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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | **Usage HuggingFace Transformers for question generation task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-QuestionGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
inp... | {} | AlekseyKulnevich/Pegasus-QuestionGeneration | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Usage HuggingFace Transformers for question generation task
Decoder configuration examples:
Input text you can see here
output:
1. *What is the impact of human induced climate change on tropical cyclones?*
2. *What is the impact of climate change on tropical cyclones?*
3. *What is the impact of human induced c... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
30
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | **Usage HuggingFace Transformers for summarization task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-Summarization")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ = token... | {} | AlekseyKulnevich/Pegasus-Summarization | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Usage HuggingFace Transformers for summarization task
Decoder configuration examples:
Input text you can see here
output:
1. *global warming will expand the range of tropical cyclones in the mid-latitudes of the world, according to a new study published by the Intergovernmental Panel on Climate change (IPCC) and... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
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30
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"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
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text-generation | transformers |
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
| {"language": "en", "tags": ["text-generation"], "pipeline_tag": "text-generation", "widget": [{"text": "The Gods"}, {"text": "What is"}]} | Alerosae/SocratesGPT-2 | null | [
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"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"en",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #text-generation #en #endpoints_compatible #text-generation-inference #region-us
|
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
| [] | [
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question-answering | transformers | # XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
[SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read!
## Evaluat... | {"language": ["en", "ru", "multilingual"], "license": "apache-2.0"} | AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | null | [
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"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1912.09723"
] | [
"en",
"ru",
"multilingual"
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#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| # XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
SberQuAD original paper is here! Recommend to read!
## Evaluation results
The results obtained are the... | [
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"## Evaluation results\nThe resul... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-compression-roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unk... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentence-compression-roberta", "results": []}]} | AlexMaclean/sentence-compression-roberta | null | [
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"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentence-compression-roberta
============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3465
* Accuracy: 0.8473
* F1: 0.6835
* Precision: 0.6835
* Recall: 0.6835
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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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. -->
# sentence-compression
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentence-compression", "results": []}]} | AlexMaclean/sentence-compression | null | [
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#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sentence-compression
====================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2973
* Accuracy: 0.8912
* F1: 0.8367
* Precision: 0.8495
* Recall: 0.8243
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-fr", "results": [{"task": {"type... | AlexN/xls-r-300m-fr-0 | null | [
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"hf-asr-leaderboard",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #fr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2388
* Wer: 0.3681
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["fr"], "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-fr", "results": [{"task": {"type": "automatic-speech-reco... | AlexN/xls-r-300m-fr | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
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"robust-speech-event",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #fr #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# \n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-pt", "results": [{"task": {"type... | AlexN/xls-r-300m-pt | null | [
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"automatic-speech-recognition",
"robust-speech-event",
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"hf-asr-leaderboard",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #pt #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2290
* Wer: 0.2382
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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* lr\\_scheduler\\_warmup\\_step... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cola
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE COLA dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "col... | Alireza1044/albert-base-v2-cola | null | [
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"tensorboard",
"albert",
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"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# cola
This model is a fine-tuned version of albert-base-v2 on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7552
- Matthews Correlation: 0.5495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evalua... | [
"# cola\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE COLA dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7552\n- Matthews Correlation: 0.5495",
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"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "mnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metric... | Alireza1044/albert-base-v2-mnli | null | [
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"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mnli
This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
M... | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mrpc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MRPC dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "mrpc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "... | Alireza1044/albert-base-v2-mrpc | null | [
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# mrpc
This model is a fine-tuned version of albert-base-v2 on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4171
- Accuracy: 0.8627
- F1: 0.9011
- Combined Score: 0.8819
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# mrpc\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4171\n- Accuracy: 0.8627\n- F1: 0.9011\n- Combined Score: 0.8819",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "qnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metric... | Alireza1044/albert-base-v2-qnli | null | [
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|
# qnli
This model is a fine-tuned version of albert-base-v2 on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.9138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
M... | [
"# qnli\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE QNLI dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3608\n- Accuracy: 0.9138",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qqp
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QQP dataset.
It ac... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "qqp", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "met... | Alireza1044/albert-base-v2-qqp | null | [
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# qqp
This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886
## Model description
More information needed
## Intended uses & limitations
More information needed... | [
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"## Intended uses & limitations\n\nMo... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rte
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE RTE dataset.
It ac... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "rte", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metric": ... | Alireza1044/albert-base-v2-rte | null | [
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# rte
This model is a fine-tuned version of albert-base-v2 on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7994
- Accuracy: 0.6859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mor... | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sst2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE SST2 dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metric... | Alireza1044/albert-base-v2-sst2 | null | [
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# sst2
This model is a fine-tuned version of albert-base-v2 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3808
- Accuracy: 0.9232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
M... | [
"# sst2\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE SST2 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3808\n- Accuracy: 0.9232",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stsb
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE STSB dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model_index": [{"name": "stsb", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "args": "stsb"}, "metri... | Alireza1044/albert-base-v2-stsb | null | [
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|
# stsb
This model is a fine-tuned version of albert-base-v2 on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3978
- Pearson: 0.9090
- Spearmanr: 0.9051
- Combined Score: 0.9071
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# stsb\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE STSB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3978\n- Pearson: 0.9090\n- Spearmanr: 0.9051\n- Combined Score: 0.9071",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset.
It ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "wnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "metric... | Alireza1044/albert-base-v2-wnli | null | [
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|
# wnli
This model is a fine-tuned version of albert-base-v2 on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
M... | [
"# wnli\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE WNLI dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6898\n- Accuracy: 0.5634",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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text-classification | transformers | A simple model trained on dialogues of characters in NBC series, `The Office`. The model can do a binary classification between `Michael Scott` and `Dwight Shrute`'s dialogues.
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#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A simple model trained on dialogues of characters in NBC series, 'The Office'. The model can do a binary classification between 'Michael Scott' and 'Dwight Shrute''s dialogues.
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text-generation | transformers |
#HarryBoy | {"tags": ["conversational"]} | AllwynJ/HarryBoy | null | [
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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. -->
# mbart50-ft-si-en
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "mbart50-ft-si-en", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]} | Aloka/mbart50-ft-si-en | null | [
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| mbart50-ft-si-en
================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0476
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Tr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | Alstractor/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7272
* Matthews Correlation: 0.5343
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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null | transformers |
# Wav2vec2-base for Danish
This wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.
This model was pretrained on 16kHz sampled spee... | {"language": "da", "license": "apache-2.0", "tags": ["speech"]} | Alvenir/wav2vec2-base-da | null | [
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|
# Wav2vec2-base for Danish
This wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.
This model was pretrained on 16kHz sampled spee... | [
"# Wav2vec2-base for Danish\nThis wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.\n\nThis model was pretrained on 16kHz sampl... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-schizophreniaReddit2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-schizophreniaReddit2", "results": []}]} | Amalq/roberta-base-finetuned-schizophreniaReddit2 | null | [
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"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-schizophreniaReddit2
===========================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7785
Model description
-----------------
More information needed
Intended uses & li... | [
"### 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",
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question-answering | transformers |
# Question Answering NLU
Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of
training an intent classifier or a slot tagger, for example, we can ask the model intent- and
slot... | {"language": "en", "license": "cc-by-4.0", "datasets": ["atis"], "widget": [{"context": "Yes. No. I'm looking for a cheap flight to Boston."}]} | AmazonScience/qanlu | null | [
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"license:cc-by-4.0",
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|
# Question Answering NLU
Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of
training an intent classifier or a slot tagger, for example, we can ask the model intent- and
slot... | [
"# Question Answering NLU\n\nQuestion Answering NLU (QANLU) is an approach that maps the NLU task into question answering, \nleveraging pre-trained question-answering models to perform well on few-shot settings. Instead of \ntraining an intent classifier or a slot tagger, for example, we can ask the model intent- a... | [
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image-classification | transformers |
# indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Amrrs/indian-foods | null | [
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"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# indian-foods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### idli
!idli
#### kachori
!kachori
#### pani puri
!pani puri
#### samosa
!samosa
#### vada pav
!vada ... | [
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"#### kachori\n\n!kachori",
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image-classification | transformers |
# south-indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/h... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Amrrs/south-indian-foods | null | [
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#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# south-indian-foods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dosai
!dosai
#### idiyappam
!idiyappam
#### idli
!idli
#### puttu
!puttu
#### vadai
!vadai | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil 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 u... | {"language": "ta", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Tamil by Amrrs", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset":... | Amrrs/wav2vec2-large-xlsr-53-tamil | null | [
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|
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil 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 follo... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 479512837
- CO2 Emissions (in grams): 123.88023112815048
## Validation Metrics
- Loss: 0.6220805048942566
- Accuracy: 0.7961119332705503
- Macro F1: 0.7616345204219084
- Micro F1: 0.7961119332705503
- Weighted F1: 0.795387503907883... | {"language": "unk", "tags": "autonlp", "datasets": ["Anamika/autonlp-data-Feedback1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 123.88023112815048} | Anamika/autonlp-Feedback1-479512837 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 479512837
- CO2 Emissions (in grams): 123.88023112815048
## Validation Metrics
- Loss: 0.6220805048942566
- Accuracy: 0.7961119332705503
- Macro F1: 0.7616345204219084
- Micro F1: 0.7961119332705503
- Weighted F1: 0.795387503907883... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 473312409
- CO2 Emissions (in grams): 25.128735714898614
## Validation Metrics
- Loss: 0.6010786890983582
- Accuracy: 0.7990650945370823
- Macro F1: 0.7429662929144928
- Micro F1: 0.7990650945370823
- Weighted F1: 0.797766036377038... | {"language": "en", "tags": "autonlp", "datasets": ["Anamika/autonlp-data-fa"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 25.128735714898614} | Anamika/autonlp-fa-473312409 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 473312409
- CO2 Emissions (in grams): 25.128735714898614
## Validation Metrics
- Loss: 0.6010786890983582
- Accuracy: 0.7990650945370823
- Macro F1: 0.7429662929144928
- Micro F1: 0.7990650945370823
- Weighted F1: 0.797766036377038... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra_large_discriminator_squad2_512
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](ht... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "electra_large_discriminator_squad2_512", "results": []}]} | Andranik/TestQA2 | null | [
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"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# electra_large_discriminator_squad2_512
This model is a fine-tuned version of ahotrod/electra_large_discriminator_squad2_512 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## T... | [
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"## Intended uses & limitations\n\nMore information needed",
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"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n# electra_large_discriminator_squad2_512\n\nThis model is a fine-tuned version of ahotrod/electra_large_discriminator_squad2_512 on an unknown dataset.## Model description\n\nMore information needed... |
text2text-generation | transformers | This is a pretrained model that was loaded from t5-base. It has been adapted and changed by changing the max_length and summary_length. | {} | AndreLiu1225/t5-news | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a pretrained model that was loaded from t5-base. It has been adapted and changed by changing the max_length and summary_length. | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
37
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# model-QA-5-epoch-RU
This model is a fine-tuned version of [AndrewChar/diplom-prod-epoch-4-datast-sber-QA](https://huggingface.co/Andre... | {"language": "ru", "tags": ["generated_from_keras_callback"], "datasets": ["sberquad"], "model-index": [{"name": "model-QA-5-epoch-RU", "results": []}]} | AndrewChar/model-QA-5-epoch-RU | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"ru",
"dataset:sberquad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #ru #dataset-sberquad #endpoints_compatible #region-us
| model-QA-5-epoch-RU
===================
This model is a fine-tuned version of AndrewChar/diplom-prod-epoch-4-datast-sber-QA on sberquad
dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1991
* Validation Loss: 0.0
* Epoch: 5
Model description
-----------------
Модель отвечающая н... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_re': 2e-06 'decay\\_steps': 2986, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name':... | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #ru #dataset-sberquad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Po... | [
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"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #ru #dataset-sberquad #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Polynomi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "de", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - German", "results": [{"t... | AndrewMcDowell/wav2vec2-xls-r-1B-german | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"de",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #de #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1355
* Wer: 0.1532
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #de #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
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"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #de #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameter... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | AndrewMcDowell/wav2vec2-xls-r-1b-arabic | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ar",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ar #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - AR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1373
* Wer: 0.8607
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ar #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... | [
67,
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5,
50
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"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ar #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the ... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "ja", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic-speech-recogniti... | AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"ja",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5500
* Wer: 1.0132
* Cer: 0.1609
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpara... | [
82,
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50,
62
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"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["ar"], "license": "apache-2.0", "tags": ["ar", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - Arabic", "results": [{"t... | AndrewMcDowell/wav2vec2-xls-r-300m-arabic | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - AR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4502
* Wer: 0.4783
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
96,
155,
5,
50
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"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameter... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.
eval results:
WER: 0.20161578657865786
CER: 0.05062357805269733
-->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - German", "results": [{"t... | AndrewMcDowell/wav2vec2-xls-r-300m-german-de | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1768
* Wer: 0.2016
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
96,
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42
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameter... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "ja", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300-m", "results": [{"task": {"... | AndrewMcDowell/wav2vec2-xls-r-300m-japanese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.
Kanji are converted into Hiragana using the pykakasi library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
96,
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62
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameter... |
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. -->
# mbert-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mbert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wiki... | Andrey1989/mbert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| mbert-finetuned-ner
===================
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1264
* Precision: 0.9305
* Recall: 0.9375
* F1: 0.9340
* Accuracy: 0.9700
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... | [
56,
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5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
token-classification | transformers | This model is a finetuning of bert-base-greek-uncased as a Token Classifier which predicts at each token which punctuation mark it is followed by. The model preprocesses everything to lowercase and removes all Greek diacritics. For information on pretraining of the Greek Bert model, please refer to [Greek Bert](https:/... | {} | Andrianos/bert-base-greek-punctuation-prediction-finetuned | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a finetuning of bert-base-greek-uncased as a Token Classifier which predicts at each token which punctuation mark it is followed by. The model preprocesses everything to lowercase and removes all Greek diacritics. For information on pretraining of the Greek Bert model, please refer to Greek Bert
Finetun... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person'... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | Andrija/M-bert-NER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991,... | {} | Andrija/RobertaFastBPE | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991,... | [
"# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\nURL(encoded['input_ids'])",
"# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>"
] | [
"TAGS\n#region-us \n",
"# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\nURL(encoded['input_ids'])",
"# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>"
] | [
5,
101,
36
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"TAGS\n#region-us \n# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\nURL(encoded['input_ids'])# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>"
] |
fill-mask | transformers | # Transformer language model for Croatian and Serbian
Trained on 43GB datasets that contain Croatian and Serbian language for one epochs (9.6 mil. steps, 3 epochs).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
Validation number of exampels run for perplexity:1620487 sentences
Perplexity:6.02
St... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig", "cc100", "hrwac"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | Andrija/SRoBERTa-F | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 43GB datasets that contain Croatian and Serbian language for one epochs (9.6 mil. steps, 3 epochs).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
Validation number of exampels run... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | Andrija/SRoBERTa-L-NER | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Transformer language model for Croatian and Serbian
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzig, OSCAR and srWac datasets
| Model | #params | Arch. | Training data |
|----------------------... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | Andrija/SRoBERTa-L | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzig, OSCAR and srWac datasets
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
66
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"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | Andrija/SRoBERTa-NER | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | Andrija/SRoBERTa-XL-NER | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Transformer language model for Croatian and Serbian
Trained on 28GB datasets that contain Croatian and Serbian language for one epochs (3 mil. steps).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
| Model | #params | Arch. | Training data ... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig", "cc100", "hrwac"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | Andrija/SRoBERTa-XL | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 28GB datasets that contain Croatian and Serbian language for one epochs (3 mil. steps).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
79
] | [
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] |
token-classification | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | Andrija/SRoBERTa-base-NER | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Transformer language model for Croatian and Serbian
Trained on 3GB datasets that contain Croatian and Serbian language for two epochs.
Leipzig and OSCAR datasets
# Information of dataset
| Model | #params | Arch. | Training data |
|----------------... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "leipzig"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | Andrija/SRoBERTa-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 3GB datasets that contain Croatian and Serbian language for two epochs.
Leipzig and OSCAR datasets
Information of dataset
======================
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
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] |
fill-mask | transformers | # Transformer language model for Croatian and Serbian
Trained on 0.7GB dataset Croatian and Serbian language for one epoch.
Dataset from Leipzig Corpora.
# Information of dataset
| Model | #params | Arch. | Training data |
|---------------------------... | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["leipzig"], "widget": [{"text": "Gde je <mask>."}]} | Andrija/SRoBERTa | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 0.7GB dataset Croatian and Serbian language for one epoch.
Dataset from Leipzig Corpora.
Information of dataset
======================
How to use in code
==================
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
54
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] |
null | null | C:\Users\andry\Desktop\Выжигание 24-12-2021.jpg | {} | Andry/1111 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| C:\Users\andry\Desktop\Выжигание URL | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
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null | null |
Now we only upload two models for creating demos for image and video classification.
More models and code can be found in our github repo: [UniFormer](https://github.com/Sense-X/UniFormer). | {"license": "mit"} | Andy1621/uniformer | null | [
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
Now we only upload two models for creating demos for image and video classification.
More models and code can be found in our github repo: UniFormer. | [] | [
"TAGS\n#license-mit #has_space #region-us \n"
] | [
13
] | [
"TAGS\n#license-mit #has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | Ann2020/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 | 2022-03-02T23:29:04+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: 0.0609
* Precision: 0.9275
* Recall: 0.9365
* F1: 0.9320
* Accuracy: 0.9840
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... | [
55,
101,
5,
44
] | [
"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: 2e-0... |
feature-extraction | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on bert-base-uncased model and pre-trained for text input | {} | Anonymous/ReasonBERT-BERT | null | [
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| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
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feature-extraction | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on roberta-base model and pre-trained for text input | {} | Anonymous/ReasonBERT-RoBERTa | null | [
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| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
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feature-extraction | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on tapas-base(no_reset) model and pre-trained for table input | {} | Anonymous/ReasonBERT-TAPAS | null | [
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| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20384195
- CO2 Emissions (in grams): 4.214012748213151
## Validation Metrics
- Loss: 1.0120062828063965
- Rouge1: 41.1808
- Rouge2: 26.2564
- RougeL: 31.3106
- RougeLsum: 38.9991
- Gen Len: 58.45
## Usage
You can use cURL to access this model... | {"language": "unk", "tags": "autonlp", "datasets": ["Anorak/autonlp-data-Niravana-test2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 4.214012748213151} | Anorak/nirvana | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20384195
- CO2 Emissions (in grams): 4.214012748213151
## Validation Metrics
- Loss: 1.0120062828063965
- Rouge1: 41.1808
- Rouge2: 26.2564
- RougeL: 31.3106
- RougeLsum: 38.9991
- Gen Len: 58.45
## Usage
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text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | AnthonyNelson/DialoGPT-small-ricksanchez | null | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Anthos23/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Anthos23/distilbert-base-uncased-finetuned-sst2", "results": []}]} | Anthos23/distilbert-base-uncased-finetuned-sst2 | null | [
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| Anthos23/distilbert-base-uncased-finetuned-sst2
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0662
* Validation Loss: 0.2623
* Train Accuracy: 0.9083
* Epoc... | [
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text-generation | transformers |
# Jordan DialoGPT Model | {"tags": ["conversational"]} | Apisate/DialoGPT-small-jordan | null | [
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text2text-generation | transformers |
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models | {"language": "en", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "thumbnail": "Keywords to Sentences"} | Apoorva/k2t-test | null | [
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|
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models | [] | [
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] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-ner
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", ... | ArBert/albert-base-v2-finetuned-ner | null | [
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| albert-base-v2-finetuned-ner
============================
This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0700
* Precision: 0.9301
* Recall: 0.9376
* F1: 0.9338
* Accuracy: 0.9852
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/bert-base-uncased-finetuned-ner](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner-kmeans", "results": []}]} | ArBert/bert-base-uncased-finetuned-ner-kmeans | null | [
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| bert-base-uncased-finetuned-ner-kmeans
======================================
This model is a fine-tuned version of ArBert/bert-base-uncased-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1169
* Precision: 0.9084
* Recall: 0.9245
* F1: 0.9164
* Accuracy: 0.9792... | [
"### 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",
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": []}]} | ArBert/bert-base-uncased-finetuned-ner | null | [
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| bert-base-uncased-finetuned-ner
===============================
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: 0.0905
* Precision: 0.9068
* Recall: 0.9200
* F1: 0.9133
* Accuracy: 0.9787
Model description
--------------... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-agglo-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggi... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-agglo-twitter", "results": []}]} | ArBert/roberta-base-finetuned-ner-agglo-twitter | null | [
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| roberta-base-finetuned-ner-agglo-twitter
========================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Precision: 0.6885
* Recall: 0.7665
* F1: 0.7254
Model description... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-kmeans-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-kmeans-twitter", "results": []}]} | ArBert/roberta-base-finetuned-ner-kmeans-twitter | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| roberta-base-finetuned-ner-kmeans-twitter
=========================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Precision: 0.6885
* Recall: 0.7665
* F1: 0.7254
Model descripti... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-kmeans", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll... | ArBert/roberta-base-finetuned-ner-kmeans | null | [
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| roberta-base-finetuned-ner-kmeans
=================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0592
* Precision: 0.9559
* Recall: 0.9615
* F1: 0.9587
Model description
--------... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-ner", "results": []}]} | ArBert/roberta-base-finetuned-ner | null | [
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| roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0738
* Precision: 0.9232
* Recall: 0.9437
* F1: 0.9333
* Accuracy: 0.9825
Model description
-----------------
More info... | [
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text-generation | transformers |
# Stark DialoGPT Model | {"tags": ["conversational"]} | ArJakusz/DialoGPT-small-stark | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Aran/DialoGPT-medium-harrypotter | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Aran/DialoGPT-small-harrypotter | null | [
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text-generation | transformers | # Rick DialoGPT Model
| {"tags": ["conversational"]} | Arcktosh/DialoGPT-small-rick | null | [
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text-generation | transformers | # Cultured Kumiko DialoGPT Model | {"tags": ["conversational"]} | AriakimTaiyo/DialoGPT-cultured-Kumiko | null | [
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text-generation | null | # Medium Kumiko DialoGPT Model | {"tags": ["conversational"]} | AriakimTaiyo/DialoGPT-medium-Kumiko | null | [
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text-generation | transformers | # Revised Kumiko DialoGPT Model | {"tags": ["conversational"]} | AriakimTaiyo/DialoGPT-revised-Kumiko | null | [
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text-generation | transformers | # Kumiko DialoGPT Model | {"tags": ["conversational"]} | AriakimTaiyo/DialoGPT-small-Kumiko | null | [
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text-generation | transformers | # Rikka DialoGPT Model | {"tags": ["conversational"]} | AriakimTaiyo/DialoGPT-small-Rikka | null | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hausa2-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-hausa2-demo-colab", "results": []}]} | Arnold/wav2vec2-hausa2-demo-colab | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hausa2-demo-colab
==========================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2032
* Wer: 0.7237
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hausa2-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hausa2-demo-colab", "results": []}]} | Arnold/wav2vec2-large-xlsr-hausa2-demo-colab | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-hausa2-demo-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2993
* Wer: 0.4826
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.6e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 8\n* seed: 13\n* gradient\\_accumulation\\_steps: 3\n* total\\_train\\_batch\\_size: 36\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Aron/distilbert-base-uncased-finetuned-emotion | null | [
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2295
* Accuracy: 0.92
* F1: 0.9202
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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text-generation | transformers |
#Okarin Bot | {"tags": ["conversational"]} | ArtemisZealot/DialoGTP-small-Qkarin | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Aruden/DialoGPT-medium-harrypotterall | null | [
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text2text-generation | transformers |
```
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large")
tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large")
text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task.
input_ids = tok... | {"inference": {"parameters": {"do_sample": true, "max_length": 500, "top_p": 0.9, "top_k": 20, "temperature": 1, "num_return_sequences": 10}}, "widget": [{"text": "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike rec... | ielabgroup/BiTAG-t5-large | null | [
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Model: Google's Pegasus (https://huggingface.co/google/pegasus-xsum)
- Problem type: Summarization
- Model ID: 34558227
- CO2 Emissions (in grams): 137.60574081887984
- Spaces: https://huggingface.co/spaces/TitleGenerators/ArxivTitleGenerator
- Dataset: arXiv Dataset (https://www.kaggle... | {"language": "en", "tags": "autonlp", "datasets": ["AryanLala/autonlp-data-Scientific_Title_Generator"], "widget": [{"text": "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for c... | AryanLala/autonlp-Scientific_Title_Generator-34558227 | null | [
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|
# Model Trained Using AutoNLP
- Model: Google's Pegasus (URL
- Problem type: Summarization
- Model ID: 34558227
- CO2 Emissions (in grams): 137.60574081887984
- Spaces: URL
- Dataset: arXiv Dataset (URL
- Data subset used: URL
## Validation Metrics
- Loss: 2.578599214553833
- Rouge1: 44.8482
- Rouge2: 24.4052
- Roug... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-parsbert-uncased-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://hug... | {"tags": ["generated_from_trainer"]} | Ashkanmh/bert-base-parsbert-uncased-finetuned | null | [
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| bert-base-parsbert-uncased-finetuned
====================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2045
Model description
-----------------
More information needed
Int... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
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text-generation | transformers |
A discord chatbot trained on the whole LiS script to simulate character speech | {"tags": ["conversational"]} | Aspect11/DialoGPT-Medium-LiSBot | null | [
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text-generation | transformers | # RinTohsaka bot | {"tags": ["conversational"]} | Asuramaru/DialoGPT-small-rintohsaka | null | [
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text-generation | transformers | GPT-Glacier, a GPT-Neo 125M model finetuned on the Glacier2 Modding Discord server. | {} | Atampy26/GPT-Glacier | null | [
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"gpt_neo",
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"region:us"
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#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| GPT-Glacier, a GPT-Neo 125M model finetuned on the Glacier2 Modding Discord server. | [] | [
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text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | Atchuth/DialoGPT-small-MichaelBot | null | [
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null | null | Placeholder | {} | Atlasky/Turkish-Negator | null | [
"region:us"
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#region-us
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text-generation | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | Augustvember/WOKKAWOKKA | null | [
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text-generation | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | Augustvember/test | null | [
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text-generation | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | Augustvember/wokka5 | null | [
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text-generation | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | Augustvember/wokkabottest2 | null | [
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null | null | https://www.geogebra.org/m/bbuczchu
https://www.geogebra.org/m/xwyasqje
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https://www.geogebra.org/m/tkqqqthm
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https://www.geogebra.org/m/ywuaj7p5
https://www.geogebra.org/m/jkfkayj3
https://www.geogebra.org/m/hptnn7ar
https://www.geogebra.org/m/de9cw... | {} | Aurora/asdawd | null | [
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text-generation | transformers |
# Blitzo DialoGPT Model | {"tags": ["conversational"]} | AvatarXD/DialoGPT-medium-Blitzo | null | [
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"gpt2",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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