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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | {} | danny911kr/calm-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## CALM
This model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.
Checkout our Project website for details!
| [
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] |
text2text-generation | transformers | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | {} | danny911kr/calm-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## CALM
This model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.
Checkout our Project website for details!
| [
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] |
text2text-generation | transformers | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | {} | danny911kr/calm-mix-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## CALM
This model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.
Checkout our Project website for details!
| [
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] |
text2text-generation | transformers | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | {} | danny911kr/calm-mix-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## CALM
This model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.
Checkout our Project website for details!
| [
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## CALM\n\nThis model is for ICLR2021 paper: Pre-training Text-to-Text Transformers for Concept-centric Common Sense.\nCheckout our Project website for details!"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-or
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on odia using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | {"language": "or", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "odia XLSR Wav2Vec2 Large 2000", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | danurahul/wav2vec2-large-xlsr-or | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"or",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"or"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-or
Fine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows o... | [
"# Wav2Vec2-Large-XLSR-53-or \nFine-tuned facebook/wav2vec2-large-xlsr-53 on odia 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 can be evaluat... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-or \nFine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi 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 b... | {"language": "pa-IN", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "danurahul/wav2vec2-large-xlsr-pa-IN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | danurahul/wav2vec2-large-xlsr-pa-IN | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi 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 fo... | [
"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi 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 can be... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Vo... |
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-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | danwilbury/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"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 #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
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: 0.9302
* Mae: 0.5
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_answering_squad2")
input_ids = tokenizer.encode("There are two apples on the counter. Q: How many apples? A:", return_tensors="pt")
outputs = model.generate(input_ids)
print("Gene... | {} | danyaljj/gpt2_question_answering_squad2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Sample usage:
Which should produce this:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_generation_given_paragraph")
input_ids = tokenizer.encode("There are two apples on the counter. Q:", return_tensors="pt")
outputs = model.generate(input_ids)
print("Generated:", t... | {} | danyaljj/gpt2_question_generation_given_paragraph | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Sample usage:
Which should produce this:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_generation_given_paragraph_answer")
input_ids = tokenizer.encode("There are two apples on the counter. A: apples Q:", return_tensors="pt")
outputs = model.generate(input_ids)
print... | {} | danyaljj/gpt2_question_generation_given_paragraph_answer | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Sample usage:
Which should produce this:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | West et al.'s model from their "reflective decoding" paper.
Sample usage:
```python
import torch
from modeling_opengpt2 import OpenGPT2LMHeadModel
from padded_encoder import Encoder
path_to_backward = 'danyaljj/opengpt2_pytorch_backward'
encoder = Encoder()
model_backward = OpenGPT2LMHeadModel.from_pretrained(pat... | {} | danyaljj/opengpt2_pytorch_backward | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| West et al.'s model from their "reflective decoding" paper.
Sample usage:
Download the additional files from here: URL
| [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | West et al.'s model from their "reflective decoding" paper.
Sample usage:
```python
import torch
from modeling_opengpt2 import OpenGPT2LMHeadModel
from padded_encoder import Encoder
path_to_forward = 'danyaljj/opengpt2_pytorch_forward'
encoder = Encoder()
model_backward = OpenGPT2LMHeadModel.from_pretrained(path_... | {} | danyaljj/opengpt2_pytorch_forward | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| West et al.'s model from their "reflective decoding" paper.
Sample usage:
Download the additional files from here: URL
| [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | daqiao202/distilgpt2-finetuned-wikitext2 | 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-wikitext2
This model is a fine-tuned version of distilgpt2 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 hyperparameter... | [
"# distilgpt2-finetuned-wikitext2\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"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-wikitext2\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Mo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | dark-knight/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
text-generation | transformers |
# Chicken Bot's Jon Snow DialoGPT Model | {"tags": ["conversational"]} | darkzek/chickenbot-jon-snow | 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
|
# Chicken Bot's Jon Snow DialoGPT Model | [
"# Chicken Bot's Jon Snow DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chicken Bot's Jon Snow DialoGPT Model"
] |
text-generation | transformers |
# Pickle Rick DialoGPT Model | {"tags": ["conversational"]} | darthboii/DialoGPT-small-PickleRick | 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
|
# Pickle Rick DialoGPT Model | [
"# Pickle Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pickle Rick DialoGPT Model"
] |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | darthboii/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
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"# Rick DialoGPT Model"
] |
null | transformers | Hi
| {} | darubramha/hi-LyricsGPT2 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| Hi
| [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/monologg/JointBERT | {} | databuzzword/JointBERT-atis | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/monologg/JointBERT | {} | databuzzword/JointBERT-snips | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](ht... | {"language": "fr", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["synpaflex"], "widget": [{"text": "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous \u00e9tions amis"}]} | dathudeptrai/tts-tacotron2-synpaflex-fr | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"fr",
"dataset:synpaflex",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"fr"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #fr #dataset-synpaflex #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us
|
# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
This repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with... | [
"# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlo... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #fr #dataset-synpaflex #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us \n",
"# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaf... |
text-generation | transformers | La descripciΓ³n en EspaΓ±ol se encuentra despuΓ©s de la descripciΓ³n en InglΓ©s.
# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP tasks...)
GPT2-small-spanish is a state-of-the-art language model for Spanish based on the GPT-2 small model.
It was trained on Spanish Wikipedia usin... | {"language": "es", "license": "apache-2.0", "datasets": ["wikipedia"], "widget": [{"text": "La inteligencia artificial en lationoam\u00e9rica se ha desarrollado "}]} | datificate/gpt2-small-spanish | null | [
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"es",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #es #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| La descripciΓ³n en EspaΓ±ol se encuentra despuΓ©s de la descripciΓ³n en InglΓ©s.
# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP tasks...)
GPT2-small-spanish is a state-of-the-art language model for Spanish based on the GPT-2 small model.
It was trained on Spanish Wikipedia usin... | [
"# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP tasks...)\nGPT2-small-spanish is a state-of-the-art language model for Spanish based on the GPT-2 small model. \n\nIt was trained on Spanish Wikipedia using Transfer Learning and Fine-tuning techniques. The training took aro... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #es #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP tasks...)\nGPT2-sma... |
fill-mask | transformers | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phα»", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... | {} | datnth1709/Phobert-classifier | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00744"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #region-us
| PhoBERT: Pre-trained language models for Vietnamese
====================================================
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. "Phα»", is a popular food in Vietnam):
* Two PhoBERT versions of "base" and "large" are the first public large-scale ... | [
"### Installation\n\n\n* Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)\n* Install 'transformers':\n- 'git clone URL\n- 'cd transformers'\n- 'pip3 install --upgrade .'",
"### Pre-trained models",
"### Example usage"
] | [
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"### Installation\n\n\n* Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)\n* Install 'transformers':\n- 'git clone URL\n- 'cd transformers'\n- 'pip3 install --upgrade .'",
... |
text-generation | transformers | #Harry Potter DialoGPT Model | {"tags": ["conversational"]} | dats/DialoGPT-small-harrypotter | 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
| #Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Tony Stark DialoGPT model
Invite me to your discord server : https://discord.com/api/oauth2/authorize?client_id=885065886787063848&permissions=137439365184&scope=bot | {"tags": ["conversational"]} | dattam/DialoGPT-medium-TonyStarkBot | 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
|
# Tony Stark DialoGPT model
Invite me to your discord server : URL | [
"# Tony Stark DialoGPT model\n\nInvite me to your discord server : URL"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT model\n\nInvite me to your discord server : URL"
] |
token-classification | transformers | BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus along with selected pubtator annotations from LitCOVID dataset
This was fine-tuned in order to use it in a datummd/bionlp system which is available at: https://github.com/datummd/bionlp
| {"language": ["en"], "license": "apache-2.0", "tags": ["BioBERT", "Diseases", "NER"], "datasets": ["ncbi_disease", "BC5CDR-diseases", "LitCOVID-pubtator"]} | datummd/NCBI_BC5CDR_disease | null | [
"transformers",
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"bert",
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"BioBERT",
"Diseases",
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"dataset:ncbi_disease",
"dataset:BC5CDR-diseases",
"dataset:LitCOVID-pubtator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #BioBERT #Diseases #NER #en #dataset-ncbi_disease #dataset-BC5CDR-diseases #dataset-LitCOVID-pubtator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus along with selected pubtator annotations from LitCOVID dataset
This was fine-tuned in order to use it in a datummd/bionlp system which is available at: URL
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #BioBERT #Diseases #NER #en #dataset-ncbi_disease #dataset-BC5CDR-diseases #dataset-LitCOVID-pubtator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | fastai |
## Model description
This model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.
This model was trained on data created from the Digitised printed books (18th-19th Century) book collection. The datasets in this collection are comprised and derived from 49,455 digitised book... | {"library_name": "fastai", "tags": ["text-classification", "fastai"], "datasets": ["blbooksgenre"], "widget": [{"text": "Poems on various subjects. Whereto is prefixed a short essay on the structure of English verse"}, {"text": "Two Centuries of Soho: its institutions, firms, and amusements. By the Clergy of St. Anne's... | TheBritishLibrary/bl-books-genre-fastai | null | [
"fastai",
"text-classification",
"dataset:blbooksgenre",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fastai #text-classification #dataset-blbooksgenre #region-us
|
## Model description
This model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.
This model was trained on data created from the Digitised printed books (18th-19th Century) book collection. The datasets in this collection are comprised and derived from 49,455 digitised book... | [
"## Model description\n\nThis model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.\n\nThis model was trained on data created from the Digitised printed books (18th-19th Century) book collection. The datasets in this collection are comprised and derived from 49,455 digiti... | [
"TAGS\n#fastai #text-classification #dataset-blbooksgenre #region-us \n",
"## Model description\n\nThis model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.\n\nThis model was trained on data created from the Digitised printed books (18th-19th Century) book collection. ... |
null | adapter-transformers |
# Adapter `davanstrien/book-genre-classification` for bert-base-cased
An [adapter](https://adapterhub.ml) for the `bert-base-cased` model that was trained on the [text-classification](https://adapterhub.ml/explore/text-classification/) dataset and includes a prediction head for classification.
This adapter was creat... | {"tags": ["bert", "adapterhub:text-classification", "adapter-transformers"]} | davanstrien/book-genre-classification | null | [
"adapter-transformers",
"bert",
"adapterhub:text-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #bert #adapterhub-text-classification #region-us
|
# Adapter 'davanstrien/book-genre-classification' for bert-base-cased
An adapter for the 'bert-base-cased' model that was trained on the text-classification dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install '... | [
"# Adapter 'davanstrien/book-genre-classification' for bert-base-cased\n\nAn adapter for the 'bert-base-cased' model that was trained on the text-classification dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFi... | [
"TAGS\n#adapter-transformers #bert #adapterhub-text-classification #region-us \n",
"# Adapter 'davanstrien/book-genre-classification' for bert-base-cased\n\nAn adapter for the 'bert-base-cased' model that was trained on the text-classification dataset and includes a prediction head for classification.\n\nThis ada... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext_flyswot
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["f1"], "base_model": "facebook/convnext-base-224-22k", "model-index": [{"name": "convnext_flyswot", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_fo... | davanstrien/convnext_flyswot | null | [
"transformers",
"pytorch",
"safetensors",
"convnext",
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"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/convnext-base-224-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| convnext\_flyswot
=================
This model is a fine-tuned version of facebook/convnext-base-224-22k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1441
* F1: 0.9592
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 666\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpar... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext_manuscript_iiif
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["f1"], "base_model": "facebook/convnext-base-224-22k", "model-index": [{"name": "convnext_manuscript_iiif", "results": []}]} | davanstrien/convnext_manuscript_iiif | null | [
"transformers",
"pytorch",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-base-224-22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #convnext #image-classification #generated_from_trainer #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| convnext\_manuscript\_iiif
==========================
This model is a fine-tuned version of facebook/convnext-base-224-22k on the davanstrien/iiif\_manuscripts\_label\_ge\_50 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.5856
* F1: 0.0037
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30.0\n* mixed\... | [
"TAGS\n#transformers #pytorch #safetensors #convnext #image-classification #generated_from_trainer #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
object-detection | transformers |
# detr_beyond_words (WIP)
[facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) fine tuned on [Beyond Words](https://github.com/LibraryOfCongress/newspaper-navigator/tree/master/beyond_words_data). | {"license": "mit", "tags": ["object-detection"], "widget": [{"src": "https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/19.jpg", "example_title": "page"}, {"src": "https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/65.jpg", "example_title": "page2"}]} | davanstrien/detr_beyond_words | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #detr #object-detection #license-mit #endpoints_compatible #region-us
|
# detr_beyond_words (WIP)
facebook/detr-resnet-50 fine tuned on Beyond Words. | [
"# detr_beyond_words (WIP) \n\nfacebook/detr-resnet-50 fine tuned on Beyond Words."
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #detr #object-detection #license-mit #endpoints_compatible #region-us \n",
"# detr_beyond_words (WIP) \n\nfacebook/detr-resnet-50 fine tuned on Beyond Words."
] |
null | null | # flyswot
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
#### Limitations and bias
Not currently intended for public consumption...
## Training data
TODO
## Eval results
| {} | davanstrien/flyswot-test | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #region-us
| # flyswot
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
#### Limitations and bias
Not currently intended for public consumption...
## Training data
TODO
## Eval results
| [
"# flyswot",
"## Model description\n\nIn progress model for detecting 'fake' flysheets",
"## Intended uses & limitations\n\nNot currently intended for public consumption...",
"#### Limitations and bias\n\nNot currently intended for public consumption...",
"## Training data\n\nTODO",
"## Eval results"
] | [
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"# flyswot",
"## Model description\n\nIn progress model for detecting 'fake' flysheets",
"## Intended uses & limitations\n\nNot currently intended for public consumption...",
"#### Limitations and bias\n\nNot currently intended for public consumption...",
"## Training data\n\n... |
null | null | TODO
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
## Limitations and bias
Not currently intended for public consumption...
## Training data
## Eval results | {} | davanstrien/flyswot | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #region-us
| TODO
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
## Limitations and bias
Not currently intended for public consumption...
## Training data
## Eval results | [
"## Model description\n\nIn progress model for detecting 'fake' flysheets",
"## Intended uses & limitations\n\nNot currently intended for public consumption...",
"## Limitations and bias\n\nNot currently intended for public consumption...",
"## Training data",
"## Eval results"
] | [
"TAGS\n#onnx #region-us \n",
"## Model description\n\nIn progress model for detecting 'fake' flysheets",
"## Intended uses & limitations\n\nNot currently intended for public consumption...",
"## Limitations and bias\n\nNot currently intended for public consumption...",
"## Training data",
"## Eval results... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flyswot_iiif
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "facebook/convnext-base-224-22k", "model-index": [{"name": "flyswot_iiif", "results": []}]} | davanstrien/flyswot_iiif | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-base-224-22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #generated_from_trainer #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| flyswot\_iiif
=============
This model is a fine-tuned version of facebook/convnext-base-224-22k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1280
* F1: 0.0034
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 666\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #generated_from_trainer #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flyswot_test
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "base_model": "facebook/convnext-base-224-22k", "model-index": [{"name": "flyswot_test", "results": []}]} | davanstrien/flyswot_test | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/convnext-base-224-22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# flyswot_test
This model is a fine-tuned version of facebook/convnext-base-224-22k on the image_folder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1518
- eval_f1: 0.9595
- eval_runtime: 5.9337
- eval_samples_per_second: 69.603
- eval_steps_per_second: 2.191
- epoch: 7.0
- step:... | [
"# flyswot_test\n\nThis model is a fine-tuned version of facebook/convnext-base-224-22k on the image_folder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.1518\n- eval_f1: 0.9595\n- eval_runtime: 5.9337\n- eval_samples_per_second: 69.603\n- eval_steps_per_second: 2.191\n- epoch: ... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/convnext-base-224-22k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# flyswot_test\n\nThis model is a fine-tuned version of facebook/convnext-base-224... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# iiif_manuscript_vit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "iiif_manuscript_vit", "results": []}]} | davanstrien/iiif_manuscript_vit | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| iiif\_manuscript\_vit
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5684
* F1: 0.5996
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | generic |
# TODO
-
-
-
- | {"library_name": "generic", "tags": ["chemistry"]} | davanstrien/test | null | [
"generic",
"chemistry",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #chemistry #region-us
|
# TODO
-
-
-
- | [
"# TODO\n-\n-\n-\n-"
] | [
"TAGS\n#generic #chemistry #region-us \n",
"# TODO\n-\n-\n-\n-"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-manuscripts
This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on t... | {"license": "apache-2.0", "tags": ["masked-auto-encoding", "generated_from_trainer"], "base_model": "facebook/vit-mae-base", "model-index": [{"name": "vit-manuscripts", "results": []}]} | davanstrien/vit-manuscripts | null | [
"transformers",
"pytorch",
"tensorboard",
"vit_mae",
"pretraining",
"masked-auto-encoding",
"generated_from_trainer",
"base_model:facebook/vit-mae-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us
| vit-manuscripts
===============
This model is a fine-tuned version of facebook/vit-mae-base on the davanstrien/manuscript\_iiif\_test dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5177
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\... | [
"TAGS\n#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_flyswot_test
This model is a fine-tuned version of [](https://huggingface.co/) on the image_folder dataset.
It achieves the ... | {"tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["f1"], "model-index": [{"name": "vit_flyswot_test", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": "default"}, "metrics": [{"type": "... | davanstrien/vit_flyswot_test | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit\_flyswot\_test
==================
This model is a fine-tuned version of [](URL on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4777
* F1: 0.8492
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 666\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | daveccampbell/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"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 #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
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: 0.9199
* Mae: 0.4756
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
**Note**: This model & model card are based on the [finetuned XLM-T for Sentiment Analysis](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)
# twitter-XLM-roBERTa-base for Emotion Analysis
This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish langu... | {"language": ["es"], "tags": ["Emotion Analysis"]} | daveni/twitter-xlm-roberta-emotion-es | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"Emotion Analysis",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #Emotion Analysis #es #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Note: This model & model card are based on the finetuned XLM-T for Sentiment Analysis
# twitter-XLM-roBERTa-base for Emotion Analysis
This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish language. This model was presented to EmoEvalEs competition, part of IberLEF 2021... | [
"# twitter-XLM-roBERTa-base for Emotion Analysis\nThis is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish language. This model was presented to EmoEvalEs competition, part of IberLEF 2021 Conference, where the proposed task was the classification of Spanish tweets betw... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #Emotion Analysis #es #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# twitter-XLM-roBERTa-base for Emotion Analysis\nThis is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish lang... |
null | null | Relevance prediction model | {} | davinan/relevance_prediction | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Relevance prediction model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
A small french language model for french text generation (and possibly more NLP tasks...)
**Introduction**
This french gpt2 model is based on openai GPT-2 small model.
It was trained on a <b>very small (190Mb) dataset </b> from french wikipedia using Transfer Learning and Fine-tuning techniques in just over a day, ... | {"language": "fr", "tags": ["french", "gpt2", "model"]} | dbddv01/gpt2-french-small | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"french",
"model",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #french #model #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
A small french language model for french text generation (and possibly more NLP tasks...)
Introduction
This french gpt2 model is based on openai GPT-2 small model.
It was trained on a <b>very small (190Mb) dataset </b> from french wikipedia using Transfer Learning and Fine-tuning techniques in just over a day, on o... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #french #model #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-italian-robust
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/faceb... | {"language": ["it"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-1b - Italian", "results": [{"task": {"type": "automatic-speech-recognition",... | dbdmg/wav2vec2-xls-r-1b-italian-robust | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"reg... | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-1b-italian-robust
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the Common Voice 7 & Libri Speech datasets.
It achieves the following results on the evaluation set:
* Loss: 0.2428
* Wer: 0.2960
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-italian-robust
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"language": "it", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "XLS-R-300m - Italian", "results": [{"t... | dbdmg/wav2vec2-xls-r-300m-italian-robust | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_7_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-italian-robust
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Italian splits of the following datasets:
* Mozilla Foundation Common Voice V7 dataset
* LibriSpeech multilingual
* TED multilingual
* Voxforge
* M-AILABS Speech Dataset
* ... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \... |
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-xls-r-300m-italian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook... | {"language": ["it"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300m - Italian", "results": [{"task":... | dbdmg/wav2vec2-xls-r-300m-italian | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-italian
===========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - IT dataset.
It achieves the following results on the evaluation set:
* Loss: inf
* Wer: 0.1710
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #it #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
... |
text2text-generation | transformers | # algebra_linear_1d
---
language: en
datasets:
- algebra_linear_1d
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/algebra_linear_1d](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetalgebra_linear_1d_defaul... | {} | dbernsohn/algebra_linear_1d | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # algebra_linear_1d
---
language: en
datasets:
- algebra_linear_1d
---
This is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d for solving algebra 1d equations mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to solve algebra 1... | [
"# algebra_linear_1d\n---\nlanguage: en\ndatasets:\n- algebra_linear_1d\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d for solving algebra 1d equations mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# algebra_linear_1d\n---\nlanguage: en\ndatasets:\n- algebra_linear_1d\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d for solving alg... |
text2text-generation | transformers | # algebra_linear_1d_composed
---
language: en
datasets:
- algebra_linear_1d_composed
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/algebra_linear_1d_composed](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdata... | {} | dbernsohn/algebra_linear_1d_composed | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # algebra_linear_1d_composed
---
language: en
datasets:
- algebra_linear_1d_composed
---
This is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d_composed for solving algebra linear 1d composed equations mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You... | [
"# algebra_linear_1d_composed\n---\nlanguage: en\ndatasets:\n- algebra_linear_1d_composed\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d_composed for solving algebra linear 1d composed equations mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sente... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# algebra_linear_1d_composed\n---\nlanguage: en\ndatasets:\n- algebra_linear_1d_composed\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/algebra_linear_... |
fill-mask | transformers | # roberta-go
---
language: Go
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Golang** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transform... | {} | dbernsohn/roberta-go | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-go
---
language: Go
datasets:
- code_search_net
---
This is a roberta pre-trained version on the CodeSearchNet dataset for Golang Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to fill masked words in a Java code.
... | [
"# roberta-go\n---\nlanguage: Go\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Golang Mask Language Model mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to fill masked words... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-go\n---\nlanguage: Go\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Golang Mask Language Model mission.\n\n... |
fill-mask | transformers | # roberta-java
---
language: Java
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Java** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transfo... | {} | dbernsohn/roberta-java | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-java
---
language: Java
datasets:
- code_search_net
---
This is a roberta pre-trained version on the CodeSearchNet dataset for Java Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to fill masked words in a Java code.... | [
"# roberta-java\n---\nlanguage: Java\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Java Mask Language Model mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to fill masked wor... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-java\n---\nlanguage: Java\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Java Mask Language Model mission.\n... |
fill-mask | transformers | # roberta-javascript
---
language: javascript
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **javascript** Mask Language Model mission.
To load the model:
(necessary packages: !p... | {} | dbernsohn/roberta-javascript | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-javascript
---
language: javascript
datasets:
- code_search_net
---
This is a roberta pre-trained version on the CodeSearchNet dataset for javascript Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to fill masked wor... | [
"# roberta-javascript\n---\nlanguage: javascript\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for javascript Mask Language Model mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-javascript\n---\nlanguage: javascript\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for javascript Mask Languag... |
fill-mask | transformers | # roberta-php
---
language: php
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **php** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transforme... | {} | dbernsohn/roberta-php | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # roberta-php
---
language: php
datasets:
- code_search_net
---
This is a roberta pre-trained version on the CodeSearchNet dataset for php Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to fill masked words in a Java code.
... | [
"# roberta-php\n---\nlanguage: php\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for php Mask Language Model mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to fill masked words ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# roberta-php\n---\nlanguage: php\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for php Mask Language Model mi... |
fill-mask | transformers | # roberta-python
---
language: python
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Python** Mask Language Model mission.
To load the model:
(necessary packages: !pip install t... | {} | dbernsohn/roberta-python | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-python
---
language: python
datasets:
- code_search_net
---
This is a roberta pre-trained version on the CodeSearchNet dataset for Python Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to fill masked words in a Pyth... | [
"# roberta-python\n---\nlanguage: python\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Python Mask Language Model mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to fill mask... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-python\n---\nlanguage: python\ndatasets:\n- code_search_net\n---\n\nThis is a roberta pre-trained version on the CodeSearchNet dataset for Python Mask Language Model miss... |
text2text-generation | transformers | # measurement_time
---
language: en
datasets:
- measurement_time
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/measurement_time](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetmeasurement_time) for solvi... | {} | dbernsohn/t5_measurement_time | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # measurement_time
---
language: en
datasets:
- measurement_time
---
This is a t5-small fine-tuned version on the math_dataset/measurement_time for solving measurement time equations mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to solve algebr... | [
"# measurement_time\n---\nlanguage: en\ndatasets:\n- measurement_time\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/measurement_time for solving measurement time equations mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this mod... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# measurement_time\n---\nlanguage: en\ndatasets:\n- measurement_time\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/measurement_time for solving measur... |
text2text-generation | transformers | # numbers_gcd
---
language: en
datasets:
- numbers_gcd
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/numbers_gcd](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetnumbers_gcd) for solving **greatest common... | {} | dbernsohn/t5_numbers_gcd | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # numbers_gcd
---
language: en
datasets:
- numbers_gcd
---
This is a t5-small fine-tuned version on the math_dataset/numbers_gcd for solving greatest common divisor mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to solve algebra 1d equations int... | [
"# numbers_gcd\n---\nlanguage: en\ndatasets:\n- numbers_gcd\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/numbers_gcd for solving greatest common divisor mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to solve algebr... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# numbers_gcd\n---\nlanguage: en\ndatasets:\n- numbers_gcd\n---\n\nThis is a t5-small fine-tuned version on the math_dataset/numbers_gcd for solving greatest common divis... |
text2text-generation | transformers | # t5_wikisql_SQL2en
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **SQL** to **English** **translation** text2text mission.
To load the m... | {} | dbernsohn/t5_wikisql_SQL2en | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # t5_wikisql_SQL2en
---
language: en
datasets:
- wikisql
---
This is a t5-small fine-tuned version on the wikisql dataset for SQL to English translation text2text mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to translate SQL queries into plain... | [
"# t5_wikisql_SQL2en\n---\nlanguage: en\ndatasets:\n- wikisql\n---\n\nThis is a t5-small fine-tuned version on the wikisql dataset for SQL to English translation text2text mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to translate SQL ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_wikisql_SQL2en\n---\nlanguage: en\ndatasets:\n- wikisql\n---\n\nThis is a t5-small fine-tuned version on the wikisql dataset for SQL to English translation text2text... |
text2text-generation | transformers | # t5_wikisql_en2SQL
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **English** to **SQL** **translation** text2text mission.
To load the m... | {} | dbernsohn/t5_wikisql_en2SQL | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # t5_wikisql_en2SQL
---
language: en
datasets:
- wikisql
---
This is a t5-small fine-tuned version on the wikisql dataset for English to SQL translation text2text mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
You can then use this model to translate SQL queries into plain... | [
"# t5_wikisql_en2SQL\n---\nlanguage: en\ndatasets:\n- wikisql\n---\n\nThis is a t5-small fine-tuned version on the wikisql dataset for English to SQL translation text2text mission.\n\nTo load the model:\n(necessary packages: !pip install transformers sentencepiece)\n\n\nYou can then use this model to translate SQL ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# t5_wikisql_en2SQL\n---\nlanguage: en\ndatasets:\n- wikisql\n---\n\nThis is a t5-small fine-tuned version on the wikisql dataset for English to SQL translatio... |
feature-extraction | generic |
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call... | {"library_name": "generic", "tags": ["feature-extraction"]} | dbguilherme/teste | null | [
"generic",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #feature-extraction #region-us
|
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These me... | [
"# Feature Extraction repository template\n\nThis is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' methods.... | [
"TAGS\n#generic #feature-extraction #region-us \n",
"# Feature Extraction repository template\n\nThis is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. ... |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "finnish", "license": "mit", "widget": [{"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}]} | dbmdz/bert-base-finnish-europeana-cased | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"finnish"
] | TAGS
#transformers #pytorch #jax #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # π€ + π dbmdz BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana BERT models π
# French Europeana BERT
We extracted all French texts using the `language` metadata attribute from the Europeana corpus.
The resulting corpus has a size of 63... | {"language": "fr", "license": "mit", "tags": ["historic french"]} | dbmdz/bert-base-french-europeana-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"historic french",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #jax #bert #historic french #fr #license-mit #endpoints_compatible #region-us
| # + dbmdz BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana BERT models
# French Europeana BERT
We extracted all French texts using the 'language' metadata attribute from the Europeana corpus.
The resulting corpus has a size of 63GB and... | [
"# + dbmdz BERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana BERT models",
"# French Europeana BERT\n\nWe extracted all French texts using the 'language' metadata attribute from the Europeana corpus.\n\nThe resulting corpus has a s... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #historic french #fr #license-mit #endpoints_compatible #region-us \n",
"# + dbmdz BERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana BERT models",
"# French Europeana BERT\n\nWe extrac... |
fill-mask | transformers |
# π€ + π dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models π
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [deepset](https://deepset.ai/) we pr... | {"language": "de", "license": "mit"} | dbmdz/bert-base-german-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz German BERT models
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models
German BERT
===========
Stats
-----
In addition to the recently released German BERT
model by deepset we provide another German-lang... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# π€ + π dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models π
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The European Library*. The ... | {"language": "de", "license": "mit", "tags": ["historic german"]} | dbmdz/bert-base-german-europeana-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"historic german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #bert #historic german #de #license-mit #endpoints_compatible #region-us
| + dbmdz BERT models
===================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models
German Europeana BERT
=====================
We use the open source Europeana newspapers
that were provided by *The European Library*. The final
tr... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #historic german #de #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
# π€ + π dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models π
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The European Library*. The ... | {"language": "de", "license": "mit", "tags": ["historic german"]} | dbmdz/bert-base-german-europeana-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"historic german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #bert #historic german #de #license-mit #endpoints_compatible #region-us
| + dbmdz BERT models
===================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models
German Europeana BERT
=====================
We use the open source Europeana newspapers
that were provided by *The European Library*. The final
tr... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #historic german #de #license-mit #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models π
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [deepset](https://deepset.ai/) we pr... | {"language": "de", "license": "mit"} | dbmdz/bert-base-german-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz German BERT models
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models
German BERT
===========
Stats
-----
In addition to the recently released German BERT
model by deepset we provide another German-lang... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Language Model for Historic Dutch
In this repository we open source a language model for Historic Dutch, trained on the
[Delpher Corpus](https://www.delpher.nl/over-delpher/delpher-open-krantenarchief/download-teksten-kranten-1618-1879\),
that include digitized texts from Dutch newspapers, ranging from 1618 to 1879... | {"language": "dutch", "license": "mit", "widget": [{"text": "de [MASK] vau Financien, in hec vorige jaar, da inkomswi"}]} | dbmdz/bert-base-historic-dutch-cased | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dutch"
] | TAGS
#transformers #pytorch #tf #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Language Model for Historic Dutch
=================================
In this repository we open source a language model for Historic Dutch, trained on the
Delpher Corpus,
that include digitized texts from Dutch newspapers, ranging from 1618 to 1879.
Changelog
=========
* 13.12.2021: Initial version of this reposit... | [] | [
"TAGS\n#transformers #pytorch #tf #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
π¨ Notice: After re-checking this model again, it seems that the model is not working very well. E.g. MLM predictions are very likely to predict `[UNK]` token, which is
actually not good.
We will update this model soon. For now, please use the [`bigscience-historical-texts/bert-base-blbooks-cased`](https://huggingfac... | {"language": "en", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}]} | dbmdz/bert-base-historic-english-cased | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Notice: After re-checking this model again, it seems that the model is not working very well. E.g. MLM predictions are very likely to predict '[UNK]' token, which is
actually not good.
We will update this model soon. For now, please use the 'bigscience-historical-texts/bert-base-blbooks-cased' instead, as it was pre... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# hmBERT: Historical Multilingual Language Models for Named Entity Recognition
More information about our hmBERT model can be found in our new paper:
["hmBERT: Historical Multilingual Language Models for Named Entity Recognition"](https://arxiv.org/abs/2205.15575).
## Languages
Our Historic Language Models Zoo cont... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-base-historic-multilingual-cased | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:2205.15575",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2205.15575"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-2205.15575 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| hmBERT: Historical Multilingual Language Models for Named Entity Recognition
============================================================================
More information about our hmBERT model can be found in our new paper:
"hmBERT: Historical Multilingual Language Models for Named Entity Recognition".
Languages
-... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-2205.15575 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/bert-base-italian-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/bert-base-italian-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/bert-base-italian-xxl-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/bert-base-italian-xxl-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "swedish", "license": "mit", "widget": [{"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}]} | dbmdz/bert-base-swedish-europeana-cased | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"swedish"
] | TAGS
#transformers #pytorch #jax #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and evaluation are contributed from the
aw... | {"language": "tr", "license": "mit"} | dbmdz/bert-base-turkish-128k-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"tr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz Turkish BERT model
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish
πΉπ· BERTurk
==========
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and evaluation... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and evaluation are contributed from t... | {"language": "tr", "license": "mit"} | dbmdz/bert-base-turkish-128k-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"tr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz Turkish BERT model
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish
πΉπ· BERTurk
==========
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and evalu... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and evaluation are contributed from the
aw... | {"language": "tr", "license": "mit"} | dbmdz/bert-base-turkish-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"tr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz Turkish BERT model
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish
πΉπ· BERTurk
==========
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and evaluation... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and evaluation are contributed from t... | {"language": "tr", "license": "mit"} | dbmdz/bert-base-turkish-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"tr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz Turkish BERT model
==========================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish
πΉπ· BERTurk
==========
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and evalu... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #tr #license-mit #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-medium-historic-multilingual-cased | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:1908.08962",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmBERT Mini\n\n\nThe following plot shows the pretraining loss curve for the mini model:\n\n\n!Training loss curve",
"### hmBERT Small\n\n\nThe following plot shows the pretraining loss... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmB... |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-mini-historic-multilingual-cased | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:1908.08962",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmBERT Mini\n\n\nThe following plot shows the pretraining loss curve for the mini model:\n\n\n!Training loss curve",
"### hmBERT Small\n\n\nThe following plot shows the pretraining loss... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmB... |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-small-historic-multilingual-cased | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:1908.08962",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmBERT Mini\n\n\nThe following plot shows the pretraining loss curve for the mini model:\n\n\n!Training loss curve",
"### hmBERT Small\n\n\nThe following plot shows the pretraining loss... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmB... |
fill-mask | transformers |
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtere... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-tiny-historic-multilingual-cased | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:1908.08962",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Historic Language Models (HLMs)
===============================
Languages
---------
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
Language: German, Training data: Europeana, Size: 13-28GB (filtered)
Language: French, Training data: Europeana, Siz... | [
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmBERT Mini\n\n\nThe following plot shows the pretraining loss curve for the mini model:\n\n\n!Training loss curve",
"### hmBERT Small\n\n\nThe following plot shows the pretraining loss... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #multilingual #arxiv-1908.08962 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### hmBERT Tiny\n\n\nThe following plot shows the pretraining loss curve for the tiny model:\n\n\n!Training loss curve",
"### hmB... |
feature-extraction | transformers |
# π€ + π dbmdz ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana ConvBERT model π
# German Europeana ConvBERT
We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The European Li... | {"language": "de", "license": "mit", "tags": ["historic german"]} | dbmdz/convbert-base-german-europeana-cased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convbert",
"feature-extraction",
"historic german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #convbert #feature-extraction #historic german #de #license-mit #endpoints_compatible #region-us
|
# + dbmdz ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana ConvBERT model
# German Europeana ConvBERT
We use the open source Europeana newspapers
that were provided by *The European Library*. The final
training corpus has a size ... | [
"# + dbmdz ConvBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a German Europeana ConvBERT model",
"# German Europeana ConvBERT\n\nWe use the open source Europeana newspapers\nthat were provided by *The European Library*. The final\ntraining corp... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convbert #feature-extraction #historic german #de #license-mit #endpoints_compatible #region-us \n",
"# + dbmdz ConvBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a German Europeana ConvBERT model"... |
feature-extraction | transformers |
# π€ + π dbmdz Turkish ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ConvBERT model for Turkish π
# πΉπ· ConvBERTurk
ConvBERTurk is a community-driven cased ConvBERT model for Turkish.
In addition to the BERT and ELECTRA based models, we... | {"language": "tr", "license": "mit"} | dbmdz/convbert-base-turkish-cased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convbert",
"feature-extraction",
"tr",
"arxiv:2008.02496",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.02496"
] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #convbert #feature-extraction #tr #arxiv-2008.02496 #license-mit #endpoints_compatible #region-us
|
# + dbmdz Turkish ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ConvBERT model for Turkish
# πΉπ· ConvBERTurk
ConvBERTurk is a community-driven cased ConvBERT model for Turkish.
In addition to the BERT and ELECTRA based models, we also ... | [
"# + dbmdz Turkish ConvBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a cased ConvBERT model for Turkish",
"# πΉπ· ConvBERTurk\n\nConvBERTurk is a community-driven cased ConvBERT model for Turkish.\n\nIn addition to the BERT and ELECTRA based mo... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convbert #feature-extraction #tr #arxiv-2008.02496 #license-mit #endpoints_compatible #region-us \n",
"# + dbmdz Turkish ConvBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a cased ConvBERT model fo... |
fill-mask | transformers |
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
W... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"]} | dbmdz/convbert-base-turkish-mc4-cased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convbert",
"fill-mask",
"tr",
"dataset:allenai/c4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #convbert #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the... | [
"# πΉπ· Turkish ConvBERT model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convbert #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# πΉπ· Turkish ConvBERT model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL... |
fill-mask | transformers |
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
W... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"]} | dbmdz/convbert-base-turkish-mc4-uncased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convbert",
"fill-mask",
"tr",
"dataset:allenai/c4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #convbert #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the... | [
"# πΉπ· Turkish ConvBERT model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convbert #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# πΉπ· Turkish ConvBERT model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\... |
null | transformers |
# π€ + π dbmdz DistilBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana DistilBERT model π
# German Europeana DistilBERT
We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The Europ... | {"language": "de", "license": "mit", "tags": ["historic german"]} | dbmdz/distilbert-base-german-europeana-cased | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"historic german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #distilbert #historic german #de #license-mit #endpoints_compatible #region-us
|
# + dbmdz DistilBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana DistilBERT model
# German Europeana DistilBERT
We use the open source Europeana newspapers
that were provided by *The European Library*. The final
training corpus has a... | [
"# + dbmdz DistilBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a German Europeana DistilBERT model",
"# German Europeana DistilBERT\n\nWe use the open source Europeana newspapers\nthat were provided by *The European Library*. The final\ntrainin... | [
"TAGS\n#transformers #pytorch #tf #distilbert #historic german #de #license-mit #endpoints_compatible #region-us \n",
"# + dbmdz DistilBERT model\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources a German Europeana DistilBERT model",
"# German Europeana Dis... |
null | transformers |
# π€ + π dbmdz Distilled Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a (cased) distilled model for Turkish π
# πΉπ· DistilBERTurk
DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
DistilBERTurk was trained on 7GB ... | {"language": "tr", "license": "mit"} | dbmdz/distilbert-base-turkish-cased | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"tr",
"arxiv:1910.01108",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"tr"
] | TAGS
#transformers #pytorch #tf #distilbert #tr #arxiv-1910.01108 #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz Distilled Turkish BERT model
====================================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a (cased) distilled model for Turkish
πΉπ· DistilBERTurk
================
DistilBERTurk is a community-driven cased distilled BERT model for ... | [] | [
"TAGS\n#transformers #pytorch #tf #distilbert #tr #arxiv-1910.01108 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | # π€ + π dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models π
# French Europeana ELECTRA
We extracted all French texts using the `language` metadata attribute from the Europeana corpus.
The resulting corpus has a ... | {"language": "fr", "license": "mit", "tags": ["historic french"]} | dbmdz/electra-base-french-europeana-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"historic french",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #historic french #fr #license-mit #endpoints_compatible #region-us
| # + dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models
# French Europeana ELECTRA
We extracted all French texts using the 'language' metadata attribute from the Europeana corpus.
The resulting corpus has a size o... | [
"# + dbmdz ELECTRA models\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana ELECTRA models",
"# French Europeana ELECTRA\n\nWe extracted all French texts using the 'language' metadata attribute from the Europeana corpus.\n\nThe resulting corp... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #historic french #fr #license-mit #endpoints_compatible #region-us \n",
"# + dbmdz ELECTRA models\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana ELECTRA models",
"# French Europeana... |
fill-mask | transformers | # π€ + π dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models π
# French Europeana ELECTRA
We extracted all French texts using the `language` metadata attribute from the Europeana corpus.
The resulting corpus has a ... | {"language": "fr", "license": "mit", "tags": ["historic french"]} | dbmdz/electra-base-french-europeana-cased-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"historic french",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #historic french #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # + dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models
# French Europeana ELECTRA
We extracted all French texts using the 'language' metadata attribute from the Europeana corpus.
The resulting corpus has a size o... | [
"# + dbmdz ELECTRA models\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana ELECTRA models",
"# French Europeana ELECTRA\n\nWe extracted all French texts using the 'language' metadata attribute from the Europeana corpus.\n\nThe resulting corp... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #historic french #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# + dbmdz ELECTRA models\n\nIn this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\nLibrary open sources French Europeana ELECT... |
null | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/electra-base-italian-xxl-cased-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"it",
"dataset:wikipedia",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #electra #pretraining #it #dataset-wikipedia #license-mit #endpoints_compatible #has_space #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #it #dataset-wikipedia #license-mit #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | {"language": "it", "license": "mit", "datasets": ["wikipedia"]} | dbmdz/electra-base-italian-xxl-cased-generator | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"fill-mask",
"it",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #electra #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #region-us
| + dbmdz BERT and ELECTRA models
===============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models
Italian BERT
============
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various te... | [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #fill-mask #it #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# π€ + π dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA base model for Turkish π
# Turkish ELECTRA model
We release a base ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*.
> ELECTRA is a ... | {"language": "tr", "license": "mit"} | dbmdz/electra-base-turkish-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #license-mit #endpoints_compatible #region-us
| + dbmdz Turkish ELECTRA model
=============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA base model for Turkish
Turkish ELECTRA model
=====================
We release a base ELECTRA model for Turkish, that was trained on the sam... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
We... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"]} | dbmdz/electra-base-turkish-mc4-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"electra",
"pretraining",
"tr",
"dataset:allenai/c4",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #tensorboard #electra #pretraining #tr #dataset-allenai/c4 #license-mit #endpoints_compatible #region-us
|
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the
... | [
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are ... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #electra #pretraining #tr #dataset-allenai/c4 #license-mit #endpoints_compatible #region-us \n",
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n... |
fill-mask | transformers |
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
We... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"], "widget": [{"text": "[MASK] s\u00f6zc\u00fc\u011f\u00fc T\u00fcrk\u00e7e k\u00f6kenlidir"}]} | dbmdz/electra-base-turkish-mc4-cased-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"tr",
"dataset:allenai/c4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the
... | [
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n... |
null | transformers |
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
We... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"]} | dbmdz/electra-base-turkish-mc4-uncased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"dataset:allenai/c4",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #dataset-allenai/c4 #license-mit #endpoints_compatible #region-us
|
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the
... | [
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are ... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #dataset-allenai/c4 #license-mit #endpoints_compatible #region-us \n",
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present ... |
fill-mask | transformers |
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
We... | {"language": "tr", "license": "mit", "datasets": ["allenai/c4"]} | dbmdz/electra-base-turkish-mc4-uncased-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"tr",
"dataset:allenai/c4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="URL
</p>
![DOI](URL
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish
Some datasets used for pretraining and evaluation are contributed from the
... | [
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI](URL\n\nWe present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish \n\nSome datasets used for pretraining and evaluation are ... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #tr #dataset-allenai/c4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# πΉπ· Turkish ELECTRA model\n\n<p align=\"center\">\n <img alt=\"Logo provided by Merve Noyan\" title=\"Awesome logo from Merve Noyan\" src=\"URL\n</p>\n\n![DOI... |
null | transformers |
# π€ + π dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA small model for Turkish π
# Turkish ELECTRA model
We release a small ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*.
> ELECTRA is ... | {"language": "tr", "license": "mit"} | dbmdz/electra-small-turkish-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #license-mit #endpoints_compatible #region-us
| + dbmdz Turkish ELECTRA model
=============================
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA small model for Turkish
Turkish ELECTRA model
=====================
We release a small ELECTRA model for Turkish, that was trained on the s... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #license-mit #endpoints_compatible #region-us \n"
] |
token-classification | flair |
# Triple E - Effective Ensembling of Embeddings and Language Models for NER of Historical German
Based on [our paper](http://ceur-ws.org/Vol-2696/paper_173.pdf) we release a new baseline model for the German
[CLEF-HIPE shared task](https://impresso.github.io/CLEF-HIPE-2020/).
In contrast to the models used in the pa... | {"language": "de", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Herr Oberst Brunner ist n\u00e4mlich Hauptagent f\u00fcr den Kanton Z\u00fcrich."}]} | dbmdz/flair-clef-hipe-german-base | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"arxiv:2011.06993",
"arxiv:2010.10392",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993",
"2010.10392"
] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #arxiv-2011.06993 #arxiv-2010.10392 #license-mit #region-us
| Triple E - Effective Ensembling of Embeddings and Language Models for NER of Historical German
==============================================================================================
Based on our paper we release a new baseline model for the German
CLEF-HIPE shared task.
In contrast to the models used in the... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #arxiv-2011.06993 #arxiv-2010.10392 #license-mit #region-us \n"
] |
token-classification | flair |
# Flair NER model trained on GermEval14 dataset
This model was trained on the official [GermEval14](https://sites.google.com/site/germeval2014ner/data)
dataset using the [Flair](https://github.com/flairNLP/flair) framework.
It uses a fine-tuned German DistilBERT model from [here](https://huggingface.co/distilbert-ba... | {"language": "de", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["germeval_14"], "widget": [{"text": "Hugging Face ist eine franz\u00f6sische Firma mit Sitz in New York."}]} | stefan-it/flair-distilbert-ner-germeval14 | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:germeval_14",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-germeval_14 #license-mit #region-us
| Flair NER model trained on GermEval14 dataset
=============================================
This model was trained on the official GermEval14
dataset using the Flair framework.
It uses a fine-tuned German DistilBERT model from here.
Results
=======
β denotes that this model is selected for upload.
Flair Fine... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-germeval_14 #license-mit #region-us \n"
] |
token-classification | flair |
# Towards Robust Named Entity Recognition for Historic German
Based on [our paper](https://www.aclweb.org/anthology/W19-4312/)
we release a new model trained on the LFT dataset.
**Note:** We use BPEmbeddings instead of the combination of
Wikipedia, Common Crawl and character embeddings (as used in the paper),
so sav... | {"language": "de", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "inference": false} | dbmdz/flair-historic-ner-lft | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us
| Towards Robust Named Entity Recognition for Historic German
===========================================================
Based on our paper
we release a new model trained on the LFT dataset.
Note: We use BPEmbeddings instead of the combination of
Wikipedia, Common Crawl and character embeddings (as used in the paper... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us \n"
] |
token-classification | flair |
# Towards Robust Named Entity Recognition for Historic German
Based on [our paper](https://www.aclweb.org/anthology/W19-4312/)
we release a new model trained on the ONB dataset.
**Note:** We use BPEmbeddings instead of the combination of
Wikipedia, Common Crawl and character embeddings (as used in the paper),
so sav... | {"language": "de", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "April Martin Ansclm, K. Gefangen-Auffehers Georg Sausgruber."}]} | dbmdz/flair-historic-ner-onb | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us
| Towards Robust Named Entity Recognition for Historic German
===========================================================
Based on our paper
we release a new model trained on the ONB dataset.
Note: We use BPEmbeddings instead of the combination of
Wikipedia, Common Crawl and character embeddings (as used in the paper... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us \n"
] |
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