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token-classification | transformers | ---
license: gpl-3.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-chinese-0407-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete ... | {"language": ["List of ISO 639-1 code for your language", "zh"], "widget": [{"text": "\u4e2d\u592e\u75ab\u60c5\u6307\u63ee\u4e2d\u5fc3\u81e8\u6642\u8a18\u8005\u6703\u5ba3\u5e03\u5168\u9662\u5340\u70ba\u7d05\u5340\uff0c\u64f4\u5927\u9694\u96e2\uff0c\u4f46\u912d\u6587\u71e6\u65e9\u5728\u4e03\u5341\u4e8c\u5c0f\u6642\u524d... | yihsuan/albert-base-chinese-0407-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T00:39:49+00:00 | [] | [
"List of ISO 639-1 code for your language",
"zh"
] | TAGS
#transformers #pytorch #tensorboard #albert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
---
license: gpl-3.0
tags:
* generated\_from\_trainer
metrics:
* precision
* recall
* f1
* accuracy
model-index:
* name: albert-base-chinese-0407-ner
results: []
---
albert-base-chinese-0407-ner
============================
This model is a fine-tuned version of ckiplab/albert-base-chinese on an unknown da... | [
"### 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* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* see... |
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-MIR_ST500-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-MIR_ST500-demo-colab", "results": []}]} | gary109/wav2vec2-base-MIR_ST500-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T02:03:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-MIR\_ST500-demo-colab
===================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7360
* Wer: 0.9837
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-classification | transformers | # Bad_text_classifier
## Model 소개
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
```
NOTE)
공개 데이터의 저작권 문제로 인해 모델 학습에 사용된 변형된 데이터는 공개 불가능하다는 점을 밝힙니다.
또한 해당 모델의 의견은 제 의견과 무관하다는 점을 미리 밝힙니다.
```
... | {} | JminJ/kcElectra_base_Bad_Sentence_Classifier | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"arxiv:2003.10555",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T02:09:01+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us
| Bad\_text\_classifier
=====================
Model 소개
--------
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
Dataset
-------
### data label
* 0 : bad sentence
* 1 : not bad sentence
... | [
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Dataset",
"### dataset 가공 방법\n\n\n기존 이진 분류가 아니였던 두 데이터를 이진 분류 형태로 labeling을 다시 해준 뒤, Korean HateSpeech Dataset중 label 1(not bad sentence)만을 추려 가공된 Korean Uns... | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us \n",
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Datase... |
text-classification | transformers |
Data: [https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) | {"license": "mit"} | nickil/real-fake-news | null | [
"transformers",
"pytorch",
"longformer",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T02:58:32+00:00 | [] | [] | TAGS
#transformers #pytorch #longformer #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Data: URL | [] | [
"TAGS\n#transformers #pytorch #longformer #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | dd | {"license": "mit"} | EasterWu/ShiNan_machine_learning | null | [
"license:mit",
"region:us"
] | null | 2022-04-07T03:36:50+00:00 | [] | [] | TAGS
#license-mit #region-us
| dd | [] | [
"TAGS\n#license-mit #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... | Sleoruiz/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 | 2022-04-07T04:28:24+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: 0.2176
* Accuracy: 0.927
* F1: 0.9273
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 | ## Model Description
Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic NER dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth) with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags
## How to Use
Huggingface pipeline way (returns tokens with labels):
```... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"], "widget": [{"text": "\u041c\u043e\u0433\u0438\u043b\u0430 \u0422\u0430\u0440\u0430\u0441\u0430 \u0428\u0435\u0432\u0447\u0435\u043d\u043a\u0430 \u2014 \u043c\u0456\u0441\u0446\u0435 \u043f\u043e\u0445\u043e\u0432\u0430\u043d\u043d\u044f \u0432\u0438\u0434\u0... | ukr-models/uk-ner | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"ukrainian",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-07T04:31:07+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Model Description
Fine-tuning of XLM-RoBERTa-Uk model on synthetic NER dataset with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags
## How to Use
Huggingface pipeline way (returns tokens with labels):
If you wish to get predictions split by words, not by tokens, you may use the following approach (download script ... | [
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on synthetic NER dataset with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags",
"## How to Use\n\nHuggingface pipeline way (returns tokens with labels):\n\n\nIf you wish to get predictions split by words, not by tokens, you may use the following approach (d... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on synthetic NER dataset with B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags",
... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | srmukundb/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T06:13:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2582
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #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... |
null | transformers | # Model Card for Model ID
## Model Details
The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingual are different types from a modell... | {"language": ["en", "gu", "mr", "hi"], "license": "apache-2.0"} | swagat-panda/multilingual-pos-tagger-indian-context-muril | null | [
"transformers",
"pytorch",
"bert",
"en",
"gu",
"mr",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T06:30:46+00:00 | [] | [
"en",
"gu",
"mr",
"hi"
] | TAGS
#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us
| Model Card for Model ID
=======================
Model Details
-------------
The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingua... | [
"### Model Description\n\n\nThe model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\\_romanised,mr\\_romanised,hi\\_romanised)\n\n\nTo use this model you have import this class\n\n\nSome sample output from the model\n\n\n\n* Devel... | [
"TAGS\n#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model Description\n\n\nThe model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\\_romanised,mr\\_romanised,hi\\_ro... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 715021714
- CO2 Emissions (in grams): 0.003341516495672918
## Validation Metrics
- Loss: 0.5571377873420715
- Accuracy: 0.8
- Macro F1: 0.6709090909090909
- Micro F1: 0.8000000000000002
- Weighted F1: 0.7739393939393939
- Macro P... | {"language": "unk", "tags": "autotrain", "datasets": ["shubh024/autotrain-data-intentclassificationfilipino"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.003341516495672918} | shubh024/autotrain-intentclassificationfilipino-715021714 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:shubh024/autotrain-data-intentclassificationfilipino",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T06:37:58+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-shubh024/autotrain-data-intentclassificationfilipino #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 715021714
- CO2 Emissions (in grams): 0.003341516495672918
## Validation Metrics
- Loss: 0.5571377873420715
- Accuracy: 0.8
- Macro F1: 0.6709090909090909
- Micro F1: 0.8000000000000002
- Weighted F1: 0.7739393939393939
- Macro P... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 715021714\n- CO2 Emissions (in grams): 0.003341516495672918",
"## Validation Metrics\n\n- Loss: 0.5571377873420715\n- Accuracy: 0.8\n- Macro F1: 0.6709090909090909\n- Micro F1: 0.8000000000000002\n- Weighted F1: 0.77393939... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-shubh024/autotrain-data-intentclassificationfilipino #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 715021... |
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... | Sleoruiz/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 | 2022-04-07T06:42:23+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: 0.7663
* Matthews Correlation: 0.5396
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... |
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/1362391177438384130/3qb0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/joshrevellyt-mattywtf1-twommof1/1649318312148/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/joshrevellyt-mattywtf1-twommof1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T06:45:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Matt Gallagher & Tommo & Josh Revell
@joshrevellyt-mattywtf1-twommof1
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, ch... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # Fairseq-dense 13B - Shinen
## Model Description
Fairseq-dense 13B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-13B-Shinen | null | [
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"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-07T07:05:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 13B - Shinen
## Model Description
Fairseq-dense 13B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.
## Training dat... | [
"# Fairseq-dense 13B - Shinen",
"## Model Description\nFairseq-dense 13B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.\nWarning: THIS model is NOT suitable for use by minors. The model will output X-rated content.",
... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fairseq-dense 13B - Shinen",
"## Model Description\nFairseq-dense 13B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this ... |
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. -->
# vit-base-cifar10
This model is a fine-tuned version of [nateraw/vit-base-patch16-224-cifar10](https://huggingface.co/nateraw/vit... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "model-index": [{"name": "vit-base-cifar10", "results": []}]} | thapasushil/vit-base-cifar10 | null | [
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"pytorch",
"tensorboard",
"vit",
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"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T07:09:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vit-base-cifar10
This model is a fine-tuned version of nateraw/vit-base-patch16-224-cifar10 on the cifar10-upside-down dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2348
- eval_accuracy: 0.9134
- eval_runtime: 157.4172
- eval_samples_per_second: 127.051
- eval_steps_per_second: ... | [
"# vit-base-cifar10\n\nThis model is a fine-tuned version of nateraw/vit-base-patch16-224-cifar10 on the cifar10-upside-down dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2348\n- eval_accuracy: 0.9134\n- eval_runtime: 157.4172\n- eval_samples_per_second: 127.051\n- eval_steps_pe... | [
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"# vit-base-cifar10\n\nThis model is a fine-tuned version of nateraw/vit-base-patch16-224-cifar10 on the cifar10-upside-down dataset.\nIt ac... |
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/456005573/scarbs_400x400... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/enginemode11-phoenixstk19-scarbstech/1649319522056/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/enginemode11-phoenixstk19-scarbstech | null | [
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"region:us"
] | null | 2022-04-07T07:15:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Craig Scarborough & Alpine F1 Team technical updates & EngineMode11
@enginemode11-phoenixstk19-scarbstech
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 unders... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robbert-twitter-sentiment-tokenized
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["dutch_social"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "robbert-twitter-sentiment-tokenized", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dutch_socia... | btjiong/robbert-twitter-sentiment-tokenized | null | [
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"dataset:dutch_social",
"license:mit",
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"endpoints_compatible",
"region:us"
] | null | 2022-04-07T07:22:43+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| robbert-twitter-sentiment-tokenized
===================================
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on the dutch\_social dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5473
* Accuracy: 0.814
* F1: 0.8133
* Precision: 0.8131
* Recall: 0.814
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\... |
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-4
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-french](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-4", "results": []}]} | guillaumegg/wav2vec2-base-timit-demo-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T07:25:30+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-4
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# wav2vec2-base-timit-demo-4\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-4\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53-french on the None dataset.",
"## Model desc... |
null | null | https://colab.research.google.com/drive/10h5b_OIB16v5R3ywaX7Yse1RSvv94CHo?usp=sharing | {"license": "mit"} | abideen305/fatima_coding_NLP | null | [
"license:mit",
"region:us"
] | null | 2022-04-07T07:35:09+00:00 | [] | [] | TAGS
#license-mit #region-us
| URL | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-generation | transformers |
# Have a chat with Dumbledore
| {"tags": ["conversational"]} | florentiino/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T07:52:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Have a chat with Dumbledore
| [
"# Have a chat with Dumbledore"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Have a chat with Dumbledore"
] |
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. -->
# violation-classification-bantai-vit-withES
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "model-index": [{"name": "violation-classification-bantai-vit-withES", "results": []}]} | AykeeSalazar/violation-classification-bantai-vit-withES | null | [
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"tensorboard",
"vit",
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"generated_from_trainer",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T07:54:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# violation-classification-bantai-vit-withES
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2234
- eval_accuracy: 0.9592
- eval_runtime: 64.9173
- eval_samples_per_second: 85.37
- eval_steps... | [
"# violation-classification-bantai-vit-withES\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2234\n- eval_accuracy: 0.9592\n- eval_runtime: 64.9173\n- eval_samples_per_second: 85.37\n- ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# violation-classification-bantai-vit-withES\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in2... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.4,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Auth... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | amrezo/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-04-07T08:09:17+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (3 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] |
text-generation | transformers |
# Multilingual GPT model
We introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus.
We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, [Deepspeed](ht... | {"language": ["ar", "he", "vi", "id", "jv", "ms", "tl", "lv", "lt", "eu", "ml", "ta", "te", "hy", "bn", "mr", "hi", "ur", "af", "da", "en", "de", "sv", "fr", "it", "pt", "ro", "es", "el", "os", "tg", "fa", "ja", "ka", "ko", "th", "bxr", "xal", "mn", "sw", "yo", "be", "bg", "ru", "uk", "pl", "my", "uz", "ba", "kk", "ky"... | ai-forever/mGPT | null | [
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"da",
"en",
... | null | 2022-04-07T08:13:42+00:00 | [
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"s... | TAGS
#transformers #pytorch #gpt2 #text-generation #multilingual #PyTorch #Transformers #gpt3 #Deepspeed #Megatron #ar #he #vi #id #jv #ms #tl #lv #lt #eu #ml #ta #te #hy #bn #mr #hi #ur #af #da #en #de #sv #fr #it #pt #ro #es #el #os #tg #fa #ja #ka #ko #th #bxr #xal #mn #sw #yo #be #bg #ru #uk #pl #my #uz #ba #kk #ky... |
# Multilingual GPT model
We introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus.
We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, Deepspeed and ... | [
"# Multilingual GPT model\n\nWe introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. \n\nWe reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, Deepsp... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #multilingual #PyTorch #Transformers #gpt3 #Deepspeed #Megatron #ar #he #vi #id #jv #ms #tl #lv #lt #eu #ml #ta #te #hy #bn #mr #hi #ur #af #da #en #de #sv #fr #it #pt #ro #es #el #os #tg #fa #ja #ka #ko #th #bxr #xal #mn #sw #yo #be #bg #ru #uk #pl #my #uz #ba #... |
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-test3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-test3", "results": []}]} | Brokette/wav2vec2-base-timit-test3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T08:36:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-test3
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 hyperpa... | [
"# wav2vec2-base-timit-test3\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 proce... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-test3\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore i... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AlbertoBertsentipol
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "AlbertoBertsentipol", "results": []}]} | GioReg/AlbertoBertsentipol | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T08:41:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# AlbertoBertsentipol
This model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# AlbertoBertsentipol\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# AlbertoBertsentipol\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Mode... |
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/1252178304192389120/bXT3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chrismedlandf1-formula24hrs-tgruener/1649324884859/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chrismedlandf1-formula24hrs-tgruener | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T08:47:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Chris Medland & Tobi Grüner & F24
@chrismedlandf1-formula24hrs-tgruener
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, ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-test5
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-test5", "results": []}]} | Brokette/wav2vec2-base-timit-test5 | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T09:18:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-test5
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 hyperpa... | [
"# wav2vec2-base-timit-test5\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 proce... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-test5\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore i... |
null | null | For the #huggan event we decided to fine-tune a cloob-conditioned latent diffusion model.
Training largely followed the guidelines in https://github.com/JD-P/cloob-latent-diffusion
The (lightly modified) training script is included in this model repo, but I'd recommend starting in the github repository linked above.
Th... | {} | huggan/ccld_wa | null | [
"has_space",
"region:us"
] | null | 2022-04-07T09:38:33+00:00 | [] | [] | TAGS
#has_space #region-us
| For the #huggan event we decided to fine-tune a cloob-conditioned latent diffusion model.
Training largely followed the guidelines in URL
The (lightly modified) training script is included in this model repo, but I'd recommend starting in the github repository linked above.
The model was fine-tuned on Wikiart (huggan/W... | [] | [
"TAGS\n#has_space #region-us \n"
] |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | Fredvv/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T09:43:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8388
- Bleu: 53.5744
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8388\n- Bleu: 53.5744",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
text-generation | transformers | # Fairseq-dense 6.7B - Janeway
## Model Description
Fairseq-dense 6.7B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts of t... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-6.7B-Janeway | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T09:47:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Fairseq-dense 6.7B - Janeway
## Model Description
Fairseq-dense 6.7B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts of t... | [
"# Fairseq-dense 6.7B - Janeway",
"## Model Description\nFairseq-dense 6.7B-Janeway is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fairseq-dense 6.7B - Janeway",
"## Model Description\nFairseq-dense 6.7B-Janeway is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training data co... |
null | null | ruDALLE Surrealist XL
---
[Alex Shonenkov](https://github.com/shonenkov-AI) trained the model. It is fine-tuning of [Malevich XL](https://huggingface.co/sberbank-ai/rudalle-Malevich) on the surreal data of famous art creators.
... | {} | shonenkov-AI/rudalle-xl-surrealist | null | [
"pytorch",
"region:us"
] | null | 2022-04-07T09:49:19+00:00 | [] | [] | TAGS
#pytorch #region-us
| ruDALLE Surrealist XL
---
![](URL
Alex Shonenkov trained the model. It is fine-tuning of Malevich XL on the surreal data of famous art creators.
+ Task: text2image generation with custom aspect ratio
+ Parameters: 1.3 B
+ Training Data: 190 text-image pairs | [] | [
"TAGS\n#pytorch #region-us \n"
] |
feature-extraction | transformers |
# RoBERTa Tagalog Base (finetuned on COHFIE)
We finetuned [RoBERTa Tagalog Base](https://huggingface.co/jcblaise/roberta-tagalog-base) on a subset of the Corpus of Historical Filipino and Philippine English (COHFIE) which contains pure filipino and code-switching (i.e. tagalog-english) sentences. All model details, tr... | {"language": "tl", "license": "cc-by-sa-4.0", "tags": ["roberta", "tagalog", "filipino"], "inference": false} | danjohnvelasco/roberta-tagalog-base-cohfie-v1 | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"tagalog",
"filipino",
"tl",
"arxiv:2204.03251",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-04-07T11:08:53+00:00 | [
"2204.03251"
] | [
"tl"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #tagalog #filipino #tl #arxiv-2204.03251 #license-cc-by-sa-4.0 #region-us
|
# RoBERTa Tagalog Base (finetuned on COHFIE)
We finetuned RoBERTa Tagalog Base on a subset of the Corpus of Historical Filipino and Philippine English (COHFIE) which contains pure filipino and code-switching (i.e. tagalog-english) sentences. All model details, training setups, and corpus details can be found in this p... | [
"# RoBERTa Tagalog Base (finetuned on COHFIE)\nWe finetuned RoBERTa Tagalog Base on a subset of the Corpus of Historical Filipino and Philippine English (COHFIE) which contains pure filipino and code-switching (i.e. tagalog-english) sentences. All model details, training setups, and corpus details can be found in t... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #tagalog #filipino #tl #arxiv-2204.03251 #license-cc-by-sa-4.0 #region-us \n",
"# RoBERTa Tagalog Base (finetuned on COHFIE)\nWe finetuned RoBERTa Tagalog Base on a subset of the Corpus of Historical Filipino and Philippine English (COHFIE) which contains... |
null | sentence-transformers |
# Filipino Sentence RoBERTa
We finetuned [RoBERTa Tagalog Base (finetuned on COHFIE)](https://huggingface.co/danjohnvelasco/roberta-tagalog-base-cohfie-v1) on [NewsPH-NLI](https://huggingface.co/datasets/newsph_nli) to learn to encode filipino/tagalog sentences to sentence embeddings. We used [sentence-transformers](h... | {"language": "tl", "license": "cc-by-sa-4.0", "tags": ["roberta", "tagalog", "filipino", "sentence-transformers"], "datasets": "newsph_nli"} | danjohnvelasco/filipino-sentence-roberta-v1 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"tagalog",
"filipino",
"tl",
"dataset:newsph_nli",
"arxiv:2204.03251",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-04-07T11:20:16+00:00 | [
"2204.03251"
] | [
"tl"
] | TAGS
#sentence-transformers #pytorch #roberta #tagalog #filipino #tl #dataset-newsph_nli #arxiv-2204.03251 #license-cc-by-sa-4.0 #region-us
|
# Filipino Sentence RoBERTa
We finetuned RoBERTa Tagalog Base (finetuned on COHFIE) on NewsPH-NLI to learn to encode filipino/tagalog sentences to sentence embeddings. We used sentence-transformers to finetune the model. All model details, training setups, and corpus details can be found in this paper: Automatic WordN... | [
"# Filipino Sentence RoBERTa\nWe finetuned RoBERTa Tagalog Base (finetuned on COHFIE) on NewsPH-NLI to learn to encode filipino/tagalog sentences to sentence embeddings. We used sentence-transformers to finetune the model. All model details, training setups, and corpus details can be found in this paper: Automatic ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #tagalog #filipino #tl #dataset-newsph_nli #arxiv-2204.03251 #license-cc-by-sa-4.0 #region-us \n",
"# Filipino Sentence RoBERTa\nWe finetuned RoBERTa Tagalog Base (finetuned on COHFIE) on NewsPH-NLI to learn to encode filipino/tagalog sentences to sentence embedding... |
null | transformers | # Model Card for Model ID
## Model Details
The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingual are different types from a modell... | {"language": ["en", "gu", "mr", "hi"], "license": "apache-2.0"} | swagat-panda/multilingual-pos-tagger-language-detection-indian-context-muril | null | [
"transformers",
"pytorch",
"bert",
"en",
"gu",
"mr",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:22:26+00:00 | [] | [
"en",
"gu",
"mr",
"hi"
] | TAGS
#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us
| Model Card for Model ID
=======================
Model Details
-------------
The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingua... | [
"### Model Description\n\n\nThe model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\\_romanised,mr\\_romanised,hi\\_romanised)\nTo use this model you have import this class\n\n\nSome sample output from the model\n\n\nThis model us... | [
"TAGS\n#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model Description\n\n\nThe model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\\_romanised,mr\\_romanised,hi\\_ro... |
image-classification | null |
## Model description
**Upside down detector**: Model to detect if images are upside down
* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not.
## Intended us... | {"language": "en", "license": "mit", "tags": "image-classification", "metrics": "accuracy (https://hf.co/metrics/accuracy)", "dataset": "cifar10"} | numbpy/flip_detector | null | [
"image-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-04-07T11:24:52+00:00 | [] | [
"en"
] | TAGS
#image-classification #en #license-mit #region-us
|
## Model description
Upside down detector: Model to detect if images are upside down
* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not.
## Intended uses &... | [
"## Model description\n\nUpside down detector: Model to detect if images are upside down\n\n* Picked a dataset of natural images - cifar10\n* Synthetically turned some of images upside down. Created a training and test set.\n* Trained it to classify image orientation ie if the image is upside down or not.",
"## I... | [
"TAGS\n#image-classification #en #license-mit #region-us \n",
"## Model description\n\nUpside down detector: Model to detect if images are upside down\n\n* Picked a dataset of natural images - cifar10\n* Synthetically turned some of images upside down. Created a training and test set.\n* Trained it to classify im... |
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. -->
# nezha-chinese-base-finetuned-product
This model is a fine-tuned version of [peterchou/nezha-chinese-base](https://huggingface.co... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "nezha-chinese-base-finetuned-product", "results": []}]} | agdsga/nezha-chinese-base-finetuned-product | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:26:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| nezha-chinese-base-finetuned-product
====================================
This model is a fine-tuned version of peterchou/nezha-chinese-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0004
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #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: 64\n* eval\\_batc... |
fill-mask | transformers | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 250k steps (batch size 64). The token embeddings were N... | {} | vocab-transformers/distilbert-word2vec_256k-MLM_250k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:38:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 250k steps (batch size 64). The token embeddings were N... | [
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this dataset with MLM for 250k steps (batch size 64). The token embeddin... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for ... |
fill-mask | transformers | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 500k steps (batch size 64). The token embeddings were N... | {} | vocab-transformers/distilbert-word2vec_256k-MLM_500k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:49:36+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 500k steps (batch size 64). The token embeddings were N... | [
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this dataset with MLM for 500k steps (batch size 64). The token embeddin... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for ... |
fill-mask | transformers | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 750k steps (batch size 64). The token embeddings were N... | {} | vocab-transformers/distilbert-word2vec_256k-MLM_750k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:53:52+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 750k steps (batch size 64). The token embeddings were N... | [
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this dataset with MLM for 750k steps (batch size 64). The token embeddin... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for ... |
fill-mask | transformers | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1M steps (batch size 64). The token embeddings were NOT... | {} | vocab-transformers/distilbert-word2vec_256k-MLM_1M | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T11:57:33+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1M steps (batch size 64). The token embeddings were NOT... | [
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this dataset with MLM for 1M steps (batch size 64). The token embeddings... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for ... |
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. -->
# BertMultiHateSpeech
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-mult... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "BertMultiHateSpeech", "results": []}]} | GioReg/BertMultiHateSpeech | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:02:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BertMultiHateSpeech
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7496
- Accuracy: 0.74
- F1: 0.4841
## Model description
More information needed
## Intended uses & limitations
More information neede... | [
"# BertMultiHateSpeech\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7496\n- Accuracy: 0.74\n- F1: 0.4841",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BertMultiHateSpeech\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the followi... |
fill-mask | transformers | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | {} | vocab-transformers/distilbert-tokenizer_256k-MLM_250k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | [
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this da... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was tra... |
fill-mask | transformers | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | {} | vocab-transformers/distilbert-tokenizer_256k-MLM_500k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | [
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this da... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was tra... |
fill-mask | transformers | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | {} | vocab-transformers/distilbert-tokenizer_256k-MLM_750k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:09:28+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | [
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this da... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was tra... |
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-pos
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-pos", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Fredvv/bert-finetuned-pos | 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 | 2022-04-07T12:16:32+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-pos
==================
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: 0.0580
* Precision: 0.9348
* Recall: 0.9502
* F1: 0.9424
* Accuracy: 0.9868
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... |
text2text-generation | transformers |
## Intro
Trained on IndicNLGSuit [IndicQuestionGeneration](https://huggingface.co/datasets/ai4bharat/IndicQuestionGeneration) data for Bengali the model is finetuned from [IndicBART](https://huggingface.co/ai4bharat/IndicBART)
## Finetuned Command
python run_summarization.py --model_name_or_path bnQG_models/... | {"language": ["bn"], "license": "mit", "tags": ["text2text-generation"], "widget": [{"text": "\u09e7\u09ee\u09ef\u09ed \u0996\u09cd\u09b0\u09bf\u09b7\u09cd\u099f\u09be\u09ac\u09cd\u09a6\u09c7\u09b0 \u09e8\u09e9 \u099c\u09be\u09a8\u09c1\u09af\u09bc\u09be\u09b0\u09bf [SEP] \u09b8\u09c1\u09ad\u09be\u09b7 \u09e7\u09ee\u09e... | arijitx/IndicBART-bn-QuestionGeneration | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bn",
"arxiv:2203.05437",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:37:56+00:00 | [
"2203.05437"
] | [
"bn"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #bn #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## Intro
Trained on IndicNLGSuit IndicQuestionGeneration data for Bengali the model is finetuned from IndicBART
## Finetuned Command
python run_summarization.py --model_name_or_path bnQG_models/checkpoint-32000 --do_eval --train_file train_bn.json
--validation_file valid_bn.json --output_dir bnQG_models... | [
"## Intro \n Trained on IndicNLGSuit IndicQuestionGeneration data for Bengali the model is finetuned from IndicBART",
"## Finetuned Command\n\n python run_summarization.py --model_name_or_path bnQG_models/checkpoint-32000 --do_eval --train_file train_bn.json \n --validation_file valid_bn.json --output_dir ... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #bn #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Intro \n Trained on IndicNLGSuit IndicQuestionGeneration data for Bengali the model is finetuned from IndicBART",
"## Finetuned Command\n\n python run... |
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-russian-colab-beam_search_test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-russian-colab-beam_search_test", "results": []}]} | jfealko/wav2vec2-large-xls-r-300m-russian-colab-beam_search_test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:53:21+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-russian-colab-beam\_search\_test
==========================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7619
* Wer: 0.4680
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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. -->
# bert-large-cased-whole-word-masking-sst2
This model is a fine-tuned version of [bert-large-cased-whole-word-masking](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-whole-word-masking-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2... | philschmid/bert-large-cased-whole-word-masking-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T12:55:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-cased-whole-word-masking-sst2
This model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# bert-large-cased-whole-word-masking-sst2\n\nThis model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1725\n- Accuracy: 0.9438",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-cased-whole-word-masking-sst2\n\nThis model is a fine-tuned version of bert-large-cased-whole-word-... |
fill-mask | transformers | language:
- it
Si è creato un modello, chiamato notiBERTo, svolgendo la fase di addestramento e utilizzando per la creazione e il tuning dei pesi del modello l’algoritmo non supervisionato di masked-language modeling (MLM); questo non richiede l’utilizzo di testo con etichettatura. L’idea e stata quella di ottener... | {} | GioReg/notiBERTo | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T13:24:36+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| language:
- it
Si è creato un modello, chiamato notiBERTo, svolgendo la fase di addestramento e utilizzando per la creazione e il tuning dei pesi del modello l’algoritmo non supervisionato di masked-language modeling (MLM); questo non richiede l’utilizzo di testo con etichettatura. L’idea e stata quella di ottener... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the Cantemist dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitati... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "eHR", "spanish"], "datasets": ["PlanTL-GOB-ES/cantemist-ner"], "metrics": ["f1"], "widget": [{"text": "El diagn\u00f3stico definitivo de nuestro paciente fue de un Adenocarcinoma de pulm\u00f3n cT2a cN3 cM1a Estadio IV (por una \u00fanica... | PlanTL-GOB-ES/bsc-bio-ehr-es-cantemist | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"biomedical",
"clinical",
"eHR",
"spanish",
"es",
"dataset:PlanTL-GOB-ES/cantemist-ner",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T13:29:07+00:00 | [
"1907.11692"
] | [
"es"
] | TAGS
#transformers #pytorch #roberta #token-classification #biomedical #clinical #eHR #spanish #es #dataset-PlanTL-GOB-ES/cantemist-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the Cantemist dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- Model description
- Intended uses and limitations
- How to use
- Limitations and bias
- Training
- Evaluation
- Additional infor... | [
"# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the Cantemist dataset.",
"## Table of contents\n<details>\n<summary>Click to expand</summary>\n\n- Model description\n- Intended uses and limitations\n- How to use\n- Limitations and bias\n- Training\n- Evaluation\n-... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #biomedical #clinical #eHR #spanish #es #dataset-PlanTL-GOB-ES/cantemist-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Spanish RoBERTa-base biomedical model finetuned for the Named ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikisql-sql-nl-nl-sql
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "widget": [{"text": "translate to SQL: How many models with BERT architecture are in the HuggingFace Hub?"}, {"text": "translate to English: SELECT COUNT Model FROM table WHERE Architecture = RoBERTa AND creator = Manuel Romero"}], "mode... | mrm8488/t5-small-finetuned-wikisql-sql-nl-nl-sql | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T13:55:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikisql-sql-nl-nl-sql
========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1932
* Bleu: 41.8787
* Gen Len: 16.6251
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
image-classification | transformers |
# fruits
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | hafidber/fruits | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-07T14:02:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# fruits
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### apple
!apple
#### banana
!banana
#### grape
!grape
#### kiwi
!kiwi
#### lemon
!lemon | [
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### grape\n\n!grape",
"#### kiwi\n\n!kiwi",
... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any is... |
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/1244120919901175808/3krE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/zahedparsa2/1649345587235/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/zahedparsa2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T14:30:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Zahedparsa
@zahedparsa2
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1496116344869244938/d7ZI... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mohamad_yazdi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T14:53:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
mohamad yazdi
@mohamad\_yazdi
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# hindi-clsril-100
Fine-tuned [Harveenchadha/wav2vec2-pretrained-clsril-23-10k](https://huggingface.co/Harveenchadha/wav2vec2-pretrained-clsril-23-10k) on Hindi using the [Common Voice](https://huggingface.co/datasets/common_voice), included [openSLR](http://www.openslr.org/103/) Hindi dataset.
When using this model... | {"language": "hi", "license": "cc", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Hindi Model by Swayam Mittal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name":... | swayam01/hindi-clsril-100 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:cc",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T15:08:35+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-cc #model-index #endpoints_compatible #region-us
|
# hindi-clsril-100
Fine-tuned Harveenchadha/wav2vec2-pretrained-clsril-23-10k on Hindi using the Common Voice, included openSLR Hindi dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Evaluation
The model can be used directly (with or without a language model) as follows:
T... | [
"# hindi-clsril-100\n\nFine-tuned Harveenchadha/wav2vec2-pretrained-clsril-23-10k on Hindi using the Common Voice, included openSLR Hindi dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Evaluation\nThe model can be used directly (with or without a language model) as f... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-cc #model-index #endpoints_compatible #region-us \n",
"# hindi-clsril-100\n\nFine-tuned Harveenchadha/wav2vec2-pretrained-clsril-23-10k on Hindi using the Common Voice, included openSLR Hindi d... |
image-classification | transformers |
# anomaly2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | hafidber/anomaly2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T15:10:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# anomaly2
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### abnormal
!abnormal
#### normal
!normal | [
"# anomaly2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### abnormal\n\n!abnormal",
"#### normal\n\n!normal"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# anomaly2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with... |
text-classification | transformers |
## Hinglish-Bert-Class fine-tuned on Hinglish11K dataset.
# MCC= 0.69
### Citation info
```bibtex
@model{
contributors= {Mohammad Yusuf Jamal Aziz Azmi and
Ayush Agrawal
},
year = {2022},
timestamp = {Sun, 08 May 2022},
}
``` | {"language": ["en", "ur", "hi", "multilingual"], "license": "afl-3.0", "widget": [{"text": "Tum bohot badiya ho."}]} | yj2773/hinglish11k-sentiment-analysis | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"ur",
"hi",
"multilingual",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-07T15:11:03+00:00 | [] | [
"en",
"ur",
"hi",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #ur #hi #multilingual #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Hinglish-Bert-Class fine-tuned on Hinglish11K dataset.
# MCC= 0.69
info
| [
"## Hinglish-Bert-Class fine-tuned on Hinglish11K dataset.",
"# MCC= 0.69\n\n\ninfo"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #ur #hi #multilingual #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Hinglish-Bert-Class fine-tuned on Hinglish11K dataset.",
"# MCC= 0.69\n\n\ninfo"
] |
null | null |
This repo contains a model that is capable of detecting upside images.
This is part of my submission for the Fatima Fellowship Selection Task. | {"language": ["en"], "license": "unlicense", "tags": ["Image Classification"], "datasets": ["cifar100"]} | gymball/FatimaFellowship-UpsideDown | null | [
"Image Classification",
"en",
"dataset:cifar100",
"license:unlicense",
"region:us"
] | null | 2022-04-07T15:18:53+00:00 | [] | [
"en"
] | TAGS
#Image Classification #en #dataset-cifar100 #license-unlicense #region-us
|
This repo contains a model that is capable of detecting upside images.
This is part of my submission for the Fatima Fellowship Selection Task. | [] | [
"TAGS\n#Image Classification #en #dataset-cifar100 #license-unlicense #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj_v4
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/b... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v4", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T15:33:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v4
=========================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3626
* Precision: 0.6308
* Recall: 0.4489
* F1: 0.5245
* Accuracy: 0.8579
Model... | [
"### 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 #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "results", "results": []}]} | Shadman-Rohan/results | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T15:41:02+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0002
* Accuracy: 0.8923
* F1: 0.9167
* Precision: 0.8462
* Recall: 1.0
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln2")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln2")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-fou... | {} | BigSalmon/MediumInformalToFormalLincoln2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T15:48:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
(makes one sentence, two sentences) (probably will not work all that well)
(makes two sentences, one sentence) (probably will not work all that well)
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | 1. Deep Learning for Vision
Upside down detector: Train a model to detect if images are upside down
Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
Synthetically turn some of images upside down. Create a training and test set.
Build a neural network (using Tensorflow, PyTorch,... | {} | reeda23/upside-down-detector | null | [
"region:us"
] | null | 2022-04-07T16:00:47+00:00 | [] | [] | TAGS
#region-us
| 1. Deep Learning for Vision
Upside down detector: Train a model to detect if images are upside down
Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
Synthetically turn some of images upside down. Create a training and test set.
Build a neural network (using Tensorflow, PyTorch,... | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221775
- CO2 Emissions (in grams): 5.080390550458655
## Validation Metrics
- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760... | {"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.080390550458655} | palakagl/bert_MultiClass_TextClassification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:palakagl/autotrain-data-PersonalAssitant",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:04:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221775
- CO2 Emissions (in grams): 5.080390550458655
## Validation Metrics
- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221775\n- CO2 Emiss... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382
## Validation Metrics
- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963... | {"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.258363491829382} | palakagl/distilbert_MultiClass_TextClassification | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:palakagl/autotrain-data-PersonalAssitant",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:10:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382
## Validation Metrics
- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221781\n- CO2 Emissions (in grams): 2.258363491829382",
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221781\n- CO2... |
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... | jackmleitch/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:10:40+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2120
* Accuracy: 0.9285
* F1: 0.9285
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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905
## Validation Metrics
- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137... | {"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.014567637985425905} | palakagl/Roberta_Multiclass_TextClassification | null | [
"transformers",
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"text-classification",
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"en",
"dataset:palakagl/autotrain-data-PersonalAssitant",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:12:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905
## Validation Metrics
- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221783\n- CO2 Em... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221787
- CO2 Emissions (in grams): 7.025108874009706
## Validation Metrics
- Loss: 0.35467109084129333
- Accuracy: 0.9186046511627907
- Macro F1: 0.9202890631142154
- Micro F1: 0.9186046511627907
- Weighted F1: 0.9185859051606... | {"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.025108874009706} | palakagl/bert_TextClassification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:palakagl/autotrain-data-PersonalAssitant",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:14:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221787
- CO2 Emissions (in grams): 7.025108874009706
## Validation Metrics
- Loss: 0.35467109084129333
- Accuracy: 0.9186046511627907
- Macro F1: 0.9202890631142154
- Micro F1: 0.9186046511627907
- Weighted F1: 0.9185859051606... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221787\n- CO2 Emissions (in grams): 7.025108874009706",
"## Validation Metrics\n\n- Loss: 0.35467109084129333\n- Accuracy: 0.9186046511627907\n- Macro F1: 0.9202890631142154\n- Micro F1: 0.9186046511627907\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 717221787\n- CO2 Emiss... |
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": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | mdroth/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:21:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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 conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0635
* Precision: 0.9300
* Recall: 0.9391
* F1: 0.9345
* Accuracy: 0.9841
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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* le... |
text-classification | transformers |
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
[Distilbert-base-un... | {"language": ["en"], "tags": ["text-classification", "fake-news", "pytorch"], "datasets": ["Fake News https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset"], "metrics": ["Accuracy, AUC"]} | ahmednasser/DistilBert-FakeNews | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"fake-news",
"en",
"arxiv:1910.01108",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:40:27+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #fake-news #en #arxiv-1910.01108 #endpoints_compatible #region-us
|
## Model description:
Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
Distilbert-base-uncased finetuned on the fake news data... | [
"## Model description:\nDistilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.\n\nDistilbert-base-uncased finetuned on the fake ne... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #fake-news #en #arxiv-1910.01108 #endpoints_compatible #region-us \n",
"## Model description:\nDistilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its la... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | lucypallent/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:49:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4718
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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-german-cased-finetuned-subj_v4_5Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v4_5Epoch", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v4_5Epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T16:55:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v4\_5Epoch
=================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3180
* Precision: 0.6640
* Recall: 0.5985
* F1: 0.6296
* Accuracy... | [
"### 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 #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robbert-twitter-sentiment-custom
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["dutch_social"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "robbert-twitter-sentiment-custom", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dutch_social",... | btjiong/robbert-twitter-sentiment-custom | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:dutch_social",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T17:07:19+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| robbert-twitter-sentiment-custom
================================
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on the dutch\_social dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6656
* Accuracy: 0.788
* F1: 0.7878
* Precision: 0.7877
* Recall: 0.788
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-dutch_social #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\... |
null | null | Architecture: Resnet-18 with two modifications.
1. 1 channel Conv2D as the first layer.
2. 2-way output on FC layer.
Training procedure:
1. Pre-trained in ImageNet.
2. Further training on FashionMNIST.
3. Final training on the task of predicting if Fashion-MNIST images are flipped vertically or not. | {"license": "cc-by-3.0"} | yGalindo/FatimaUpsideDown | null | [
"license:cc-by-3.0",
"region:us"
] | null | 2022-04-07T17:25:08+00:00 | [] | [] | TAGS
#license-cc-by-3.0 #region-us
| Architecture: Resnet-18 with two modifications.
1. 1 channel Conv2D as the first layer.
2. 2-way output on FC layer.
Training procedure:
1. Pre-trained in ImageNet.
2. Further training on FashionMNIST.
3. Final training on the task of predicting if Fashion-MNIST images are flipped vertically or not. | [] | [
"TAGS\n#license-cc-by-3.0 #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... | deblagoj/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 | 2022-04-07T17:26:07+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: 0.2225
* Accuracy: 0.919
* F1: 0.9191
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... |
feature-extraction | transformers |
My new cool model
| {} | rosbo/test-rosbo | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T17:27:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
|
My new cool model
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # Fairseq-dense 6.7B - Shinen
## Model Description
Fairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Traini... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-6.7B-Shinen | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-07T17:30:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 6.7B - Shinen
## Model Description
Fairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.
## Training d... | [
"# Fairseq-dense 6.7B - Shinen",
"## Model Description\nFairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.\nWarning: THIS model is NOT suitable for use by minors. The model will output X-rated content.",... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fairseq-dense 6.7B - Shinen",
"## Model Description\nFairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, thi... |
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/1010263656456744960/bXOU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/timjdillon/1649358240896/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/timjdillon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T18:01:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tim Dillon
@timjdillon
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"
] |
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-german-cased-finetuned-subj_v5_7Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v5_7Epoch", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v5_7Epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T18:06:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v5\_7Epoch
=================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3036
* Precision: 0.7983
* Recall: 0.7781
* F1: 0.7881
* Accuracy... | [
"### 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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ACTS_feedback1
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "ACTS_feedback1", "results": []}]} | mp6kv/ACTS_feedback1 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T18:23:57+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ACTS\_feedback1
===============
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2357
* Accuracy: 0.8936
* Balanced accuracy: 0.8897
* Precision: 0.8951
* Recall: 0.8936
* F1: 0.8915
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
null | transformers | # Description
This model is Part of the NLP assignment for Fatima Fellowship.
This model is a fine-tuned version of 'bert-base-uncased' on the below dataset: [Fake News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset).
It achieves the following results on the evaluation set:
- Acc... | {} | rematchka/Bert_fake_news_detection | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T18:25:47+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| # Description
This model is Part of the NLP assignment for Fatima Fellowship.
This model is a fine-tuned version of 'bert-base-uncased' on the below dataset: Fake News Dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.995
- Precision: 0.995
- Recall: 0.995
- F_score: 0.995
# Labels
Fa... | [
"# Description\nThis model is Part of the NLP assignment for Fatima Fellowship.\n\nThis model is a fine-tuned version of 'bert-base-uncased' on the below dataset: Fake News Dataset. \n\nIt achieves the following results on the evaluation set:\n- Accuracy: 0.995\n- Precision: 0.995\n- Recall: 0.995\n- F_score: 0.995... | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# Description\nThis model is Part of the NLP assignment for Fatima Fellowship.\n\nThis model is a fine-tuned version of 'bert-base-uncased' on the below dataset: Fake News Dataset. \n\nIt achieves the following results on the evaluation se... |
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/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-marknorm-timjdillon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T18:39:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Tim Dillon & mark normand
@elonmusk-marknorm-timjdillon
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, chec... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | {} | vocab-transformers/distilbert-tokenizer_256k-MLM_1M | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T19:03:35+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | [
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this da... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was tra... |
text-generation | transformers |
GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained
on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX
library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally
resembles that of GPT-3, and is almost identical to that of [GPT-J-
6B](https://huggingface.co/El... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["EleutherAI/pile"]} | EleutherAI/gpt-neox-20b | null | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2204.06745",
"arxiv:2101.00027",
"arxiv:2201.07311",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"t... | null | 2022-04-07T19:28:29+00:00 | [
"2204.06745",
"2101.00027",
"2201.07311",
"2104.09864"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #causal-lm #en #dataset-EleutherAI/pile #arxiv-2204.06745 #arxiv-2101.00027 #arxiv-2201.07311 #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained
on the Pile using the GPT-NeoX
library. Its architecture intentionally
resembles that of GPT-3, and is almost identical to that of GPT-J-
6B. Its training dataset contains
a multitude of English-language texts, reflecting the general-purpose n... | [
"### Model details\n\n\n* Developed by: EleutherAI\n* Model type: Transformer-based Language Model\n* Language: English\n* Learn more: GPT-NeoX-20B: An Open-Source Autoregressive Language\nModel. For details about the training dataset,\nsee the Pile paper, and its data\nsheet.\n* License: Apache 2.0\n* Contact: to ... | [
"TAGS\n#transformers #pytorch #safetensors #gpt_neox #text-generation #causal-lm #en #dataset-EleutherAI/pile #arxiv-2204.06745 #arxiv-2101.00027 #arxiv-2201.07311 #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Model det... |
null | null | Basic CNN to detect upside down images | {} | UlkuTuncer/Upsidedowncnn | null | [
"region:us"
] | null | 2022-04-07T20:04:09+00:00 | [] | [] | TAGS
#region-us
| Basic CNN to detect upside down images | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | cj-mills/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 | 2022-04-07T20:11:21+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: 0.1319
* F1: 0.8576
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: 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\n* mixed\\_prec... | [
"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-generation | null |
# My Awesome Model of Eva | {"tags": ["conversational"]} | NUTELEX/Eva | null | [
"conversational",
"region:us"
] | null | 2022-04-07T20:18:28+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model of Eva | [
"# My Awesome Model of Eva"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model of Eva"
] |
question-answering | transformers | python run_squad.py
--model_name_or_path google/canine-s
--do_train
--do_eval
--per_gpu_train_batch_size 1
--per_gpu_eval_batch_size 1
--gradient_accumulation_steps 128
--learning_rate 3e-5
--num_train_epochs 3
--max_seq_length 1024
--doc_stride 128
--max_answer_length 240
--output_dir canine-s-squad
--model_type bert
... | {} | Splend1dchan/canine-s-squad | null | [
"transformers",
"pytorch",
"canine",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T22:36:48+00:00 | [] | [] | TAGS
#transformers #pytorch #canine #question-answering #endpoints_compatible #region-us
| python run_squad.py
--model_name_or_path google/canine-s
--do_train
--do_eval
--per_gpu_train_batch_size 1
--per_gpu_eval_batch_size 1
--gradient_accumulation_steps 128
--learning_rate 3e-5
--num_train_epochs 3
--max_seq_length 1024
--doc_stride 128
--max_answer_length 240
--output_dir canine-s-squad
--model_type bert
... | [] | [
"TAGS\n#transformers #pytorch #canine #question-answering #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/abovethebed | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T22:42:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
3bkreno
@abovethebed
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Ron Swanson DialoGPT Model | {"tags": ["conversational"]} | jessicammow/DialoGPT-small-ronswanson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-07T23:08:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ron Swanson DialoGPT Model | [
"# Ron Swanson DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ron Swanson DialoGPT Model"
] |
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_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | srmukundb/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-07T23:45:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4104
Model description
-----------------
More information needed
Intended u... | [
"### 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 #dataset-squad_v2 #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\... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | nateraw/lightweight-gan-test | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:22:20+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
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-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | cj-mills/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:23:56+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1580
* F1: 0.8547
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n*... |
null | null |
This repository contains model trained to predict orientation {0:'up', 1:'down'} of images.
The model '.pkl' file contains a dictionary with the following keys:
```python:
'optimizer': optimizer state dictionary
'scheduler': scheduler state dictionary
'model': model state dictionary
'epoch': checkpoint epoch
```
Th... | {"framework": "pytorch", "model": "EfficientNet-B1", "dataset": "CIFAR10 (restructured to have random upright and upside-down samples, with labels {0:'up', 1:'down'}", "imgSize": "224x224x3"} | TalalWasim/CIFAR10_UP_DOWN_Classifier | null | [
"region:us"
] | null | 2022-04-08T00:25:31+00:00 | [] | [] | TAGS
#region-us
|
This repository contains model trained to predict orientation {0:'up', 1:'down'} of images.
The model '.pkl' file contains a dictionary with the following keys:
The image size is 3x224x224. The model can be initialized as:
| [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
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-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | cj-mills/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:41:32+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
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: 0.2719
* F1: 0.8293
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: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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-it
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-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | cj-mills/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:49:20+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
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: 0.2928
* F1: 0.7730
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: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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-en
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-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | cj-mills/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:56:46+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
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: 0.5084
* F1: 0.5794
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: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-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-mic
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mic", "results": []}]} | agi-css/distilbert-base-uncased-finetuned-mic | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T00:57:30+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-mic
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5640
* Accuracy: 0.7809
* F1: 0.8769
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 400\n* eval\\_batch\\_size: 400\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | nateraw/lightweight-gan-pokemon | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T01:02:32+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
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-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | cj-mills/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T01:04:08+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1674
* F1: 0.8477
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: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n*... |
text-generation | transformers |
# Harry Potter2 DialoGPT Model | {"tags": ["conversational"]} | MrYiRen/DialoGPT-small-ZC | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T01:17:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter2 DialoGPT Model | [
"# Harry Potter2 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter2 DialoGPT Model"
] |
fill-mask | transformers |
# PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。
# cluner NER实验(globalpointer的结果差不多，softmax结果差好多- -)
```python
# flash base + globalpointer
04/08/2022 10:53:34 - INFO - __main__ - ADDRESS = Score(f1=0.607703, precision=0.64939, recall=0.571046, tp=213, pred=328, gold=373)
04/08/2022 10:53:34 - INFO - __main__ - BOOK = S... | {"license": "mit", "inference": false} | junnyu/flash_base_wwm_cluecorpussmall | null | [
"transformers",
"pytorch",
"flash",
"fill-mask",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-08T01:32:25+00:00 | [] | [] | TAGS
#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us
|
# PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。
# cluner NER实验(globalpointer的结果差不多，softmax结果差好多- -)
# cmeee + globalpointer
# install
- URL
# usage
| [
"# PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。",
"# cluner NER实验(globalpointer的结果差不多，softmax结果差好多- -)",
"# cmeee + globalpointer",
"# install\n- URL",
"# usage"
] | [
"TAGS\n#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us \n",
"# PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。",
"# cluner NER实验(globalpointer的结果差不多，softmax结果差好多- -)",
"# cmeee + globalpointer",
"# install\n- URL",
"# usage"
] |
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