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token-classification | transformers | # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned version of "xlm-roberta-base"
(a multilingual version of RoBERTa)
using a reviewed version of well known Turkish NER dataset
(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
# Fine-tuning parameters:
```
task = "ner"
mod... | {"language": "tr", "widget": [{"text": "Mustafa Kemal Atat\u00fcrk 19 May\u0131s 1919'da Samsun'a \u00e7\u0131kt\u0131."}]} | akdeniz27/xlm-roberta-base-turkish-ner | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #tr #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned version of "xlm-roberta-base"
(a multilingual version of RoBERTa)
using a reviewed version of well known Turkish NER dataset
(URL
# Fine-tuning parameters:
# How to use:
Pls refer "URL for entity grouping with aggregation_strategy paramete... | [
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned version of \"xlm-roberta-base\"\n(a multilingual version of RoBERTa) \nusing a reviewed version of well known Turkish NER dataset \n(URL",
"# Fine-tuning parameters:",
"# How to use: \n\nPls refer \"URL for entity grouping with aggreg... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #tr #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned version of \"xlm-roberta-base\"\n(a multilingual version of RoBERTa) \nusing a revi... |
object-detection | null |
<div align="left">
## You Only Look Once for Panoptic Driving Perception
> [**You Only Look at Once for Panoptic driving Perception**](https://arxiv.org/abs/2108.11250)
>
> by Dong Wu, Manwen Liao, Weitian Zhang, [Xinggang Wang](https://xinggangw.info/) [*School of EIC, HUST*](http://eic.hust.edu.cn/English/... | {"tags": ["object-detection"]} | akhaliq/YOLOP | null | [
"object-detection",
"arxiv:2108.11250",
"arxiv:1612.07695",
"arxiv:1606.02147",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.11250",
"1612.07695",
"1606.02147"
] | [] | TAGS
#object-detection #arxiv-2108.11250 #arxiv-1612.07695 #arxiv-1606.02147 #region-us
|
You Only Look Once for Panoptic Driving Perception
----------------------------------------------------
>
> You Only Look at Once for Panoptic driving Perception
>
>
> by Dong Wu, Manwen Liao, Weitian Zhang, Xinggang Wang *School of EIC, HUST*
>
>
> *arXiv technical report (arXiv 2108.11250)*
>
>
>
--... | [
"### The Illustration of YOLOP\n\n\n!yolop",
"### Contributions\n\n\n* We put forward an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection to save computational costs, reduce inference time as well as imp... | [
"TAGS\n#object-detection #arxiv-2108.11250 #arxiv-1612.07695 #arxiv-1606.02147 #region-us \n",
"### The Illustration of YOLOP\n\n\n!yolop",
"### Contributions\n\n\n* We put forward an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area ... |
text-generation | transformers |
# GPT2-Small-Arabic-Poetry
## Model description
Fine-tuned model of Arabic poetry dataset based on gpt2-small-arabic.
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](https://colab.research.google.com/drive/1mRl7c-5v-Klx27EEAEOAbrfkustL4g7a?usp=sharing).
#### Limitat... | {"language": "ar", "tags": ["text-generation"], "datasets": ["Arabic poetry from several eras"]} | akhooli/gpt2-small-arabic-poetry | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2-Small-Arabic-Poetry
## Model description
Fine-tuned model of Arabic poetry dataset based on gpt2-small-arabic.
## Intended uses & limitations
#### How to use
An example is provided in this colab notebook.
#### Limitations and bias
Both the GPT2-small-arabic (trained on Arabic Wikipedia) and this model ha... | [
"# GPT2-Small-Arabic-Poetry",
"## Model description\n\nFine-tuned model of Arabic poetry dataset based on gpt2-small-arabic.",
"## Intended uses & limitations",
"#### How to use\n\nAn example is provided in this colab notebook.",
"#### Limitations and bias\n\nBoth the GPT2-small-arabic (trained on Arabic Wi... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Small-Arabic-Poetry",
"## Model description\n\nFine-tuned model of Arabic poetry dataset based on gpt2-small-arabic.",
"## Intend... |
text-generation | transformers |
# GPT2-Small-Arabic
## Model description
GPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](https://colab.research.google.com/drive/1mRl7c-5v-Klx27EEAEOAbrfkustL4g7a?usp=sharing).
Both text a... | {"language": "ar", "datasets": ["Arabic Wikipedia"], "metrics": ["none"]} | akhooli/gpt2-small-arabic | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2-Small-Arabic
## Model description
GPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).
## Intended uses & limitations
#### How to use
An example is provided in this colab notebook.
Both text and poetry (fine-tuned model) generation are included.
#### Limitations and bias
GPT2-sm... | [
"# GPT2-Small-Arabic",
"## Model description\n\nGPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).",
"## Intended uses & limitations",
"#### How to use\n\nAn example is provided in this colab notebook. \nBoth text and poetry (fine-tuned model) generation are included.",
"#### Limi... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Small-Arabic",
"## Model description\n\nGPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).",
"## Inten... |
translation | transformers | ### mbart-large-ar-en
This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en.
Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing)
Note: model has limited training set, not fully trained (do not use for production).
Other mode... | {"language": ["ar", "en"], "license": "mit", "tags": ["translation"]} | akhooli/mbart-large-cc25-ar-en | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"ar",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #ar #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-ar-en
This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en.
Usage: see example notebook
Note: model has limited training set, not fully trained (do not use for production).
Other models by me: Abed Khooli
| [
"### mbart-large-ar-en\nThis is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en. \nUsage: see example notebook \nNote: model has limited training set, not fully trained (do not use for production). \nOther models by me: Abed Khooli"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #ar #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### mbart-large-ar-en\nThis is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en. \nUsage: see example notebook \nNote: model has limited ... |
translation | transformers | ### mbart-large-en-ar
This is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar.
Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing)
Note: model has limited training set, not fully trained (do not use for production).
| {"language": ["en", "ar"], "license": "mit", "tags": ["translation"]} | akhooli/mbart-large-cc25-en-ar | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"en",
"ar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ar"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #en #ar #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-en-ar
This is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar.
Usage: see example notebook
Note: model has limited training set, not fully trained (do not use for production).
| [
"### mbart-large-en-ar\nThis is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar. \nUsage: see example notebook \nNote: model has limited training set, not fully trained (do not use for production)."
] | [
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"### mbart-large-en-ar\nThis is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar. \nUsage: see example notebook \nNote: model has limited trai... |
text-generation | transformers | ## personachat-arabic (conversational AI)
This is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English)
and fine-tuned from [akhooli/gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small-arabic) which is a limited text generation model.
Usage:... | {"language": ["ar"], "license": "mit", "tags": ["conversational"]} | akhooli/personachat-arabic | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"conversational",
"ar",
"license:mit",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #conversational #ar #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us
| ## personachat-arabic (conversational AI)
This is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English)
and fine-tuned from akhooli/gpt2-small-arabic which is a limited text generation model.
Usage: see the last section of this example notebook
Note... | [
"## personachat-arabic (conversational AI)\nThis is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English) \nand fine-tuned from akhooli/gpt2-small-arabic which is a limited text generation model. \nUsage: see the last section of this example notebo... | [
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"## personachat-arabic (conversational AI)\nThis is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabi... |
text-classification | transformers | ### xlm-r-large-arabic-sent
Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may confus... | {"language": ["ar", "en", "multilingual"], "license": "mit"} | akhooli/xlm-r-large-arabic-sent | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"ar",
"en",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #ar #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### xlm-r-large-arabic-sent
Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may confus... | [
"### xlm-r-large-arabic-sent \nMultilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large. \nZero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ar #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### xlm-r-large-arabic-sent \nMultilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tunin... |
text-classification | transformers | ### xlm-r-large-arabic-toxic (toxic/hate speech classifier)
Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English).
Usage and further info: see las... | {"language": ["ar", "en"], "license": "mit"} | akhooli/xlm-r-large-arabic-toxic | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"ar",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #ar #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### xlm-r-large-arabic-toxic (toxic/hate speech classifier)
Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English).
Usage and further info: see las... | [
"### xlm-r-large-arabic-toxic (toxic/hate speech classifier) \nToxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large. \nZero shot classification of other languages (also works in mixed languages - ex. Arabic & English). \nUsage and further info:... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ar #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### xlm-r-large-arabic-toxic (toxic/hate speech classifier) \nToxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529614927
- CO2 Emissions (in grams): 5.999771405025692
## Validation Metrics
- Loss: 0.7582379579544067
- Accuracy: 0.7636103151862464
- Macro F1: 0.770630619486531
- Micro F1: 0.7636103151862464
- Weighted F1: 0.765233270165301
-... | {"language": "en", "tags": "autonlp", "datasets": ["akilesh96/autonlp-data-mrcooper_text_classification"], "widget": [{"text": "Not Many People Know About The City 1200 Feet Below Detroit"}, {"text": "Bob accepts the challenge, and the next week they're standing in Saint Peters square. 'This isnt gonna work, he's never... | akilesh96/autonlp-mrcooper_text_classification-529614927 | null | [
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"bert",
"text-classification",
"autonlp",
"en",
"dataset:akilesh96/autonlp-data-mrcooper_text_classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-akilesh96/autonlp-data-mrcooper_text_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529614927
- CO2 Emissions (in grams): 5.999771405025692
## Validation Metrics
- Loss: 0.7582379579544067
- Accuracy: 0.7636103151862464
- Macro F1: 0.770630619486531
- Micro F1: 0.7636103151862464
- Weighted F1: 0.765233270165301
-... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 529614927\n- CO2 Emissions (in grams): 5.999771405025692",
"## Validation Metrics\n\n- Loss: 0.7582379579544067\n- Accuracy: 0.7636103151862464\n- Macro F1: 0.770630619486531\n- Micro F1: 0.7636103151862464\n- Weighted F1: 0... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-akilesh96/autonlp-data-mrcooper_text_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 529614927\n- CO... |
text-generation | transformers | hello
| {} | akozlo/con_bal60k | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# conserv_fulltext_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Mode... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "conserv_fulltext_model", "results": []}]} | akozlo/conserv_fulltext_1_18_22 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# conserv_fulltext_model
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# conserv_fulltext_model\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
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"# conserv_fulltext_model\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informat... |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-bert
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-bert | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-gpt2
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-gpt2 | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #endpoints_compatible #text-generation-inference #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mbart
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-mbart | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mbart #endpoints_compatible #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #mbart #endpoints_compatible #region-us \n"
] |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mpnet
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-mpnet | null | [
"transformers",
"pytorch",
"tf",
"mpnet",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mpnet #endpoints_compatible #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #mpnet #endpoints_compatible #region-us \n"
] |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-t5
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-t5 | null | [
"transformers",
"pytorch",
"tf",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #endpoints_compatible #text-generation-inference #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-xlnet
Changes: use old format for `pytorch_model.bin`.
| {} | akreal/tiny-random-xlnet | null | [
"transformers",
"pytorch",
"tf",
"xlnet",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #xlnet #endpoints_compatible #region-us
| This is a copy of: URL
Changes: use old format for 'pytorch_model.bin'.
| [] | [
"TAGS\n#transformers #pytorch #tf #xlnet #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Matthews Correlation", "type": "matthews_correlat... | akshara23/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0475
* Matthews Correlation: 0.6290
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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-cloud-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cloud-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-cloud-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cloud-ner
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0812
* Precision: 0.8975
* Recall: 0.9080
* F1: 0.9027
* Accuracy: 0.9703
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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: 3e-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. -->
# distilbert-base-uncased-finetuned-cloud1-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cloud1-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-cloud1-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cloud1-ner
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0074
* Precision: 0.9714
* Recall: 0.9855
* F1: 0.9784
* Accuracy: 0.9972
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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: 3e-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. -->
# distilbert-base-uncased-finetuned-cloud2-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cloud2-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-cloud2-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cloud2-ner
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8866
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.8453
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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: 3e-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. -->
# distilbert-base-uncased-finetuned-hypertuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-hypertuned-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-hypertuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-hypertuned-ner
================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5683
* Precision: 0.3398
* Recall: 0.6481
* F1: 0.4459
* Accuracy: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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: 3e-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. -->
# 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"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9988
* Precision: 0.3
* Recall: 0.6
* F1: 0.4
* Accuracy: 0.7870
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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. -->
# 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": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | al00014/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 0.0611
* Precision: 0.9250
* Recall: 0.9321
* F1: 0.9285
* Accuracy: 0.9834
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
text2text-generation | transformers |
# BART Pretrained
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 BART Pretrain 단계를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약](https://aihub.or.kr/aidata/30714) 데이터를 사용하였습니다. | {"language": ["ko"], "widget": [{"text": "[BOS]\ubb50 \ud574?[SEP][MASK]\ud558\ub2e4\uac00 \uc774\uc81c [MASK]\ub824\uace0[EOS]"}], "inference": {"parameters": {"max_length": 64}}} | alaggung/bart-pretrained | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
|
# BART Pretrained
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
2021-dialogue-summary-competition 레포지토리의 BART Pretrain 단계를 학습한 모델입니다.
데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다. | [
"# BART Pretrained\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\n2021-dialogue-summary-competition 레포지토리의 BART Pretrain 단계를 학습한 모델입니다.\n\n데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다."
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART Pretrained\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\n2021-dialogue-summary-competition 레포지토리의 BART Pretrain 단계를 학습한 모델입니다.\n\n데이터는 AIHub 한국어 대화... |
summarization | transformers |
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[bart-pretrained](https://huggingface.co/alaggung/bart-pretrained) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약... | {"language": ["ko"], "tags": ["summarization"], "widget": [{"text": "[BOS]\ubc25 \u3131?[SEP]\uace0\uace0\uace0\uace0 \ubb50 \uba39\uc744\uae4c?[SEP]\uc5b4\uc81c \uae40\uce58\ucc0c\uac1c \uba39\uc5b4\uc11c \ud55c\uc2dd\ub9d0\uace0 \ub534 \uac70[SEP]\uadf8\ub7fc \ub3c8\uae4c\uc2a4 \uc5b4\ub54c?[SEP]\uc624 \uc88b\ub2e4 1... | alaggung/bart-r3f | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"summarization",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #summarization #ko #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
bart-pretrained 모델에 2021-dialogue-summary-competition 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다. | [
"# BART R3F\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\nbart-pretrained 모델에 2021-dialogue-summary-competition 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다.\n\n데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다."
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #summarization #ko #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BART R3F\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\nbart-pretrained 모델에 2021-dialogue-summary-competition 레포지토리의 R3F를 적용해 대... |
summarization | transformers |
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[bart-r3f](https://huggingface.co/alaggung/bart-r3f) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 RL 기법을 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약](https://ai... | {"language": ["ko"], "tags": ["summarization"], "widget": [{"text": "[BOS]\ubc25 \u3131?[SEP]\uace0\uace0\uace0\uace0 \ubb50 \uba39\uc744\uae4c?[SEP]\uc5b4\uc81c \uae40\uce58\ucc0c\uac1c \uba39\uc5b4\uc11c \ud55c\uc2dd\ub9d0\uace0 \ub534 \uac70[SEP]\uadf8\ub7fc \ub3c8\uae4c\uc2a4 \uc5b4\ub54c?[SEP]\uc624 \uc88b\ub2e4 1... | alaggung/bart-rl | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"summarization",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #summarization #ko #autotrain_compatible #endpoints_compatible #region-us
|
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
bart-r3f 모델에 2021-dialogue-summary-competition 레포지토리의 RL 기법을 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다.
| [
"# BART R3F\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\nbart-r3f 모델에 2021-dialogue-summary-competition 레포지토리의 RL 기법을 적용해 대화요약 Task를 학습한 모델입니다.\n\n데이터는 AIHub 한국어 대화요약 데이터를 사용하였습니다."
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #summarization #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART R3F\n\n[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.\n\nbart-r3f 모델에 2021-dialogue-summary-competition 레포지토리의 RL 기법을 적용해 대화요약 Task를 학습한 모델... |
text2text-generation | transformers |
# mt5-large-finetuned-mnli-xtreme-xnli
## Model Description
This model takes a pretrained large [multilingual-t5](https://github.com/google-research/multilingual-t5) (also available from [models](https://huggingface.co/google/mt5-large)) and fine-tunes it on English MNLI and the [xtreme_xnli](https://www.tensorflow... | {"language": ["multilingual", "en", "fr", "es", "de", "el", "bg", "ru", "tr", "ar", "vi", "th", "zh", "hi", "sw", "ur"], "license": "apache-2.0", "tags": ["pytorch"], "datasets": ["multi_nli", "xnli"], "metrics": ["xnli"]} | alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"mt5",
"text2text-generation",
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:2010.11934",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [
"2010.11934"
] | [
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur"
] | TAGS
#transformers #pytorch #tf #safetensors #mt5 #text2text-generation #multilingual #en #fr #es #de #el #bg #ru #tr #ar #vi #th #zh #hi #sw #ur #dataset-multi_nli #dataset-xnli #arxiv-2010.11934 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| mt5-large-finetuned-mnli-xtreme-xnli
====================================
Model Description
-----------------
This model takes a pretrained large multilingual-t5 (also available from models) and fine-tunes it on English MNLI and the xtreme\_xnli training set. It is intended to be used for zero-shot text classificat... | [
"### Zero-shot example:\n\n\nThe model retains its text-to-text characteristic after fine-tuning. This means that our expected outputs will be text. During fine-tuning, the model learns to respond to the NLI task with a series of single token responses that map to entailment, neutral, or contradiction. The NLI task... | [
"TAGS\n#transformers #pytorch #tf #safetensors #mt5 #text2text-generation #multilingual #en #fr #es #de #el #bg #ru #tr #ar #vi #th #zh #hi #sw #ur #dataset-multi_nli #dataset-xnli #arxiv-2010.11934 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",... |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | alankar/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model | [
"# Rick Sanchez DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 1311135
## Validation Metrics
- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro ... | {"language": "bn", "tags": "autonlp", "datasets": ["albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autonlp",
"bn",
"dataset:albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #albert #text-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 1311135
## Validation Metrics
- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 1311135",
"## Validation Metrics\n\n- Loss: 0.35616958141326904\n- Accuracy: 0.8979447200566973\n- Macro F1: 0.8545383956197669\n- Micro F1: 0.8979447200566975\n- Weighted F1: 0.8983951947775538\n- Macro Precision: 0.8615833... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 1311135"... |
token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 1301123
## Validation Metrics
- Loss: 0.14097803831100464
- Accuracy: 0.9740097463451206
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" ... | {"language": "bn", "tags": "autonlp", "datasets": ["albertvillanova/autonlp-data-wikiann-entity_extraction-1e67664"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | albertvillanova/autonlp-wikiann-entity_extraction-1e67664-1301123 | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"token-classification",
"autonlp",
"bn",
"dataset:albertvillanova/autonlp-data-wikiann-entity_extraction-1e67664",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #safetensors #albert #token-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-wikiann-entity_extraction-1e67664 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 1301123
## Validation Metrics
- Loss: 0.14097803831100464
- Accuracy: 0.9740097463451206
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 1301123",
"## Validation Metrics\n\n- Loss: 0.14097803831100464\n- Accuracy: 0.9740097463451206\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #safetensors #albert #token-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-wikiann-entity_extraction-1e67664 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 1301123",
"##... |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "clip", "emoji": "\ud83d\udc41", "colorFrom": "indigo", "colorTo": "blue", "sdk": "streamlit", "app_file": "app.py", "pinned": true} | allen0s/clip | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | [
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow,... | [
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbna... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736
## Validation Metrics
- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.770716086318180... | {"language": "en", "tags": "autonlp", "datasets": ["alecmullen/autonlp-data-group-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.4362732160754736} | alecmullen/autonlp-group-classification-441411446 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:alecmullen/autonlp-data-group-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-alecmullen/autonlp-data-group-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736
## Validation Metrics
- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.770716086318180... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 441411446\n- CO2 Emissions (in grams): 0.4362732160754736",
"## Validation Metrics\n\n- Loss: 0.7598486542701721\n- Accuracy: 0.8222222222222222\n- Macro F1: 0.2912091747693842\n- Micro F1: 0.8222222222222222\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-alecmullen/autonlp-data-group-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 441411446\n- CO2 Em... |
feature-extraction | transformers | ## Classifier to check if two sequences are paraphrase or not
Trained based on ruBert by DeepPavlov.
Use this way:
```
import torch
import torch.nn as nn
import os
import copy
import random
import numpy as np
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from torch.cuda.amp import autocast, Gra... | {} | alenusch/par_cls_bert | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| ## Classifier to check if two sequences are paraphrase or not
Trained based on ruBert by DeepPavlov.
Use this way:
| [
"## Classifier to check if two sequences are paraphrase or not\n\nTrained based on ruBert by DeepPavlov.\n\nUse this way:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n",
"## Classifier to check if two sequences are paraphrase or not\n\nTrained based on ruBert by DeepPavlov.\n\nUse this way:"
] |
feature-extraction | transformers | alex6095/SanctiMolyOH_Cpu | {} | alex6095/SanctiMolyOH_Cpu | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #has_space #region-us
| alex6095/SanctiMolyOH_Cpu | [] | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# DanBERT
## Model description
DanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2 million sentences and 40 millions, danish words. The training has been conducted as part of a thesis.
The model can be found at:
* [danbert-da](https://huggingface.co/alex... | {"language": ["da", "en"], "license": "apache-2.0", "tags": ["named entity recognition", "token criticality"], "datasets": ["custom danish dataset"], "metrics": ["array of metric identifiers"], "inference": false} | alexanderfalk/danbert-small-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"named entity recognition",
"token criticality",
"da",
"en",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da",
"en"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #named entity recognition #token criticality #da #en #license-apache-2.0 #autotrain_compatible #region-us
|
# DanBERT
## Model description
DanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2 million sentences and 40 millions, danish words. The training has been conducted as part of a thesis.
The model can be found at:
* danbert-da
## Intended uses & limitatio... | [
"# DanBERT",
"## Model description\n\nDanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2 million sentences and 40 millions, danish words. The training has been conducted as part of a thesis. \nThe model can be found at:\n\n* danbert-da",
"## Intended... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #named entity recognition #token criticality #da #en #license-apache-2.0 #autotrain_compatible #region-us \n",
"# DanBERT",
"## Model description\n\nDanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2... |
token-classification | transformers | # ArcheoBERTje-NER
A Dutch BERT model for Named Entity Recognition in the Archaeology domain
This is the [ArcheoBERTje](https://huggingface.co/alexbrandsen/ArcheoBERTje) model finetuned for NER, targeting the following entities:
- Time periods
- Places
- Artefacts
- Contexts
- Materials
- Species
| {} | alexbrandsen/ArcheoBERTje-NER | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # ArcheoBERTje-NER
A Dutch BERT model for Named Entity Recognition in the Archaeology domain
This is the ArcheoBERTje model finetuned for NER, targeting the following entities:
- Time periods
- Places
- Artefacts
- Contexts
- Materials
- Species
| [
"# ArcheoBERTje-NER\nA Dutch BERT model for Named Entity Recognition in the Archaeology domain\n\nThis is the ArcheoBERTje model finetuned for NER, targeting the following entities:\n\n- Time periods\n- Places\n- Artefacts\n- Contexts\n- Materials\n- Species"
] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# ArcheoBERTje-NER\nA Dutch BERT model for Named Entity Recognition in the Archaeology domain\n\nThis is the ArcheoBERTje model finetuned for NER, targeting the following entities:\n\n- Time... |
fill-mask | transformers | # ArcheoBERTje
A Dutch BERT model for the Archaeology domain
This model is based on the Dutch BERTje model by wietsedv (https://github.com/wietsedv/bertje).
We further finetuned BERTje with a corpus of roughly 60k Dutch excavation reports (~650 million tokens) from the DANS data archive (https://easy.dans.knaw.nl/ui... | {} | alexbrandsen/ArcheoBERTje | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ArcheoBERTje
A Dutch BERT model for the Archaeology domain
This model is based on the Dutch BERTje model by wietsedv (URL
We further finetuned BERTje with a corpus of roughly 60k Dutch excavation reports (~650 million tokens) from the DANS data archive (URL | [
"# ArcheoBERTje\nA Dutch BERT model for the Archaeology domain\n\nThis model is based on the Dutch BERTje model by wietsedv (URL \n\nWe further finetuned BERTje with a corpus of roughly 60k Dutch excavation reports (~650 million tokens) from the DANS data archive (URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ArcheoBERTje\nA Dutch BERT model for the Archaeology domain\n\nThis model is based on the Dutch BERTje model by wietsedv (URL \n\nWe further finetuned BERTje with a corpus of roughly 60k Dutch excava... |
automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-polish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Polish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | {"language": "pl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2vec2 Large 53 Polish by Alex Leu", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"... | alexcleu/wav2vec2-large-xlsr-polish | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
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"xlsr-fine-tuning-week",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-polish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Polish using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on th... | [
"# wav2vec2-large-xlsr-polish\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Polish using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-polish\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Polish using t... |
text2text-generation | transformers | t5_boolq
| {} | alexcruz0202/t5_boolq | null | [
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"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5_boolq
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned-en-to-de | null | [
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"tensorboard",
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"text2text-generation",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-de
===========================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned128-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned128-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned128-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned128-en-to-de
This model is a fine-tuned version of t5-small on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# t5-small-finetuned128-en-to-de\n\nThis model is a fine-tuned version of t5-small on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned128-en-to-de\n\nThis model is a fine-tuned version of t5-small on the wmt16 da... |
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-finetuned16-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned16-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned16-en-to-de | null | [
"transformers",
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"tensorboard",
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"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned16-en-to-de
=============================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #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* ... |
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-finetuned300-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned300-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned300-en-to-de | null | [
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"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned300-en-to-de
==============================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #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* ... |
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-finetuned32-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned32-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned32-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned32-en-to-de
=============================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #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* ... |
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-finetuned8-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned8-en-to-de", "results": []}]} | alexrfelicio/t5-small-finetuned8-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned8-en-to-de
============================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #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* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# alexrink/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "alexrink/t5-small-finetuned-xsum", "results": []}]} | alexrink/t5-small-finetuned-xsum | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| alexrink/t5-small-finetuned-xsum
================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.6399
* Validation Loss: 6.0028
* Epoch: 19
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 0.2, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
fill-mask | transformers | Paper: https://arxiv.org/abs/2204.03951
Code: https://github.com/alexyalunin/RuBioRoBERTa | {} | alexyalunin/RuBioBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2204.03951",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2204.03951"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2204.03951 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Paper: URL
Code: URL | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-2204.03951 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | ### Contact
blinoff.pavel@gmail.com
https://t.me/pavel_blinoff
### Paper
https://arxiv.org/abs/2204.03951
### Code
https://github.com/alexyalunin/RuBioRoBERTa
### Citation
```
@misc{alex2022rubioroberta,
title={RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining},
... | {"language": ["ru"], "multilinguality": ["monolingual"], "widget": [{"text": "\u0416\u0430\u043b\u043e\u0431\u044b \u043d\u0430 \u0431\u043e\u043b\u044c \u0432\u043d\u0438\u0437\u0443 <mask> \u043f\u043e\u0441\u043b\u0435 \u043f\u0440\u0438\u0451\u043c\u0430 \u043f\u0438\u0449\u0438.", "example_title": "pain_example"},... | alexyalunin/RuBioRoBERTa | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ru",
"arxiv:2204.03951",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2204.03951"
] | [
"ru"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ru #arxiv-2204.03951 #autotrain_compatible #endpoints_compatible #region-us
| ### Contact
URL@URL
https://t.me/pavel_blinoff
### Paper
URL
### Code
URL
| [
"### Contact\n\nURL@URL\n\nhttps://t.me/pavel_blinoff",
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"### Paper\nURL",
"### Code\nURL"
] |
fill-mask | transformers | # RuBio
for paper: dsdfsfsdf | {} | alexyalunin/my-awesome-model | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # RuBio
for paper: dsdfsfsdf | [
"# RuBio\n\nfor paper: dsdfsfsdf"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# RuBio\n\nfor paper: dsdfsfsdf"
] |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/>
# DziriBERT
DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents wr... | {"language": ["ar", "dz"], "license": "apache-2.0", "tags": ["pytorch", "bert", "multilingual", "ar", "dz"], "widget": [{"text": " \u0623\u0646\u0627 \u0645\u0646 \u0627\u0644\u062c\u0632\u0627\u0626\u0631 \u0645\u0646 \u0648\u0644\u0627\u064a\u0629 [MASK] "}, {"text": "rabi [MASK] khouya sami"}, {"text": " \u0631\u062... | alger-ia/dziribert | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"ar",
"dz",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.12346"
] | [
"ar",
"dz"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #ar #dz #arxiv-2109.12346 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="drawing" width="25%" height="25%" align="right"/>
# DziriBERT
DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art re... | [
"# DziriBERT\n\n\nDziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has b... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #ar #dz #arxiv-2109.12346 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DziriBERT\n\n\nDziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for th... |
fill-mask | transformers | <p>Chinese Bert Large Model</p>
<p>bert large中文预训练模型</p>
#### 训练语料
中文wiki, 2018-2020海量新闻语料 | {} | algolet/bert-large-chinese | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| <p>Chinese Bert Large Model</p>
<p>bert large中文预训练模型</p>
#### 训练语料
中文wiki, 2018-2020海量新闻语料 | [
"#### 训练语料\n中文wiki, 2018-2020海量新闻语料"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### 训练语料\n中文wiki, 2018-2020海量新闻语料"
] |
text2text-generation | transformers | <h3 align="center">
<p>MT5 Base Model for Chinese Question Generation</p>
</h3>
<h3 align="center">
<p>基于mt5的中文问题生成任务</p>
</h3>
#### 可以通过安装question-generation包开始用
```
pip install question-generation
```
使用方法请参考github项目：https://github.com/algolet/question_generation
#### 在线使用
可以直接在线使用我们的模型：https://www.algolet.... | {} | algolet/mt5-base-chinese-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <h3 align="center">
<p>MT5 Base Model for Chinese Question Generation</p>
</h3>
<h3 align="center">
<p>基于mt5的中文问题生成任务</p>
</h3>
#### 可以通过安装question-generation包开始用
使用方法请参考github项目：URL
#### 在线使用
可以直接在线使用我们的模型：URL
#### 通过transformers调用
#### 指标
rouge-1: 0.4041
rouge-2: 0.2104
rouge-l: 0.3843
---
language:... | [
"#### 可以通过安装question-generation包开始用\n\n使用方法请参考github项目：URL",
"#### 在线使用\n可以直接在线使用我们的模型：URL",
"#### 通过transformers调用",
"#### 指标\nrouge-1: 0.4041\n\nrouge-2: 0.2104\n\nrouge-l: 0.3843\n\n---\nlanguage: \n - zh\n \ntags:\n- mt5\n- question generation\n\nmetrics:\n- rouge\n\n---"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"#### 可以通过安装question-generation包开始用\n\n使用方法请参考github项目：URL",
"#### 在线使用\n可以直接在线使用我们的模型：URL",
"#### 通过transformers调用",
"#### 指标\nrouge-1: 0.4041\n\nrouge-2: 0.2104\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-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27
This model is a fine-tuned version of [bert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27", "results": []}]} | ali2066/bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased\_token\_itr0\_0.0001\_all\_01\_03\_2022-04\_48\_27
====================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2899
* Precision: 0.3170
* Recall:... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25
This model is a fine-tuned version of [bert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25", "results": []}]} | ali2066/bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased\_token\_itr0\_0.0001\_all\_01\_03\_2022-14\_21\_25
====================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2698
* Precision: 0.3321
* Recall:... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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-uncased_token_itr0_2e-05_all_01_03_2022-04_40_10
This model is a fine-tuned version of [bert-base-uncased](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased_token_itr0_2e-05_all_01_03_2022-04_40_10", "results": []}]} | ali2066/bert-base-uncased_token_itr0_2e-05_all_01_03_2022-04_40_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased\_token\_itr0\_2e-05\_all\_01\_03\_2022-04\_40\_10
===================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2741
* Precision: 0.1936
* Recall: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15
This model is a fine-tuned version of [bert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15", "results": []}]} | ali2066/bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_base\_uncased\_itr0\_0.0001\_all\_01\_03\_2022-14\_08\_15
===============================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7632
* Accuracy: 0.8263
* F1: 0.8871
* Preci... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 0.0001\n* train\\_batch\... |
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. -->
# correct_BERT_token_itr0_0.0001_all_01_03_2022-15_52_19
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_BERT_token_itr0_0.0001_all_01_03_2022-15_52_19", "results": []}]} | ali2066/correct_BERT_token_itr0_0.0001_all_01_03_2022-15_52_19 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_BERT\_token\_itr0\_0.0001\_all\_01\_03\_2022-15\_52\_19
================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2711
* Precision: 0.3373
* Recall: 0.5670
... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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. -->
# correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21
This model is a fine-tuned version of [bert-base-uncased](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21", "results": []}]} | ali2066/correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_BERT\_token\_itr0\_0.0001\_editorials\_01\_03\_2022-15\_50\_21
=======================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1059
* Precision: 0.0637
* R... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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. -->
# correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47
This model is a fine-tuned version of [bert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47", "results": []}]} | ali2066/correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_BERT\_token\_itr0\_0.0001\_essays\_01\_03\_2022-15\_48\_47
===================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1801
* Precision: 0.6153
* Recall: 0... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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. -->
# correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14
This model is a fine-tuned version of [bert-base-uncased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14", "results": []}]} | ali2066/correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_BERT\_token\_itr0\_0.0001\_webDiscourse\_01\_03\_2022-15\_47\_14
=========================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6542
* Precision: 0.0092... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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. -->
# correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47
This model is a fine-tuned version of [distilbert-base-uncased-finet... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47", "results": []}]} | ali2066/correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_distilBERT\_token\_itr0\_1e-05\_all\_01\_03\_2022-15\_43\_47
=====================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.33... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32
This model is a fine-tuned version of [distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32", "results": []}]} | ali2066/correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_distilBERT\_token\_itr0\_1e-05\_editorials\_01\_03\_2022-15\_42\_32
============================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29
This model is a fine-tuned version of [distilbert-base-uncased-fi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29", "results": []}]} | ali2066/correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_distilBERT\_token\_itr0\_1e-05\_essays\_01\_03\_2022-15\_41\_29
========================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24
This model is a fine-tuned version of [distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24", "results": []}]} | ali2066/correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| correct\_distilBERT\_token\_itr0\_1e-05\_webDiscourse\_01\_03\_2022-15\_40\_24
==============================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation s... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04
This model is a fine-tuned version of [cardiffnlp/twitter-rober... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04", "results": []}]} | ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| correct\_twitter\_RoBERTa\_token\_itr0\_1e-05\_all\_01\_03\_2022-15\_36\_04
===========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2876
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eva... |
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. -->
# correct_twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_33_51
This model is a fine-tuned version of [cardiffnlp/twitte... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_33_51", "results": []}]} | ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_33_51 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| correct\_twitter\_RoBERTa\_token\_itr0\_1e-05\_editorials\_01\_03\_2022-15\_33\_51
==================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eva... |
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. -->
# correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16
This model is a fine-tuned version of [cardiffnlp/twitter-ro... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16", "results": []}]} | ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| correct\_twitter\_RoBERTa\_token\_itr0\_1e-05\_essays\_01\_03\_2022-15\_32\_16
==============================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eva... |
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. -->
# correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39
This model is a fine-tuned version of [cardiffnlp/twit... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39", "results": []}]} | ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| correct\_twitter\_RoBERTa\_token\_itr0\_1e-05\_webDiscourse\_01\_03\_2022-15\_30\_39
====================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eva... |
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_token_itr0_0.0001_all_01_03_2022-15_22_12
This model is a fine-tuned version of [bert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12", "results": []}]} | ali2066/distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_0.0001\_all\_01\_03\_2022-15\_22\_12
=============================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2811
* Precision: 0.3231
* Recall: 0.5151
* F1: ... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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_token_itr0_0.0001_editorials_01_03_2022-15_20_12
This model is a fine-tuned version of [bert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_0.0001_editorials_01_03_2022-15_20_12", "results": []}]} | ali2066/distilBERT_token_itr0_0.0001_editorials_01_03_2022-15_20_12 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_0.0001\_editorials\_01\_03\_2022-15\_20\_12
====================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1290
* Precision: 0.0637
* Recall:... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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_token_itr0_0.0001_essays_01_03_2022-15_18_35
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35", "results": []}]} | ali2066/distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_0.0001\_essays\_01\_03\_2022-15\_18\_35
================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1832
* Precision: 0.6138
* Recall: 0.7169
... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57
This model is a fine-tuned version of [bert-base-uncased](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57", "results": []}]} | ali2066/distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_0.0001\_webDiscourse\_01\_03\_2022-15\_16\_57
======================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5923
* Precision: 0.0039
* Rec... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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_token_itr0_1e-05_all_01_03_2022-15_14_04
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04", "results": []}]} | ali2066/distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_1e-05\_all\_01\_03\_2022-15\_14\_04
============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3121
* Precision: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_12_47
This model is a fine-tuned version of [distilbert-base-uncased-finetu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_12_47", "results": []}]} | ali2066/distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_12_47 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_1e-05\_editorials\_01\_03\_2022-15\_12\_47
===================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1194
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44", "results": []}]} | ali2066/distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_1e-05\_essays\_01\_03\_2022-15\_11\_44
===============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3082
* Precisi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39
This model is a fine-tuned version of [distilbert-base-uncased-fine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39", "results": []}]} | ali2066/distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilBERT\_token\_itr0\_1e-05\_webDiscourse\_01\_03\_2022-15\_10\_39
=====================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.58... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# distilbert_token_itr0_0.0001_all_01_03_2022-14_30_58
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-ss... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert_token_itr0_0.0001_all_01_03_2022-14_30_58", "results": []}]} | ali2066/distilbert_token_itr0_0.0001_all_01_03_2022-14_30_58 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_token\_itr0\_0.0001\_all\_01\_03\_2022-14\_30\_58
=============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2572
* Precision: ... | [
"### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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. -->
# distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33", "results": []}]} | ali2066/distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_token\_itr0\_1e-05\_all\_01\_03\_2022-14\_33\_33
============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3255
* Precision: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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. -->
# finetuned-token-argumentative
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "finetuned-token-argumentative", "results": []}]} | ali2066/finetuned-token-argumentative | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-token-argumentative
=============================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1573
* Precision: 0.3777
* Recall: 0.3919
* F1: 0.3847
* Accuracy: 0.9497
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
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. -->
# finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_all\_27\_02\_2022-17\_55\_43
===============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7600
* Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_all\_27\_02\_2022-19\_11\_17
===============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4064
* Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_all\_27\_02\_2022-22\_30\_53
===============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3825
* Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36
This model is a fine-tuned version of [distilbert-base-uncased-fin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_editorials\_27\_02\_2022-19\_42\_36
======================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10
This model is a fine-tuned version of [distilbert-base-uncased-finetun... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_essays\_27\_02\_2022-19\_33\_10
==================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3358
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06
This model is a fine-tuned version of [distilbert-base-uncased-f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06", "results": []}]} | ali2066/finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_0.0002\_webDiscourse\_27\_02\_2022-19\_25\_06
========================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 5",
"### Train... | [
"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: 0.0002\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32", "results": []}]} | ali2066/finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_1e-05\_all\_01\_03\_2022-13\_25\_32
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4787
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51
This model is a fine-tuned version of [siebert/sentiment-roberta-large-eng... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_01\_03\_2022-02\_53\_51
==============================================================
This model is a fine-tuned version of siebert/sentiment-roberta-large-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4563
* Accuracy: 0.8440
... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval... |
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. -->
# finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_01\_03\_2022-05\_32\_03
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4208
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_01\_03\_2022-13\_11\_55
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6168
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_26\_02\_2022-03\_57\_45
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4345
* Accuracy:... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_27\_02\_2022-17\_27\_47
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5002
* Accuracy:... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_27\_02\_2022-19\_05\_42
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4917
* Accuracy:... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_all\_27\_02\_2022-22\_25\_09
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4638
* Accuracy:... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42
This model is a fine-tuned version of [distilbert-base-uncased-fine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_editorials\_27\_02\_2022-19\_38\_42
=====================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.09... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22
This model is a fine-tuned version of [distilbert-base-uncased-finetune... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_essays\_27\_02\_2022-19\_30\_22
=================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3455
* Acc... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55
This model is a fine-tuned version of [distilbert-base-uncased-fi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_webDiscourse\_01\_03\_2022-13\_17\_55
=======================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55
This model is a fine-tuned version of [distilbert-base-uncased-fi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_webDiscourse\_27\_02\_2022-18\_51\_55
=======================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: ... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29
This model is a fine-tuned version of [distilbert-base-uncased-fi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29", "results": []}]} | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned\_sentence\_itr0\_2e-05\_webDiscourse\_27\_02\_2022-19\_22\_29
=======================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: ... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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