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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-xls-r-300m-ia
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["ia"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r... | ayameRushia/wav2vec2-large-xls-r-300m-ia | null | [
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"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| wav2vec2-large-xls-r-300m-ia
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1452
* Wer: 0.1253
Training Procedure
------------------
Training is conducted in Google C... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\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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"### Training hyperpar... |
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": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "XLS-R-300M - Indonesia", "results": [{"task": {"type": "automatic-speech-recognition", "... | ayameRushia/wav2vec2-large-xls-r-300m-id | null | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ID dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3975
* Wer: 0.2633
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### 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": ["mn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r... | ayameRushia/wav2vec2-large-xls-r-300m-mn | null | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5502
* Wer: 0.4042
Training and evaluation data
----------------------------
Evaluation is conducted in Notebook, you c... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperpar... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Indonesia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia 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 ... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesia by Ayame Rushia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"},... | ayameRushia/wav2vec2-large-xlsr-indo-base | null | [
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| # Wav2Vec2-Large-XLSR-53-Indonesia
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia 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... | [
"# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
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automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Indonesia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia 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 ... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesia by Ayame Rushia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"},... | ayameRushia/wav2vec2-large-xlsr-indonesia | null | [
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| # Wav2Vec2-Large-XLSR-53-Indonesia
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia 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... | [
"# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
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fill-mask | transformers |
# `false-positives-scancode-bert-base-uncased-L8-1`
## Intended Use
This model is intended to be used for Sentence Classification which is used for results
analysis in [`scancode-results-analyzer`](https://github.com/nexB/scancode-results-analyzer).
`scancode-results-analyzer` helps detect faulty scans in [`scancod... | {"language": "en", "license": "apache-2.0", "tags": ["license", "sentence-classification", "scancode", "license-compliance"], "datasets": ["bookcorpus", "wikipedia", "scancode-rules"], "version": 1.0} | ayansinha/false-positives-scancode-bert-base-uncased-L8-1 | null | [
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|
# 'false-positives-scancode-bert-base-uncased-L8-1'
## Intended Use
This model is intended to be used for Sentence Classification which is used for results
analysis in 'scancode-results-analyzer'.
'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among... | [
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fill-mask | transformers |
# `lic-class-scancode-bert-base-cased-L32-1`
## Intended Use
This model is intended to be used for Sentence Classification which is used for results
analysis in [`scancode-results-analyzer`](https://github.com/nexB/scancode-results-analyzer).
`scancode-results-analyzer` helps detect faulty scans in [`scancode-toolk... | {"language": "en", "license": "apache-2.0", "tags": ["license", "sentence-classification", "scancode", "license-compliance"], "datasets": ["bookcorpus", "wikipedia", "scancode-rules"], "version": 1.0} | ayansinha/lic-class-scancode-bert-base-cased-L32-1 | null | [
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|
# 'lic-class-scancode-bert-base-cased-L32-1'
## Intended Use
This model is intended to be used for Sentence Classification which is used for results
analysis in 'scancode-results-analyzer'.
'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other ... | [
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"# 'lic-class-scancode-bert-base-cased-L32-1'",
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text-classification | transformers |
# bert-base-cased trained on TREC 6-class task
## Model description
A simple base BERT model trained on the "trec" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = Au... | {"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["trec"], "model-index": [{"name": "aychang/bert-base-cased-trec-coarse", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "trec", "type": "trec", "config": "default", "split": "te... | aychang/bert-base-cased-trec-coarse | null | [
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"region:us"
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"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-cased trained on TREC 6-class task
## Model description
A simple base BERT model trained on the "trec" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
##### AdaptNLP
#### Limitations and bias
This is minimal language model trained on a benchmark dataset.
## Training ... | [
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"## Inten... |
question-answering | null |
# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad
## Model description
A serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a config.pbtxt for deployment using NVIDIA Triton Inference Server. | {"language": ["en"], "license": "mit", "tags": ["question-answering", "torchscript", "FastNN"], "datasets": ["squad"]} | aychang/bert-large-cased-whole-word-masking-finetuned-squad | null | [
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"en"
] | TAGS
#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us
|
# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad
## Model description
A serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment using NVIDIA Triton Inference Server. | [
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"## Model description\n\nA serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment u... |
text-classification | transformers |
# TREC 6-class Task: distilbert-base-cased
## Model description
A simple base distilBERT model trained on the "trec" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model =... | {"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["trec"], "model-index": [{"name": "aychang/distilbert-base-cased-trec-coarse", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "trec", "type": "trec", "config": "default", "split... | aychang/distilbert-base-cased-trec-coarse | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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|
# TREC 6-class Task: distilbert-base-cased
## Model description
A simple base distilBERT model trained on the "trec" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
##### AdaptNLP
#### Limitations and bias
This is minimal language model trained on a benchmark dataset.
## Traini... | [
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"##### Transformers",
"##### AdaptNLP",
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"## Model description\n\nA simple base distilBERT model trained on the \"trec\" dataset.",
"## In... |
question-answering | null |
# TorchScript model of distilbert-squad
## Model description
A serialized torchscript model of distilbert-squad with a config.pbtxt for deployment using NVIDIA Triton Inference Server. | {"language": ["en"], "license": "mit", "tags": ["question-answering", "torchscript", "FastNN"], "datasets": ["squad"]} | aychang/distilbert-squad | null | [
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"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us
|
# TorchScript model of distilbert-squad
## Model description
A serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server. | [
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"# TorchScript model of distilbert-squad",
"## Model description\n\nA serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server."
] |
object-detection | null |
# TorchScript model of faster-rcnn
## Model description
A serialized torchscript model of [faster-rcnn](https://pytorch.org/vision/stable/models.html#faster-r-cnn) with a config.pbtxt for deployment using NVIDIA Triton Inference Server. | {"language": ["en"], "license": "mit", "tags": ["object-detection", "torchscript", "FastNN"], "datasets": ["coco"]} | aychang/fasterrcnn-resnet50-cpu | null | [
"object-detection",
"torchscript",
"FastNN",
"en",
"dataset:coco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#object-detection #torchscript #FastNN #en #dataset-coco #license-mit #region-us
|
# TorchScript model of faster-rcnn
## Model description
A serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server. | [
"# TorchScript model of faster-rcnn",
"## Model description\n\nA serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server."
] | [
"TAGS\n#object-detection #torchscript #FastNN #en #dataset-coco #license-mit #region-us \n",
"# TorchScript model of faster-rcnn",
"## Model description\n\nA serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server."
] |
text-classification | transformers |
# IMDB Sentiment Task: roberta-base
## Model description
A simple base roBERTa model trained on the "imdb" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModel... | {"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["imdb"]} | aychang/roberta-base-imdb | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:imdb",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# IMDB Sentiment Task: roberta-base
## Model description
A simple base roBERTa model trained on the "imdb" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
##### AdaptNLP
#### Limitations and bias
This is minimal language model trained on a benchmark dataset.
## Training data
I... | [
"# IMDB Sentiment Task: roberta-base",
"## Model description\n\nA simple base roBERTa model trained on the \"imdb\" dataset.",
"## Intended uses & limitations",
"#### How to use",
"##### Transformers",
"##### AdaptNLP",
"#### Limitations and bias\n\nThis is minimal language model trained on a benchmark ... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# IMDB Sentiment Task: roberta-base",
"## Model description\n\nA simple base roBERTa model trained on the \"imdb\" dataset.",
"## Intended use... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | aydin/DialoGPT-medium-michael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-imdb
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [imdb](https://www.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-imdb", "results": []}]} | aypan17/distilgpt2-imdb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-imdb
This model is a fine-tuned version of distilgpt2 on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followin... | [
"# distilgpt2-imdb\n\nThis model is a fine-tuned version of distilgpt2 on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-imdb\n\nThis model is a fine-tuned version of distilgpt2 on the imdb dataset.",
"## Model description\n\nMore info... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-med-imdb
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
##... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-med-imdb", "results": []}]} | aypan17/gpt2-med-imdb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-med-imdb
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followi... | [
"# gpt2-med-imdb\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trainin... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-med-imdb\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
... |
text-classification | transformers |
TrainingArgs:
lr=2e-5,
train-batch-size=16,
eval-batch-size=16,
num-train-epochs=5,
weight-decay=0.01,
| {"license": "mit"} | aypan17/roberta-base-imdb | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
TrainingArgs:
lr=2e-5,
train-batch-size=16,
eval-batch-size=16,
num-train-epochs=5,
weight-decay=0.01,
| [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# RudeRick discord bot | {"tags": ["conversational"]} | ayush19/rick-sanchez | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# RudeRick discord bot | [
"# RudeRick discord bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RudeRick discord bot"
] |
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-azerbaijani-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mbert-finetuned-azerbaijani-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "... | azizbarank/mbert-finetuned-azerbaijani-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 | 2022-03-02T23:29:05+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-azerbaijani-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: 0.1385
* Precision: 0.8899
* Recall: 0.9154
* F1: 0.9025
* Accuracy: 0.9669
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #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\\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-gn-demo
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["gn"], "license": "apache-2.0", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice", "mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-base-gn-demo", "results": []}]} | azuur/wav2vec2-base-gn-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"hf-asr-leaderboard",
"gn",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0... | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #mozilla-foundation/common_voice_8_0 #robust-speech-event #hf-asr-leaderboard #gn #dataset-common_voice #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-gn-demo
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7426
* Wer: 0.7256
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler... | [
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... |
text-generation | transformers |
#Ragnar Lothbrok DialoGPT Model | {"tags": ["conversational"]} | b0shakk/DialoGPT-small-Ragnar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Ragnar Lothbrok DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# shirt_identifier
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/hug... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | b25mayank3/shirt_identifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# shirt_identifier
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
#### Big Check shirt
!Big Check shirt
#### Formal Shirt
!Formal Shirt
#### casual shirt
!casual shirt
#... | [
"# shirt_identifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Big Check shirt\n\n!Big Check shirt",
"#### Formal Shirt\n\n!Formal Shirt",
"#### c... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# shirt_identifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any iss... |
text-generation | transformers |
# GPT-Neo 125M finetuned with beer recipes
## Model Description
GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture https://huggingface.co/EleutherAI/gpt-neo-125M.
It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.
... | {"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["custom"], "widget": [{"text": "style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:", "example_title": "Pilsener"}, {"text": "style: IPA\nbatch_size: 20\nefficiency: 75\nboil_size:", "example_title": "... | b3ck1/gpt-neo-125M-finetuned-beer-recipes | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"causal-lm",
"en",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT-Neo 125M finetuned with beer recipes
## Model Description
GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL
It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.
## Training data
This model was trained on... | [
"# GPT-Neo 125M finetuned with beer recipes",
"## Model Description\n\nGPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL\nIt generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.",
"## Training data\n\nThis mod... | [
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"# GPT-Neo 125M finetuned with beer recipes",
"## Model Description\n\nGPT-Neo 125M is a transformer model based on EleutherAI's... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - A... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | baaastien/xls-r-ab-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 133.5167
- Wer: 18.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.5167\n- Wer: 18.9286",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the e... |
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-timit_asr-oogway
This model is a fine-tuned version of [OthmaneJ/distil-wav2vec2](https://huggingface.co/OthmaneJ/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-timit_asr-oogway", "results": []}]} | baby-oogway/wav2vec2-timit_asr-oogway | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-timit_asr-oogway
This model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# wav2vec2-timit_asr-oogway\n\nThis model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-timit_asr-oogway\n\nThis model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.",
"## Model description\n\nMore... |
null | transformers | "hello"
| {} | bada/test | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us
| "hello"
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trai... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]} | baffo32/genji-python-6B-split | null | [
"transformers",
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"text-generation",
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"en"
] | TAGS
#transformers #gpt_neo #text-generation #pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Genji-python 6B
===============
For example usage or to easily use the model you can check our colab notebook:
Notebook
Model Description
-----------------
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
Split mod... | [
"### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\ngit-lfs also needs to be installed, on ubuntu:\n\n\nafter i... | [
"TAGS\n#transformers #gpt_neo #text-generation #pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we ... |
text-generation | transformers |
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
<figure>
| Hyperparameter | Value |
|-----... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]} | baffo32/gpt-j-6B-ptmap | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
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"en",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864",
"2101.00027"
] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| GPT-J 6B
========
Model Description
-----------------
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
**\*** Each layer consists of one feedforward block and one self attention block.
... | [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unkn... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Bias... |
text-generation | transformers |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "mit", "tags": ["exbert"]} | baffo32/gpt2-ptmap | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
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"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #tflite #rust #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #tf #jax #tflite #rust #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some ... |
text2text-generation | transformers |
# ByT5 - Base
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4mult... | {"language": "multilingual", "license": "apache-2.0", "datasets": ["mc4"]} | baffo32/pyc2py_alpha2 | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"multilingual",
"dataset:mc4",
"arxiv:1907.06292",
"arxiv:2105.13626",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06292",
"2105.13626"
] | [
"multilingual"
] | TAGS
#transformers #jax #t5 #text2text-generation #multilingual #dataset-mc4 #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5 - Base
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5... | [
"# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream tas... | [
"TAGS\n#transformers #jax #t5 #text2text-generation #multilingual #dataset-mc4 #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the ... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | baffo32/t5-base-ptmap | null | [
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"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"... | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #pytorch #tf #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-... |
text-generation | transformers |
# Model name
Indian Political Tweets LM
## Model description
Note: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the [IndianPoliticalTweetsLMMedium](https://huggingface.co/bagdaebhishek/IndianPoliticalTweetsLMMedium) model.
This is ... | {"language": "en", "license": "apache-2.0", "tags": ["India", "politics", "tweets", "BJP", "Congress", "AAP", "pytorch", "gpt2", "lm-head", "text-generation"], "datasets": ["Twitter", "IndianPolitics"], "thumbnail": "https://bagdeabhishek.github.io/twitterAnalysis_files/networkfin.jpg"} | bagdaebhishek/IndianPoliticalTweetsLM | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"India",
"politics",
"tweets",
"BJP",
"Congress",
"AAP",
"lm-head",
"en",
"dataset:Twitter",
"dataset:IndianPolitics",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model name
Indian Political Tweets LM
## Model description
Note: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model.
This is a GPT2 Language model with LM head fine-tuned on tweets crawled from h... | [
"# Model name\nIndian Political Tweets LM",
"## Model description\nNote: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model. \n\nThis is a GPT2 Language model with LM head fine-tuned on tweets cr... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model name\nIndian Political Tweets LM",
"## ... |
text-generation | transformers |
# Model name
Indian Political Tweets LM Medium (Based on GPT2-Medium)
## Model description
This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this [blog](https://bagdeabhis... | {"language": "en", "license": "apache-2.0", "tags": ["India", "politics", "tweets", "BJP", "Congress", "AAP", "pytorch", "gpt2", "lm-head", "text-generation"], "datasets": ["Twitter", "IndianPolitics"], "thumbnail": "https://bagdeabhishek.github.io/twitterAnalysis_files/networkfin.jpg"} | bagdaebhishek/IndianPoliticalTweetsLMMedium | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"India",
"politics",
"tweets",
"BJP",
"Congress",
"AAP",
"lm-head",
"en",
"dataset:Twitter",
"dataset:IndianPolitics",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model name
Indian Political Tweets LM Medium (Based on GPT2-Medium)
## Model description
This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post.
This model ... | [
"# Model name\nIndian Political Tweets LM Medium (Based on GPT2-Medium)",
"## Model description\n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. \n... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model name\nIndian Political Tweets LM Medium (... |
fill-mask | transformers | hello
| {} | baicuya/bert_cn | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sinai Voice Arabic Speech Recognition Model
# نموذج **صوت سيناء** للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
This mode... | {"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "widget": [{"example_title": "Example 1", "src": "https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/m... | bakrianoo/sinai-voice-ar-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Sinai Voice Arabic Speech Recognition Model
===========================================
نموذج صوت سيناء للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
====================================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATI... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'",
"### Inference Without LM",
"### 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\\_si... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/com... |
text2text-generation | transformers |
## Arabic T5 Base Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-base` model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
```
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and sup... | {"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]} | bakrianoo/t5-arabic-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Arabic"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Arabic T5 Base Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
Read More
| [
"## Arabic T5 Base Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks.",
"### About T5\n\n\n\nRead More"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Arabic T5 Base Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' mo... |
text2text-generation | transformers |
## Arabic T5 Large Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-large` model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
```
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and s... | {"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]} | bakrianoo/t5-arabic-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Arabic"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Arabic T5 Large Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
Read More
| [
"## Arabic T5 Large Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks.",
"### About T5\n\n\n\nRead More"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Arabic T5 Large Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' ... |
text2text-generation | transformers |
## Arabic T5 Small Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-small` model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
```
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and s... | {"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]} | bakrianoo/t5-arabic-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Arabic"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Arabic T5 Small Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks.
### About T5
Read More
| [
"## Arabic T5 Small Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks.",
"### About T5\n\n\n\nRead More"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Arabic T5 Small Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' ... |
null | null | The main card for Saturday’s Manny Pacquiao vs Yordenis Ugas fight gets underway at T-Mobile Arena in Las Vegas at 9 p.m. ET and the main event is expected to start sometime around 11:30 p.m. This is going to air on FOX Sports PPV and YouTube PPV. The card will cost
https://web.sites.google.com/view/ppv-livemanny-pac... | {} | balalsahabi/fdgdfg | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| The main card for Saturday’s Manny Pacquiao vs Yordenis Ugas fight gets underway at T-Mobile Arena in Las Vegas at 9 p.m. ET and the main event is expected to start sometime around 11:30 p.m. This is going to air on FOX Sports PPV and YouTube PPV. The card will cost
URL
URL
URL
URL
URL
URL
URL
LIVE::Watch Full ... | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers | # Named Entity Recognition using Transformers
This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction obje... | {} | balamurugan1603/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Named Entity Recognition using Transformers
This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction obje... | [
"# Named Entity Recognition using Transformers\nThis is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-predictio... | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Named Entity Recognition using Transformers\nThis is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state... |
text-generation | transformers |
# Test Bot DialoGTP Model | {"tags": ["conversational"]} | balta/DialoGPT-small-TestBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test Bot DialoGTP Model | [
"# Test Bot DialoGTP Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test Bot DialoGTP Model"
] |
text-generation | transformers |
TRIGGER WARNING
---------------
This model was created by training GPT2-medium on a custom dataset containing tens of thousands of blog posts about people's experiences living with mental illnesses. As such, the texts that this model generates may be triggering and/or NSFW. Please explore at your own discretion.
The ... | {"language": "en", "widget": [{"text": "I feel "}, {"text": "I want "}, {"text": "I believe "}]} | banalyst/wonder-egg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
TRIGGER WARNING
---------------
This model was created by training GPT2-medium on a custom dataset containing tens of thousands of blog posts about people's experiences living with mental illnesses. As such, the texts that this model generates may be triggering and/or NSFW. Please explore at your own discretion.
The ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | banden/DialoGPT-medium-RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model | [
"# Rick Sanchez DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
text-generation | transformers |
# Loki DialoGPT Model | {"tags": ["conversational"]} | banden/DialoGPT-small-LokiBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Loki DialoGPT Model | [
"# Loki DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Loki DialoGPT Model"
] |
text-classification | transformers |
## Overview
This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is.
The model was fine-tuned from the `distilbert-base-uncased` model
### Labels
LABEL_0 - Not helpful
LABEL_1 - Helpful
### How to use
The following c... | {"license": "apache-2.0"} | banjtheman/distilbert-base-uncased-helpful-amazon | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Overview
This model was trained with data from URL to predict how "helpful" a review is.
The model was fine-tuned from the 'distilbert-base-uncased' model
### Labels
LABEL_0 - Not helpful
LABEL_1 - Helpful
### How to use
The following code shows how to make a prediction with this model
| [
"## Overview\r\n\r\nThis model was trained with data from URL to predict how \"helpful\" a review is.\r\n\r\nThe model was fine-tuned from the 'distilbert-base-uncased' model",
"### Labels\r\nLABEL_0 - Not helpful \r\nLABEL_1 - Helpful",
"### How to use\r\n\r\nThe following code shows how to make a prediction ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Overview\r\n\r\nThis model was trained with data from URL to predict how \"helpful\" a review is.\r\n\r\nThe model was fine-tuned from the 'distilbert-base-uncased' mo... |
text-generation | transformers | Model based on [ruGPT-3](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2) for generating songs.
Tuned on lyrics collected from [genius](https://genius.com/).
Examples of used artists:
* [Oxxxymiron](https://genius.com/artists/Oxxxymiron)
* [Моргенштерн](https://genius.com/artists/Morgenshtern)
* [ЛСП](http... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "widget": [{"text": "\u0411\u0430\u0442\u044f \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442\u0441\u044f \u0442\u0440\u0435\u0437\u0432\u044b\u0439, \u0432 \u0440\u0443\u043a\u0435 \u0431\u0443\u0445\u0430\u043d\u043a\u0430", "example_title": "Exam... | bankholdup/rugpt3_song_writer | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model based on ruGPT-3 for generating songs.
Tuned on lyrics collected from genius.
Examples of used artists:
* Oxxxymiron
* Моргенштерн
* ЛСП
* Гражданская оборона
* Король и Шут
* etc | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | banri/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7523
* Matthews Correlation: 0.5259
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
fill-mask | transformers | # Multi-dialect-Arabic-BERT
This is a repository of Multi-dialect Arabic BERT model.
By [Mawdoo3-AI](https://ai.mawdoo3.com/).
<p align="center">
<br>
<img src="https://raw.githubusercontent.com/mawdoo3/Multi-dialect-Arabic-BERT/master/multidialct_arabic_bert.png" alt="Background reference: http://www.qfi.or... | {"language": "ar", "datasets": ["nadi"], "thumbnail": "https://raw.githubusercontent.com/mawdoo3/Multi-dialect-Arabic-BERT/master/multidialct_arabic_bert.png"} | bashar-talafha/multi-dialect-bert-base-arabic | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:nadi",
"arxiv:2007.05612",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.05612"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ar #dataset-nadi #arxiv-2007.05612 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Multi-dialect-Arabic-BERT
This is a repository of Multi-dialect Arabic BERT model.
By Mawdoo3-AI.
<p align="center">
<br>
<img src="URL alt="Background reference: URL width="500"/>
<br>
<p>
### About our Multi-dialect-Arabic-BERT model
Instead of training the Multi-dialect Arabic BERT model from scr... | [
"# Multi-dialect-Arabic-BERT\nThis is a repository of Multi-dialect Arabic BERT model.\n\nBy Mawdoo3-AI. \n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"Background reference: URL width=\"500\"/>\n <br>\n<p>",
"### About our Multi-dialect-Arabic-BERT model\nInstead of training the Multi-dialect Ara... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #dataset-nadi #arxiv-2007.05612 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Multi-dialect-Arabic-BERT\nThis is a repository of Multi-dialect Arabic BERT model.\n\nBy Mawdoo3-AI. \n\n<p align=\"center\">\n <br>\n <img src=\... |
text-classification | transformers |
# BatteryBERT-cased for Battery Abstract Classification
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure*... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batterybert-cased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryBERT-cased for Battery Abstract Classification
Language model: batterybert-cased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## Usage
##... | [
"# BatteryBERT-cased for Battery Abstract Classification \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Perfo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryBERT-cased for Battery Abstract Classification \r\nLanguage model: batterybert-cased\r\nLanguage: English... |
question-answering | transformers |
# BatteryBERT-cased for QA
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_s... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batterybert-cased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatteryBERT-cased for QA
Language model: batterybert-cased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the battery... | [
"# BatteryBERT-cased for QA \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatteryBERT-cased for QA \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Extracti... |
fill-mask | transformers |
# BatteryBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](ht... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batterybert-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between... | [
"# BatteryBERT-uncased model\r\n\r\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in\r\nthis paper and first released in\r\nthis repository. This model is case-sensitive: it makes a differe... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryBERT-uncased model\r\n\r\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) ob... |
text-classification | transformers |
# BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastruct... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batterybert-uncased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryBERT-uncased for Battery Abstract Classification
Language model: batterybert-uncased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## Usage... | [
"# BatteryBERT-uncased for Battery Abstract Classification \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## P... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryBERT-uncased for Battery Abstract Classification \r\nLanguage model: batterybert-uncased\r\nLanguage: Eng... |
question-answering | transformers |
# BatteryBERT-uncased for QA
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
bat... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batterybert-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatteryBERT-uncased for QA
Language model: batterybert-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the bat... | [
"# BatteryBERT-uncased for QA \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev set.... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatteryBERT-uncased for QA \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Extr... |
fill-mask | transformers |
# BatteryBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [bert-base-uncased](https://huggingface.co/bert-base-uncased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](htt... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batterybert-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between e... | [
"# BatteryBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\n... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) object... |
text-classification | transformers |
# BatteryOnlyBERT-cased for Battery Abstract Classification
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrast... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batteryonlybert-cased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryOnlyBERT-cased for Battery Abstract Classification
Language model: batteryonlybert-cased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## U... | [
"# BatteryOnlyBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-cased\r\nLanguage:... |
question-answering | transformers |
# BatteryOnlyBERT-cased for QA
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batteryonlybert-cased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatteryOnlyBERT-cased for QA
Language model: batteryonlybert-cased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the... | [
"# BatteryOnlyBERT-cased for QA \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev ... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-cased for QA \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: ... |
text-classification | transformers |
# BatteryOnlyBERT-uncased for Battery Abstract Classification
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Inf... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batteryonlybert-uncased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatteryOnlyBERT-uncased for Battery Abstract Classification
Language model: batteryonlybert-uncased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
... | [
"# BatteryOnlyBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-uncased\r\nLangu... |
question-answering | transformers |
# BatteryOnlyBERT-uncased for QA
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batteryonlybert-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatteryOnlyBERT-uncased for QA
Language model: batteryonlybert-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on... | [
"# BatteryOnlyBERT-uncased for QA \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 ... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatteryOnlyBERT-uncased for QA \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-ta... |
text-classification | transformers |
# BatterySciBERT-cased for Battery Abstract Classification
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastru... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batteryscibert-cased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatterySciBERT-cased for Battery Abstract Classification
Language model: batteryscibert-cased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## Usa... | [
"# BatterySciBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"##... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatterySciBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryscibert-cased\r\nLanguage: E... |
question-answering | transformers |
# BatterySciBERT-cased for QA
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
b... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batteryscibert-cased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatterySciBERT-cased for QA
Language model: batteryscibert-cased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the b... | [
"# BatterySciBERT-cased for QA \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev se... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatterySciBERT-cased for QA \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Ex... |
fill-mask | transformers |
# BatterySciBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repos... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batteryscibert-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatterySciBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between engli... | [
"# BatterySciBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it makes a difference betwe... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatterySciBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objec... |
text-classification | transformers |
# BatterySciBERT-uncased for Battery Abstract Classification
**Language model:** batteryscibert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infra... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/batteryscibert-uncased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatterySciBERT-uncased for Battery Abstract Classification
Language model: batteryscibert-uncased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
##... | [
"# BatterySciBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatterySciBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryscibert-uncased\r\nLanguag... |
question-answering | transformers |
# BatterySciBERT-uncased for QA
**Language model:** batteryscibert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
``... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/batteryscibert-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BatterySciBERT-uncased for QA
Language model: batteryscibert-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on t... | [
"# BatterySciBERT-uncased for QA \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 de... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BatterySciBERT-uncased for QA \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task... |
fill-mask | transformers |
# BatterySciBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. It was introduced in
[this paper](paper_link) and first released in
[this... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]} | batterydata/batteryscibert-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BatterySciBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between ... | [
"# BatterySciBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BatterySciBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) obj... |
text-classification | transformers |
# BERT-base-cased for Battery Abstract Classification
**Language model:** bert-base-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/bert-base-cased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT-base-cased for Battery Abstract Classification
Language model: bert-base-cased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## Usage
### In... | [
"# BERT-base-cased for Battery Abstract Classification \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performan... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-base-cased for Battery Abstract Classification \r\nLanguage model: bert-base-cased\r\nLanguage: English \r... |
question-answering | transformers |
# BERT-base-cased for QA
**Language model:** bert-base-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size ... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/bert-base-cased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BERT-base-cased for QA
Language model: bert-base-cased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the battery dev... | [
"# BERT-base-cased for QA \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\n... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERT-base-cased for QA \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Extractive Q... |
text-classification | transformers |
# BERT-base-uncased for Battery Abstract Classification
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure*... | {"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"} | batterydata/bert-base-uncased-abstract | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT-base-uncased for Battery Abstract Classification
Language model: bert-base-uncased
Language: English
Downstream-task: Text Classification
Training data: training\_data.csv
Eval data: val\_data.csv
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
## Usage
##... | [
"# BERT-base-uncased for Battery Abstract Classification \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Perfo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-base-uncased for Battery Abstract Classification \r\nLanguage model: bert-base-uncased\r\nLanguage: English... |
question-answering | transformers |
# BERT-base-cased for QA
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_siz... | {"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"} | batterydata/bert-base-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BERT-base-cased for QA
Language model: bert-base-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
## Hyperparameters
## Performance
Evaluated on the SQuAD v1.0 dev set.
Evaluated on the battery d... | [
"# BERT-base-cased for QA \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100",
"## Hyperparameters",
"## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r... | [
"TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERT-base-cased for QA \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Extractive... |
fill-mask | transformers |
# ALBERT-Mongolian
[pretraining repo link](https://github.com/bayartsogt-ya/albert-mongolian)
## Model description
Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.
## Evaluatio... | {"language": "mn"} | bayartsogt/albert-mongolian | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"mn",
"arxiv:1904.00962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.00962"
] | [
"mn"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #mn #arxiv-1904.00962 #autotrain_compatible #endpoints_compatible #region-us
|
# ALBERT-Mongolian
pretraining repo link
## Model description
Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.
## Evaluation Result:
## Fine-tuning Result on Eduge Dataset:
... | [
"# ALBERT-Mongolian\npretraining repo link",
"## Model description\nHere we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.",
"## Evaluation Result:",
"## Fine-tuning Result o... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #mn #arxiv-1904.00962 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALBERT-Mongolian\npretraining repo link",
"## Model description\nHere we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Train... |
null | null | |fold|accuracy|
|-|-|
| fold 0 | 0.974197247706422 |
| fold 1 | 0.9627293577981652 |
| fold 2 | 0.9724770642201835 |
| fold 3 | 0.9696100917431193 |
| fold 4 | 0.9684633027522935 |
| OOF Acc | 0.9694954128440367 | | {} | bayartsogt/mlub-bert-base-uncased-tr5meaning | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | |
null | null | |fold|accuracy|
|-|-|
| fold 0 | 0.9730504587155964 |
| fold 1 | 0.9690366972477065 |
| fold 2 | 0.970756880733945 |
| fold 3 | 0.9684633027522935 |
| fold 4 | 0.9719036697247706 |
| OOF Acc | 0.9706422018348624 | | {} | bayartsogt/mlub-bert-large-cased-tr5do30ep25s42 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | |
null | null | |fold|accuracy|
|-|-|
| fold 0 | 0.9753440366972477 |
| fold 1 | 0.9678899082568807 |
| fold 2 | 0.9747706422018348 |
| fold 3 | 0.9690366972477065 |
| fold 4 | 0.9759174311926605 |
| OOF Acc | 0.9725917431192661 | | {} | bayartsogt/mlub-bert-large-uncased-tr5do20ep25s42 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | |
null | null | |fold|accuracy|
|-|-|
| fold 0 | 0.974197247706422 |
| fold 1 | 0.9678899082568807 |
| fold 2 | 0.9724770642201835 |
| fold 3 | 0.9701834862385321 |
| fold 4 | 0.9736238532110092 |
| OOF Acc | 0.9716743119266055 |
```
synset_word
ав 1.000000
ам 0.931507
баг 0.980000
байр 0.943548
бараа ... | {} | bayartsogt/mlub-bert-large-uncased-tr5do30ep25 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | |
fill-mask | transformers | # StructBERT: Un-Official Copy
Official Repository Link: https://github.com/alibaba/AliceMind/tree/main/StructBERT
**Claimer**
* This model card is not produced by [AliceMind Team](https://github.com/alibaba/AliceMind/)
## Reproduce HFHub models:
Download model/tokenizer vocab
```bash
wget https://raw.githubusercon... | {} | bayartsogt/structbert-large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:1908.04577",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.04577"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-1908.04577 #autotrain_compatible #endpoints_compatible #region-us
| StructBERT: Un-Official Copy
============================
Official Repository Link: URL
Claimer
* This model card is not produced by AliceMind Team
Reproduce HFHub models:
-----------------------
Download model/tokenizer vocab
URL
StructBERT: Incorporating Language Structures into Pre-training for Deep La... | [
"#### URL\n\n\nGLUE benchmark",
"#### URL\n\n\nCLUE benchmark\n\n\n\nExample usage\n-------------",
"#### Requirements and Installation\n\n\n* PyTorch version >= 1.0.1\n* Install other libraries via\n* For faster training install NVIDIA's apex library",
"#### Finetune MNLI\n\n\nIf you use our work, please cit... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1908.04577 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### URL\n\n\nGLUE benchmark",
"#### URL\n\n\nCLUE benchmark\n\n\n\nExample usage\n-------------",
"#### Requirements and Installation\n\n\n* PyTorch version >= 1.0.1\n* Install other ... |
text-to-speech | fairseq | # tts_transformer-mn-mbspeech
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Mongolian
- Single-speaker male voice
- Trained on [MBSpeech](https://github.c... | {"language": "mn", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["mbspeech"], "task": "text-to-speech", "widget": [{"text": "\u043c\u0438\u043d\u0438\u0439 \u043d\u044d\u0440\u0438\u0439\u0433 \u0431\u0430\u044f\u0440\u0446\u043e\u0433\u0442 \u0433\u044d\u0434\u044d\u0433", "ex... | bayartsogt/tts_transformer-mn-mbspeech | null | [
"fairseq",
"audio",
"text-to-speech",
"mn",
"dataset:mbspeech",
"arxiv:1809.08895",
"arxiv:2109.06912",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"mn"
] | TAGS
#fairseq #audio #text-to-speech #mn #dataset-mbspeech #arxiv-1809.08895 #arxiv-2109.06912 #region-us
| # tts_transformer-mn-mbspeech
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Mongolian
- Single-speaker male voice
- Trained on MBSpeech
| [
"# tts_transformer-mn-mbspeech\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Mongolian\n- Single-speaker male voice\n- Trained on MBSpeech"
] | [
"TAGS\n#fairseq #audio #text-to-speech #mn #dataset-mbspeech #arxiv-1809.08895 #arxiv-2109.06912 #region-us \n",
"# tts_transformer-mn-mbspeech\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Mongolian\n- Single-speaker male voice\n- Trained on MBSpeech"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Mongolian-v1
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Mongolian 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 mo... | {"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice mn"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Mongolian V1 by Bayartsogt", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | bayartsogt/wav2vec2-large-xlsr-mongolian-v1 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mn",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Mongolian-v1
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian 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 evaluate... | [
"# Wav2Vec2-Large-XLSR-53-Mongolian-v1\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mod... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Mongolian-v1\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice.\n\nW... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Mongolian 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 ... | {"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Mongolian by Bayartsogt", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "... | bayartsogt/wav2vec2-large-xlsr-mongolian | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mn",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian 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 ... | [
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model c... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongol... |
sentence-similarity | sentence-transformers |
# bchan007/fnctech
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy w... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | bchan007/fnctech | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# bchan007/fnctech
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you c... | [
"# bchan007/fnctech\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# bchan007/fnctech\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | bdwjaya/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-xsum
This model is a fine-tuned version of t5-small on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
... |
text-generation | transformers |
RICK!!! | {"tags": ["conversational"]} | beatajackowska/DialoGPT-RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
RICK!!! | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# DiLBERT (Disease Language BERT)
The objective of this model was to obtain a specialized disease-related language, trained **from scratch**. <br>
We created a pre-training corpora starting from **ICD-11** entities, and enriched it with documents from **PubMed** and **Wikipedia** related to the same entities. <br>
R... | {"language": ["en"], "tags": ["medical", "disease", "classification"]} | beatrice-portelli/DiLBERT | null | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"medical",
"disease",
"classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #medical #disease #classification #en #autotrain_compatible #endpoints_compatible #region-us
| DiLBERT (Disease Language BERT)
===============================
The objective of this model was to obtain a specialized disease-related language, trained from scratch.
We created a pre-training corpora starting from ICD-11 entities, and enriched it with documents from PubMed and Wikipedia related to the same enti... | [
"### Composition of the pretraining corpus",
"### Main repository\n\n\nFor more details check the main repo URL\n\n\nUsage\n=====\n\n\nHow to cite\n==========="
] | [
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #medical #disease #classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Composition of the pretraining corpus",
"### Main repository\n\n\nFor more details check the main repo URL\n\n\nUsage\n=====\n\n\nHow to cite\n==========="
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned", "results": []}]} | begar/distilgpt2-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-finetuned
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# distilgpt2-finetuned\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-finetuned\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model desc... |
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. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc", "results": []}]} | begar/xlm-roberta-base-finetuned-marc | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-marc
===============================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0276
* Mae: 0.5310
Model description
-----------------
More information needed
Intend... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
null | null | from transformers import pipeline
import json
import requests
API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-neo-2.7B"
headers = {"Authorization": "Bearer api_hwKbAMoHAzOVDdCxgfpPxMjjcrdKHMakhg"}
def query(payload):
\tdata = json.dumps(payload)
\tresponse = requests.request("POST", API_URL, hea... | {} | begimayk/try1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| from transformers import pipeline
import json
import requests
API_URL = "URL
headers = {"Authorization": "Bearer api_hwKbAMoHAzOVDdCxgfpPxMjjcrdKHMakhg"}
def query(payload):
\tdata = URL(payload)
\tresponse = requests.request("POST", API_URL, headers=headers, data=data)
\treturn URL(URL("utf-8"))
data = query("Can y... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# DaddyBen DialoGPT Model | {"tags": ["conversational"]} | benajtil/DialoGPT-small-Daddyben | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DaddyBen DialoGPT Model | [
"# DaddyBen DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DaddyBen DialoGPT Model"
] |
text-generation | transformers |
# Rick And Morty Scripts DialoGPT Model | {"tags": ["conversational"]} | benajtil/DialoGPT-small-RickAndMortyScripts | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick And Morty Scripts DialoGPT Model | [
"# Rick And Morty Scripts DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick And Morty Scripts DialoGPT Model"
] |
text-generation | transformers |
# GerPT2
German large and small versions of GPT2:
- https://huggingface.co/benjamin/gerpt2
- https://huggingface.co/benjamin/gerpt2-large
See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2... | {"language": "de", "license": "mit", "widget": [{"text": "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einh\u00f6rner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."}]} | benjamin/gerpt2-large | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GerPT2
======
German large and small versions of GPT2:
* URL
* URL
See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.
Comparison to dbmdz/german-gpt2
-------------------------------
I evaluated both GerPT2-large and the other German GPT2, dbmdz/... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# GerPT2
German large and small versions of GPT2:
- https://huggingface.co/benjamin/gerpt2
- https://huggingface.co/benjamin/gerpt2-large
See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2... | {"language": "de", "license": "mit", "widget": [{"text": "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einh\u00f6rner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."}]} | benjamin/gerpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GerPT2
======
German large and small versions of GPT2:
* URL
* URL
See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.
Comparison to dbmdz/german-gpt2
-------------------------------
I evaluated both GerPT2-large and the other German GPT2, dbmdz/... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# gpt2-wechsel-chinese
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Model | N... | {"language": "zh", "license": "mit"} | benjamin/gpt2-wechsel-chinese | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #zh #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wechsel-chinese
====================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper for details.
P... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #zh #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
text-generation | transformers |
# gpt2-wechsel-french
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Model | NL... | {"language": "fr", "license": "mit"} | benjamin/gpt2-wechsel-french | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wechsel-french
===================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper for details.
Ple... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
text-generation | transformers |
# gpt2-wechsel-german
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Model | NL... | {"language": "de", "license": "mit"} | benjamin/gpt2-wechsel-german | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wechsel-german
===================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper for details.
Ple... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
text-generation | transformers |
# gpt2-wechsel-swahili
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Model | N... | {"language": "sw", "license": "mit"} | benjamin/gpt2-wechsel-swahili | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"sw",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #sw #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wechsel-swahili
====================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper for details.
P... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #sw #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
fill-mask | transformers |
# roberta-base-wechsel-chinese
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| M... | {"language": "zh", "license": "mit"} | benjamin/roberta-base-wechsel-chinese | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"zh",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #fill-mask #zh #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wechsel-chinese
============================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper ... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #zh #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
fill-mask | transformers |
# roberta-base-wechsel-french
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Mo... | {"language": "fr", "license": "mit"} | benjamin/roberta-base-wechsel-french | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wechsel-french
===========================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper fo... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
fill-mask | transformers |
# roberta-base-wechsel-german
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Mo... | {"language": "de", "license": "mit"} | benjamin/roberta-base-wechsel-german | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wechsel-german
===========================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper fo... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
fill-mask | transformers |
# roberta-base-wechsel-swahili
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| M... | {"language": "sw", "license": "mit"} | benjamin/roberta-base-wechsel-swahili | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"sw",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #sw #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wechsel-swahili
============================
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: URL
And the paper here: URL
Performance
-----------
### RoBERTa
### GPT2
See our paper ... | [
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #sw #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### RoBERTa",
"### GPT2\n\n\n\n\n\n\nSee our paper for details.\n\n\nPlease cite WECHSEL as"
] |
text-generation | transformers |
Still figuring out to properly write model cards.
WIP. | {"language": ["en"], "license": "mit", "tags": ["conversational", "pytorch", "transformers", "gpt2"], "datasets": ["empathetic dialogues"]} | benjaminbeilharz/dialoGPT-small-empatheticdialogues-generation | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #conversational #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Still figuring out to properly write model cards.
WIP. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #conversational #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Misato Katsuragi DialoGPT Model
--- | {"tags": ["conversational"]} | benmrtnz27/DialoGPT-small-misato | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Misato Katsuragi DialoGPT Model
--- | [
"# Misato Katsuragi DialoGPT Model\n---"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Misato Katsuragi DialoGPT Model\n---"
] |
text-generation | transformers |
#GPTCartman | {"tags": ["conversational"]} | bensuydam/CartmanBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#GPTCartman | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | benyong/testmodel | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\... |
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