": "940", "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-2-medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T09:51:20+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 small subset (115k<= messages) of Art Union's general-chat channel.
### Current issues
(Which hopefully will be fixed in future iterations) ... | [
"# Art Union server chatbot\n\nBased on a DialoGPT-medium model, fine-tuned to a small subset (115k<= messages) of Art Union's general-chat channel.",
"### Current issues \n(Which hopefully will be fixed in future iterations) Include, but not limited to:\n- Limited turns, after ~11 turns output may break for no ... | [
"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 small subset (115k<= messages) of Art Union's ge... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | MatthewAlanPow1/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T10:27:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7994
* Matthews Correlation: 0.5422
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text2text-generation | transformers | # Kingify 2Way
This is a custom AI model that translates modern English into 17th-century English or "King James" English.
## Details of the model
This model is a fine-tuned version of [google/t5-v1_1-large] on a dataset of a modern Bible translation with matching King James Bible verses.
## Intended uses & limitati... | {"language": "english", "tags": ["t5"], "widget": [{"text": "dekingify: ", "example_title": "Translate 17th-century English to modern English"}, {"text": "kingify: ", "example_title": "Translate modern English to 17th-century English"}]} | swcrazyfan/Kingify-2Way-T5-Large-v1_1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T10:37:20+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Kingify 2Way
This is a custom AI model that translates modern English into 17th-century English or "King James" English.
## Details of the model
This model is a fine-tuned version of [google/t5-v1_1-large] on a dataset of a modern Bible translation with matching King James Bible verses.
## Intended uses & limitati... | [
"# Kingify 2Way\nThis is a custom AI model that translates modern English into 17th-century English or \"King James\" English.",
"## Details of the model\n\nThis model is a fine-tuned version of [google/t5-v1_1-large] on a dataset of a modern Bible translation with matching King James Bible verses.",
"## Intend... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Kingify 2Way\nThis is a custom AI model that translates modern English into 17th-century English or \"King James\" English.",
"## Details of the model\n\nT... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner3", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "... | Ghost1/bert-finetuned-ner3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T10:51:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner3
===================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0603
* Precision: 0.9296
* Recall: 0.9490
* F1: 0.9392
* Accuracy: 0.9863
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
This model is used to detect **abusive speech** in **Code-Mixed Tamil**. It is finetuned on MuRIL model using Code-Mixed Tamil 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 :-> ... | {"language": "ta-en", "license": "afl-3.0"} | Hate-speech-CNERG/tamil-codemixed-abusive-MuRIL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T11:10:58+00:00 | [
"2204.12543"
] | [
"ta-en"
] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.12543 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used to detect abusive speech in Code-Mixed Tamil. It is finetuned on MuRIL model using Code-Mixed Tamil 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... | [
"### 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 #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 Langu... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | AlexTaylor/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T11:41:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2257
* Accuracy: 0.926
* F1: 0.9263
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new-test-model
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "new-test-model", "results": []}]} | kSaluja/new-test-model | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T11:49:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| new-test-model
==============
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0962
* Precision: 0.9704
* Recall: 0.9766
* F1: 0.9735
* Accuracy: 0.9791
Model description
-----------------
More information needed
... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
image-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. -->
# convnext-tiny-finetuned-beans
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "convnext-tiny-finetuned-beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "args": "default"},... | mrm8488/convnext-tiny-finetuned-beans | null | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T12:18:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| convnext-tiny-finetuned-beans
=============================
This model is a fine-tuned version of facebook/convnext-tiny-224 on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1255
* Accuracy: 0.9609
!pic
Model description
-----------------
More information needed
Inten... | [
"### 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: 7171\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #convnext #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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/1233003191538790400/3OxN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jstoone/1650893492572/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jstoone | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T12:30:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jakob Steinn
@jstoone
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"
] |
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-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T12:34:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 2.8797
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 2... | {"tags": ["generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["bleu"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/xtreme_s_xlsr_2_bart_covost2_fr_en | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:xtreme_s",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T12:35:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-xtreme_s #endpoints_compatible #region-us
|
This model was trained from scratch on the xtreme\_s dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1356
* Bleu: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-xtreme_s #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batc... |
question-answering | transformers |
# BERT Base Uncased Finetuned on NewsQA
The BERT (Base) model is finetuned on the NewsQA dataset using a modified version of the run_squad.py legacy script in Transformers. The script is provided in this repository. Examples with `noAnswer` and `badQuestion` are not included in the training process.
```bash
$ cd ~/... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["newsqa"], "metrics": ["f1", "exact_match"]} | mirbostani/bert-base-uncased-finetuned-newsqa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"dataset:newsqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T12:53:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-newsqa #license-apache-2.0 #endpoints_compatible #region-us
|
# BERT Base Uncased Finetuned on NewsQA
The BERT (Base) model is finetuned on the NewsQA dataset using a modified version of the run_squad.py legacy script in Transformers. The script is provided in this repository. Examples with 'noAnswer' and 'badQuestion' are not included in the training process.
Results:
T... | [
"# BERT Base Uncased Finetuned on NewsQA\n\nThe BERT (Base) model is finetuned on the NewsQA dataset using a modified version of the run_squad.py legacy script in Transformers. The script is provided in this repository. Examples with 'noAnswer' and 'badQuestion' are not included in the training process.\n\n\n\nResu... | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-newsqa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERT Base Uncased Finetuned on NewsQA\n\nThe BERT (Base) model is finetuned on the NewsQA dataset using a modified version of the run_squad.py legacy script in Transformers. The... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cakiki/distilbert-base-uncased-finetuned-tweet-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cakiki/distilbert-base-uncased-finetuned-tweet-sentiment", "results": []}]} | cakiki/distilbert-base-uncased-finetuned-tweet-sentiment | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T13:26:58+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| cakiki/distilbert-base-uncased-finetuned-tweet-sentiment
========================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1025
* Train Sparse Categorical Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new-test-model2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "new-test-model2", "results": []}]} | kSaluja/new-test-model2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T13:30:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| new-test-model2
===============
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1040
* Precision: 0.9722
* Recall: 0.9757
* F1: 0.9739
* Accuracy: 0.9808
Model description
-----------------
More information needed... | [
"### 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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_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"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | BSlinky/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T13:51:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
text2text-generation | transformers |
# t5-sl-small
t5-sl-small model is a Slovene T5 model. It has 8 encoder and 8 decoder layers, in total about 60 million parameters.
It was trained for 5 epochs on the following corpora:
## Corpora
The following corpora were used for training the model:
* Gigafida 2.0
* Kas 1.0
* Janes 1.0 (only Janes-news, Janes-foru... | {"language": ["sl"], "license": "cc-by-sa-4.0"} | cjvt/t5-sl-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"sl",
"arxiv:2207.13988",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-25T13:56:41+00:00 | [
"2207.13988"
] | [
"sl"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #sl #arxiv-2207.13988 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# t5-sl-small
t5-sl-small model is a Slovene T5 model. It has 8 encoder and 8 decoder layers, in total about 60 million parameters.
It was trained for 5 epochs on the following corpora:
## Corpora
The following corpora were used for training the model:
* Gigafida 2.0
* Kas 1.0
* Janes 1.0 (only Janes-news, Janes-foru... | [
"# t5-sl-small\nt5-sl-small model is a Slovene T5 model. It has 8 encoder and 8 decoder layers, in total about 60 million parameters.\nIt was trained for 5 epochs on the following corpora:",
"## Corpora\nThe following corpora were used for training the model:\n* Gigafida 2.0\n* Kas 1.0\n* Janes 1.0 (only Janes-ne... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #sl #arxiv-2207.13988 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# t5-sl-small\nt5-sl-small model is a Slovene T5 model. It has 8 encoder and 8 decoder layers, in total about 60 mi... |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | drsis/pegasus-samsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:00:47+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4251
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 #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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: 1\n* e... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824218
- CO2 Emissions (in grams): 237.58504390669626
## Validation Metrics
- Loss: 0.2379177361726761
- Accuracy: 0.9734973172736223
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ cur... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-final"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 237.58504390669626} | Lucifermorningstar011/autotrain-final-784824218 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:23:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824218
- CO2 Emissions (in grams): 237.58504390669626
## Validation Metrics
- Loss: 0.2379177361726761
- Accuracy: 0.9734973172736223
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Pyth... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824218\n- CO2 Emissions (in grams): 237.58504390669626",
"## Validation Metrics\n\n- Loss: 0.2379177361726761\n- Accuracy: 0.9734973172736223\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acces... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824218\n- CO2 Emiss... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824206
- CO2 Emissions (in grams): 354.21745907505175
## Validation Metrics
- Loss: 0.1393078863620758
- Accuracy: 0.9785765909606228
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ cur... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-final"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 354.21745907505175} | Lucifermorningstar011/autotrain-final-784824206 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:23:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824206
- CO2 Emissions (in grams): 354.21745907505175
## Validation Metrics
- Loss: 0.1393078863620758
- Accuracy: 0.9785765909606228
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Pyth... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824206\n- CO2 Emissions (in grams): 354.21745907505175",
"## Validation Metrics\n\n- Loss: 0.1393078863620758\n- Accuracy: 0.9785765909606228\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acces... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824206\n- CO2 Emissions (... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824213
- CO2 Emissions (in grams): 443.62532415086787
## Validation Metrics
- Loss: 0.12777526676654816
- Accuracy: 0.9823625038850627
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ cu... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-final"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 443.62532415086787} | Lucifermorningstar011/autotrain-final-784824213 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:24:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824213
- CO2 Emissions (in grams): 443.62532415086787
## Validation Metrics
- Loss: 0.12777526676654816
- Accuracy: 0.9823625038850627
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Pyt... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824213\n- CO2 Emissions (in grams): 443.62532415086787",
"## Validation Metrics\n\n- Loss: 0.12777526676654816\n- Accuracy: 0.9823625038850627\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acce... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824213\n- CO2 Emissions (... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824209
- CO2 Emissions (in grams): 0.8282546197737336
## Validation Metrics
- Loss: 0.18077287077903748
- Accuracy: 0.9639925673427913
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ cu... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-final"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.8282546197737336} | Lucifermorningstar011/autotrain-final-784824209 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:24:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824209
- CO2 Emissions (in grams): 0.8282546197737336
## Validation Metrics
- Loss: 0.18077287077903748
- Accuracy: 0.9639925673427913
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Pyt... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824209\n- CO2 Emissions (in grams): 0.8282546197737336",
"## Validation Metrics\n\n- Loss: 0.18077287077903748\n- Accuracy: 0.9639925673427913\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acce... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824209\n- CO2 Emiss... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824211
- CO2 Emissions (in grams): 292.55119229577315
## Validation Metrics
- Loss: 0.17682738602161407
- Accuracy: 0.9732196168090091
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ cu... | {"language": "en", "tags": "autotrain", "datasets": ["Lucifermorningstar011/autotrain-data-final"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 292.55119229577315} | Lucifermorningstar011/autotrain-final-784824211 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:Lucifermorningstar011/autotrain-data-final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:24:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 784824211
- CO2 Emissions (in grams): 292.55119229577315
## Validation Metrics
- Loss: 0.17682738602161407
- Accuracy: 0.9732196168090091
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Pyt... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824211\n- CO2 Emissions (in grams): 292.55119229577315",
"## Validation Metrics\n\n- Loss: 0.17682738602161407\n- Accuracy: 0.9732196168090091\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to acce... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-Lucifermorningstar011/autotrain-data-final #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 784824211\n- CO2 Emiss... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem: Fake News Classification
- Problem type: Binary Classification
- Model ID: 785124234
- CO2 Emissions (in grams): 4.415122243239347
## Validation Metrics
- Loss: 0.00012586714001372457
- Accuracy: 0.9998886538247411
- Precision: 1.0
- Recall: 0.9997665732959851
- AUC: 0.999... | {"language": "en", "tags": "autotrain", "datasets": ["Fake and real news datasets by CL\u00c9MENT BISAILLON"], "widget": [{"text": "Bill Gates wants to use mass Covid-19 vaccination campaign to implant microchips to track people"}], "co2_eq_emissions": 4.415122243239347} | Nithiwat/fake-news-debunker | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-25T14:55:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem: Fake News Classification
- Problem type: Binary Classification
- Model ID: 785124234
- CO2 Emissions (in grams): 4.415122243239347
## Validation Metrics
- Loss: 0.00012586714001372457
- Accuracy: 0.9998886538247411
- Precision: 1.0
- Recall: 0.9997665732959851
- AUC: 0.999... | [
"# Model Trained Using AutoTrain\n\n- Problem: Fake News Classification\n- Problem type: Binary Classification\n- Model ID: 785124234\n- CO2 Emissions (in grams): 4.415122243239347",
"## Validation Metrics\n\n- Loss: 0.00012586714001372457\n- Accuracy: 0.9998886538247411\n- Precision: 1.0\n- Recall: 0.99976657329... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem: Fake News Classification\n- Problem type: Binary Classification\n- Model ID: 785124234\n- CO2 Emissions (in grams)... |
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