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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
This model is an implementation of the paper [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC).
We implement the version with the T5-small with the reported F_0... | {"language": ["en"], "license": "apache-2.0", "tags": ["grammatical error correction", "text2text", "t5"], "datasets": ["clang-8", "conll-14", "conll-13"], "metrics": ["f0.5"]} | Unbabel/gec-t5_small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"grammatical error correction",
"text2text",
"en",
"dataset:clang-8",
"dataset:conll-14",
"dataset:conll-13",
"arxiv:2106.03830",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation... | null | 2022-03-02T23:29:05+00:00 | [
"2106.03830"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #grammatical error correction #text2text #en #dataset-clang-8 #dataset-conll-14 #dataset-conll-13 #arxiv-2106.03830 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model is an implementation of the paper A Simple Recipe for Multilingual Grammatical Error Correction from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC).
We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70).
To effec... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #grammatical error correction #text2text #en #dataset-clang-8 #dataset-conll-14 #dataset-conll-13 #arxiv-2106.03830 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # Model
mMiniLM-L12xH384 XLM-R model proposed in [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://arxiv.org/abs/2012.15828) that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 202... | {} | Unbabel/xlm-roberta-comet-small | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2012.15828",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15828"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2012.15828 #endpoints_compatible #region-us
| # Model
mMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.
This model is much more light we... | [
"# Model\n\nmMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.\n\nThis model is much more... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2012.15828 #endpoints_compatible #region-us \n",
"# Model\n\nmMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessmen... |
text-generation | transformers | # Mourinhio | {"tags": ["conversational"]} | Username1/Mourinhio-medium | 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
| # Mourinhio | [
"# Mourinhio"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mourinhio"
] |
text-generation | transformers | # Mourinhio | {"tags": ["conversational"]} | Username1/Mourinho | 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
| # Mourinhio | [
"# Mourinhio"
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"# Mourinhio"
] |
text-generation | transformers | # Wenger | {"tags": ["conversational"]} | Username1/Wenger | 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
| # Wenger | [
"# Wenger"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wenger"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | V3RX2000/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8107
* Matthews Correlation: 0.5396
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | V3RX2000/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0612
* Precision: 0.9272
* Recall: 0.9376
* F1: 0.9324
* Accuracy: 0.9842
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | V3RX2000/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1580
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-generation | transformers |
# GGODMODEL | {"tags": ["conversational"]} | VLRevolution/DialogGPT-small-GGODMODEL | 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
|
# GGODMODEL | [
"# GGODMODEL"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GGODMODEL"
] |
text-generation | transformers |
# Dumb bot | {"tags": ["conversational"]} | VMET/DialoGPT-small-dumbassbot | 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
|
# Dumb bot | [
"# Dumb bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dumb bot"
] |
text-generation | transformers |
#Rick Sanchez DiaploGPT Model | {"tags": ["conversational"]} | VaguelyCynical/DialoGPT-small-RickSanchez | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Sanchez DiaploGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # 中文预训练Longformer模型 | Longformer_ZH with PyTorch
相比于Transformer的O(n^2)复杂度，Longformer提供了一种以线性复杂度处理最长4K字符级别文档序列的方法。Longformer Attention包括了标准的自注意力与全局注意力机制，方便模型更好地学习超长序列的信息。
Compared with O(n^2) complexity for Transformer model, Longformer provides an efficient method for processing long-document level sequence in Linea... | {} | ValkyriaLenneth/longformer_zh | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #endpoints_compatible #region-us
| 中文预训练Longformer模型 | Longformer\_ZH with PyTorch
===============================================
相比于Transformer的O(n^2)复杂度，Longformer提供了一种以线性复杂度处理最长4K字符级别文档序列的方法。Longformer Attention包括了标准的自注意力与全局注意力机制，方便模型更好地学习超长序列的信息。
Compared with O(n^2) complexity for Transformer model, Longformer provides an efficient method for ... | [
"### CCF Sentiment Analysis\n\n\n* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试\n* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.",
"### Pretraining BPC\n\n\n* 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.\n* We also pro... | [
"TAGS\n#transformers #pytorch #longformer #feature-extraction #endpoints_compatible #region-us \n",
"### CCF Sentiment Analysis\n\n\n* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试\n* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.",
... |
text-generation | transformers |
# Dante (DMC V) DialogGPT Model | {"tags": ["conversational"]} | Vampiro/DialoGPT-small-dante_b | 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
|
# Dante (DMC V) DialogGPT Model | [
"# Dante (DMC V) DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dante (DMC V) DialogGPT Model"
] |
text-generation | transformers |
# Dante - Devi May Cry V DialoGPT Model | {"tags": ["conversational"]} | Vampiro/DialoGPT-small-dante_c | 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
|
# Dante - Devi May Cry V DialoGPT Model | [
"# Dante - Devi May Cry V DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dante - Devi May Cry V DialoGPT Model"
] |
text-generation | transformers |
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How to use
## Requires sentencepiece: # !pip install sentencepiece
PyTorch and TF models available
```python
from tr... | {"language": "en", "tags": ["paraphrase-generation", "text-generation", "Conditional Generation"], "inference": false} | Vamsi/T5_Paraphrase_Paws | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"en",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #has_space #text-generation-inference #region-us
|
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.
## How to use
## Requires sentencepiece: # !pip install sentencepiece
PyTorch and TF models available
For more reference on training your own T5 model or using this mode... | [
"# Paraphrase-Generation\n",
"## Model description\n\nT5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.\n",
"## How to use\n## Requires sentencepiece: # !pip install sentencepiece\nPyTorch and TF models available\n\n\n\nFor more reference on training your own T5 ... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# Paraphrase-Generation\n",
"## Model description\n\nT5 Model for generating paraphrase... |
question-answering | transformers | "hello"
| {} | Vasanth/bert-base-uncased-qa-squad2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us
| "hello"
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transform... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-... | [
"# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and ca... |
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. -->
# tamil-sentiment-distilbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tamilmixsentiment"], "metrics": ["accuracy"], "model_index": [{"name": "tamil-sentiment-distilbert", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "tamilmixsentiment", "type": "tamilmix... | Vasanth/tamil-sentiment-distilbert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tamilmixsentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tamil-sentiment-distilbert
==========================
This model is a fine-tuned version of distilbert-base-cased on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0230
* Accuracy: 0.665
Dataset Information
-------------------
* text: Tamil-English code-mixed c... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | Vassilis/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1628
* Accuracy: 0.9345
* F1: 0.9348
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | Verge/Peterbot | 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
|
# Peter from Your Boyfriend Game.
| [
"# Peter from Your Boyfriend Game."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter from Your Boyfriend Game."
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Vibharkchauhan/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0626
* Precision: 0.9193
* Recall: 0.9311
* F1: 0.9251
* Accuracy: 0.9824
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
# RoBERTa-base-finetuned-yelp-polarity
This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the test set.
## Hyper-parameters
We used the fo... | {"language": "en", "datasets": ["yelp_polarity"]} | VictorSanh/roberta-base-finetuned-yelp-polarity | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:yelp_polarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-base-finetuned-yelp-polarity
This is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.
It gets 98.08% accuracy on the test set.
## Hyper-parameters
We used the following hyper-parameters to train the model on one GPU:
| [
"# RoBERTa-base-finetuned-yelp-polarity\n\nThis is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.\nIt gets 98.08% accuracy on the test set.",
"## Hyper-parameters\n\nWe used the following hyper-parameters to train the model on one GPU:"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-base-finetuned-yelp-polarity\n\nThis is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.\nIt get... |
text-generation | transformers |
# GPT-J 6B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@gmail.com (Dương) / imthanhlv@gmail.com (Thành) / nguyenvulebinh@gmail.com (Bình).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_... | {"language": ["vi"], "tags": ["pytorch", "causal-lm", "text-generation"]} | VietAI/gpt-j-6B-vietnamese-news | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #vi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT-J 6B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).
### How to use
| [
"# GPT-J 6B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-J 6B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@... |
text-generation | transformers |
# GPT-Neo 1.3B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@gmail.com (Dương) / imthanhlv@gmail.com (Thành) / nguyenvulebinh@gmail.com (Bình).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.... | {"language": ["vi"], "tags": ["pytorch", "causal-lm", "gpt"]} | VietAI/gpt-neo-1.3B-vietnamese-news | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"gpt",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT-Neo 1.3B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).
### How to use
| [
"# GPT-Neo 1.3B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-Neo 1.3B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / ngu... |
null | transformers |
# Norwegian Electra
Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)
Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
# Model
## Electr... | {"language": false, "thumbnail": "https://i.imgur.com/QqSEC5I.png"} | ViktorAlm/electra-base-norwegian-uncased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"no",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #no #endpoints_compatible #region-us
|
# Norwegian Electra
!Image of norwegian electra
Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)
Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
# Model
## Electra: Pre-training Text Encoders as Di... | [
"# Norwegian Electra\n!Image of norwegian electra\n\nTrained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)\n\nUse with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.",
"# Model",
"## Electra: Pre-training ... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #no #endpoints_compatible #region-us \n",
"# Norwegian Electra\n!Image of norwegian electra\n\nTrained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)\n\nUse with caution. I have no downstream tasks in Norweg... |
fill-mask | transformers | # Albumin-15s
## Model description
This is a version of [Albert-base-v2](https://huggingface.co/albert-base-v2) for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.
The Albert model was pretrained in the English language, it has many similarities with language or protein... | {} | Vilnius-Lithuania-iGEM/Albumin | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Albumin-15s
## Model description
This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.
The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fin... | [
"# Albumin-15s",
"## Model description\n\nThis is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.\n\nThe Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Albumin-15s",
"## Model description\n\nThis is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.\n\nThe Albert model was pret... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | VincentButterfield/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 | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
null | pytorch |
Ce modèle est développé pour KARA.
Ce modèle est :
- Un outil d'analyse de sentiment associé à un commentaire de sondage RH
- Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits)
- Spécialisé pour des commentaires entre 10 et 512 charactères
Ce modèle n'est pas :
- Utilisable po... | {"language": ["en"], "library_name": "pytorch", "tags": ["sentiment-analysis"], "metrics": ["negative", "positive"], "widget": [{"text": "Thank you for listening to the recommendations of the telephone team for teleworking. we have a strong expertise in this field and accurate listening to Our management!!!!", "example... | VincentC12/sentiment_analysis_kara | null | [
"pytorch",
"distilbert",
"sentiment-analysis",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #distilbert #sentiment-analysis #en #region-us
|
Ce modèle est développé pour KARA.
Ce modèle est :
- Un outil d'analyse de sentiment associé à un commentaire de sondage RH
- Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits)
- Spécialisé pour des commentaires entre 10 et 512 charactères
Ce modèle n'est pas :
- Utilisable po... | [] | [
"TAGS\n#pytorch #distilbert #sentiment-analysis #en #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | VirenS13117/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7809
* Matthews Correlation: 0.5286
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | VishalArun/DialoGPT-medium-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 | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
image-classification | null |
# VAN-Base
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed fo... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | Visual-Attention-Network/VAN-Base-original | null | [
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.09741"
] | [] | TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
| VAN-Base
========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mecha... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
image-classification | null |
# VAN-Large
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally de... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | Visual-Attention-Network/VAN-Large-original | null | [
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.09741"
] | [] | TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
| VAN-Large
=========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mec... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
image-classification | null |
# VAN-Small
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally de... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | Visual-Attention-Network/VAN-Small-original | null | [
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.09741"
] | [] | TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
| VAN-Small
=========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mec... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
image-classification | null |
# VAN-Tiny
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed fo... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | Visual-Attention-Network/VAN-Tiny-original | null | [
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.09741"
] | [] | TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
| VAN-Tiny
========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mecha... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
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] |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | Vitafeu/DialoGPT-medium-ricksanchez | null | [
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"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
|
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null | null | This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also. | {} | Vivek/flax-gpt2-common-sense-reasoning | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also. | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also. | {} | Vivek/gpt2-common-sense-reasoning | null | [
"transformers",
"jax",
"tensorboard",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us
| This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also. | [] | [
"TAGS\n#transformers #jax #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | transformers |
#### Table of contents
1. [Introduction](#introduction)
2. [Pretrain model](#models)
3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
- [Installation](#install1)
- [Example usage](#usage1)
4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
- [Installation](#install2... | {"language": ["vi"], "pipeline_tag": "sentence-similarity"} | VoVanPhuc/sup-SimCSE-VietNamese-phobert-base | null | [
"transformers",
"pytorch",
"roberta",
"sentence-similarity",
"vi",
"arxiv:2104.08821",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [
"vi"
] | TAGS
#transformers #pytorch #roberta #sentence-similarity #vi #arxiv-2104.08821 #endpoints_compatible #has_space #region-us
| #### Table of contents
1. Introduction
2. Pretrain model
3. Using SimeCSE\_Vietnamese with 'sentences-transformers'
* Installation
* Example usage
4. Using SimeCSE\_Vietnamese with 'transformers'
* Installation
* Example usage
SimeCSE\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnam... | [
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentenc... | [
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null | transformers |
#### Table of contents
1. [Introduction](#introduction)
2. [Pretrain model](#models)
3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
- [Installation](#install1)
- [Example usage](#usage1)
4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
- [Installation](#install2... | {} | VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base | null | [
"transformers",
"pytorch",
"roberta",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-2104.08821 #endpoints_compatible #region-us
| #### Table of contents
1. Introduction
2. Pretrain model
3. Using SimeCSE\_Vietnamese with 'sentences-transformers'
* Installation
* Example usage
4. Using SimeCSE\_Vietnamese with 'transformers'
* Installation
* Example usage
SimeCSE\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnam... | [
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentenc... | [
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text-generation | transformers | #Cortana DialoGPT Model | {"tags": ["conversational"]} | VulcanBin/DialoGPT-small-cortana | 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
| #Cortana DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | # Deberta-Chinese
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
| 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 |
| --------------------- | ------... | {} | WENGSYX/Deberta-Chinese-Large | null | [
"transformers",
"pytorch",
"deberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #endpoints_compatible #region-us
| Deberta-Chinese
===============
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
### 加载与使用
依托于huggingface-transformers
#### 注意，请使用BertTokenizer加载中文词表
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] |
feature-extraction | transformers | # Multilingual SimCSE
#### A contrastive learning model using parallel language pair training
##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse
##### Firstly, the [mDeBERTa](https://huggingface.co/microsoft/mdeberta-v3-... | {} | WENGSYX/Multilingual_SimCSE | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #endpoints_compatible #region-us
| # Multilingual SimCSE
#### A contrastive learning model using parallel language pair training
##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse
##### Firstly, the mDeBERTa model is used to load the pre-training paramete... | [
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"##### By using parallel sentence pairs in different languages, the text is mapped to the same ve... |
automatic-speech-recognition | transformers | "Hello"
| {} | WSS/wav2vec2-large-xlsr-53-vietnamese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| "Hello"
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/zejunwang1/bert4vec | {} | WangZeJun/roformer-sim-base-chinese | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/zejunwang1/bert4vec | {} | WangZeJun/roformer-sim-small-chinese | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/zejunwang1/bert4vec | {} | WangZeJun/simbert-base-chinese | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #has_space #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | WarrenK-Design/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
|
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] |
null | null | Testing a new model | {} | WayScriptDerrick/SampleModel | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Testing a new model | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used towards research grants, it should be applicable to any type of ... | {"license": "apache-2.0", "pipeline_tag": "text-classification"} | Wellcome/WellcomeBertMesh | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-classification",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #text-classification #custom_code #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was tra... | [
"# WellcomeBertMesh\n\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it w... | [
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"# WellcomeBertMesh\n\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Me... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "... | Wende/bert-finetuned-ner1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner1
===================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0584
* Precision: 0.9286
* Recall: 0.9475
* F1: 0.9379
* Accuracy: 0.9859
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# Harry Potter DaibloGPT Model | {"tags": ["conversational"]} | Wessel/DiabloGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
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] |
text-generation | transformers |
# White's Bot | {"tags": ["conversational"]} | White/white-bot | 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
|
# White's Bot | [
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"# White's Bot"
] |
text-generation | transformers |
# Twety DialoGPT Model | {"tags": ["conversational"]} | Whitez/DialoGPT-small-twety | 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
|
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] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-arabic-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-arabic-demo-colab", "results": []}]} | Wiam/wav2vec2-large-xlsr-arabic-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-arabic-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# wav2vec2-large-xlsr-arabic-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
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feature-extraction | transformers |
# IndoConvBERT Base Model
IndoConvBERT is a ConvBERT model pretrained on Indo4B.
## Pretraining details
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequen... | {"language": "id", "inference": false} | Wikidepia/IndoConvBERT-base | null | [
"transformers",
"pytorch",
"tf",
"convbert",
"feature-extraction",
"id",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #convbert #feature-extraction #id #region-us
|
# IndoConvBERT Base Model
IndoConvBERT is a ConvBERT model pretrained on Indo4B.
## Pretraining details
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequen... | [
"# IndoConvBERT Base Model\n\nIndoConvBERT is a ConvBERT model pretrained on Indo4B.",
"## Pretraining details\n\nWe follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model wit... | [
"TAGS\n#transformers #pytorch #tf #convbert #feature-extraction #id #region-us \n",
"# IndoConvBERT Base Model\n\nIndoConvBERT is a ConvBERT model pretrained on Indo4B.",
"## Pretraining details\n\nWe follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90%... |
text2text-generation | transformers | # Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretrained("Wikidepia/IndoT5-... | {"language": ["id"]} | Wikidepia/IndoT5-base-paraphrase | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
## Limitations
Sometimes paraphrase contain date which doesnt exists in the original text :/
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s. | [
"# Paraphrase Generation with IndoT5 Base\n\nIndoT5-base trained on translated PAWS.",
"## Model in action",
"## Limitations\n\nSometimes paraphrase contain date which doesnt exists in the original text :/",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] | [
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"# Paraphrase Generation with IndoT5 Base\n\nIndoT5-base trained on translated PAWS.",
"## Model in action",
"## Limitations\n\nSometi... |
text2text-generation | transformers | # Indonesian T5 Base
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M... | {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Indonesian T5 Base
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following 'google/t5-v1_1-base'.
## Model Performance
TBD
## Li... | [
"# Indonesian T5 Base\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-base'.",
"## Model Perfo... | [
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"# Indonesian T5 Base\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-traine... |
text2text-generation | transformers |
**NOTE** : This model might be broken :/
# Indonesian T5 Large
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tas... | {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
NOTE : This model might be broken :/
# Indonesian T5 Large
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 500K steps following 'google/t5-v1_1... | [
"# Indonesian T5 Large\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 500K steps following 'google/t5-v1_1-large'.",
"## Model Per... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Large\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained... |
text2text-generation | transformers | # Indonesian T5 Small
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1... | {"language": ["id"], "datasets": ["allenai/c4"]} | Wikidepia/IndoT5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Indonesian T5 Small
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following 'google/t5-v1_1-small'.
## Model Performance
TBD
## ... | [
"# Indonesian T5 Small\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-small'.",
"## Model Per... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Small\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-train... |
token-classification | flair |
# SponsorBlock Auto Segment | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"]} | Wikidepia/SB-AutoSegment | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #region-us
|
# SponsorBlock Auto Segment | [
"# SponsorBlock Auto Segment"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #region-us \n",
"# SponsorBlock Auto Segment"
] |
question-answering | transformers |
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/albert-bahasa-uncased-squad'
)
nlp = pipeline('q... | {"language": "id", "inference": false} | Wikidepia/albert-bahasa-uncased-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #albert #question-answering #id #region-us
|
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
| [
"# SQuAD IndoBERT-Lite Base Model\n\nFine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.",
"## How to use",
"### Using pipeline"
] | [
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"# SQuAD IndoBERT-Lite Base Model\n\nFine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.",
"## How to use",
"### Using pipeline"
] |
question-answering | transformers |
# IndoBERT-Lite base fine-tuned on Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answering/SQuAD) f... | {"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eid... | Wikidepia/indobert-lite-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us
|
# IndoBERT-Lite base fine-tuned on Translated SQuAD v2
IndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.
## Model in action
Fast usage with pipelines:
# Output:
README copied from mrm8488's repository
| [
"# IndoBERT-Lite base fine-tuned on Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] | [
"TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n",
"# IndoBERT-Lite base fine-tuned on Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipeline... |
question-answering | transformers |
# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answeri... | {"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eid... | Wikidepia/indobert-lite-squadx | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us
|
# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2
IndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.
## Model in action
Fast usage with pipelines:
# Output:
README copied from mrm8488's repository | [
"# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] | [
"TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n",
"# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage wi... |
text2text-generation | transformers | # NMT Model for English-Indonesian
| {} | Wikidepia/marian-nmt-enid | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # NMT Model for English-Indonesian
| [
"# NMT Model for English-Indonesian"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# NMT Model for English-Indonesian"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of `facebook/wav2vec2-xls-r-300m` on the `mozilla-foundation/common_voice_8_0` and [MagicHub Indonesian Conversational Speech Corpus](https://magichub.com/datasets/indonesian-conversational-speech-corpus/).
| {"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "id", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R-300M - Indonesian", "results":... | Wikidepia/wav2vec2-xls-r-300m-indonesian | null | [
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"automatic-speech-recognition",
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"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #id #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus.
| [
"# Wav2Vec2 XLS-R-300M - Indonesian\n\nThis model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #id #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2 XLS-R-300M - Indonesian\n\nThis mo... |
image-classification | transformers |
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model req... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_ti... | WinKawaks/vit-small-patch16-224 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.
Note that [safetensors] model requires torch 2.0 environment. | [] | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model req... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_ti... | WinKawaks/vit-tiny-patch16-224 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.
Note that [safetensors] model requires torch 2.0 environment. | [] | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# JC DialogGPT Model | {"tags": ["conversational"]} | Wise/DialogGPT-small-JC | 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
|
# JC DialogGPT Model | [
"# JC DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JC DialogGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Worldman/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2162
* Accuracy: 0.9225
* F1: 0.9227
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers | # waaaa | {"tags": ["conversational"]} | WoutN2001/james3 | 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
| # waaaa | [
"# waaaa"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# waaaa"
] |
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. -->
# albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-rea... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-fakenews-discriminator", "results": []}]} | XSY/albert-base-v2-fakenews-discriminator | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-fakenews-discriminator
=====================================
The dataset: Fake and real news dataset URL
I use title and label to train the classifier
label\_0 : Fake news
label\_1 : Real news
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following re... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-imdb-calssification
label_0: negative
label_1: positive
This model is a fine-tuned version of [albert-base-v2](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-imdb-calssification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text... | XSY/albert-base-v2-imdb-calssification | null | [
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"license:apache-2.0",
"model-index",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| albert-base-v2-imdb-calssification
==================================
label\_0: negative
label\_1: positive
This model is a fine-tuned version of albert-base-v2 on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1983
* Accuracy: 0.9361
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n*... |
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. -->
# albert-base-v2-scarcasm-discriminator
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-scarcasm-discriminator", "results": []}]} | XSY/albert-base-v2-scarcasm-discriminator | null | [
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"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-scarcasm-discriminator
=====================================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2379
* Accuracy: 0.8996
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-scarcasm-discriminator
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github https://g... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-scarcasm-discriminator", "results": []}]} | XSY/roberta-scarcasm-discriminator | null | [
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"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-scarcasm-discriminator
==============================
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github URL
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1844
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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text2text-generation | transformers | 这个模型是根据这个一步一步完成的，如果想自己微调，请参考https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/master/exampl... | {} | XSY/t5-small-finetuned-xsum | null | [
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"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 这个模型是根据这个一步一步完成的，如果想自己微调，请参考https://URL
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to URL
---
license: apache-2.0
tags:
* generated\_from\_trainer
datasets:
* xsum
metrics:
* rouge
model-index:
* name: t5-small-finetuned-xsum
results:
+ task:
name: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.954969... | {"language": "unk", "tags": "autonlp", "datasets": ["XYHY/autonlp-data-123"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 69.86520391863117} | XYHY/autonlp-123-478412765 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-XYHY/autonlp-data-123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.954969... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 478412765\n- CO2 Emissions (in grams): 69.86520391863117",
"## Validation Metrics\n\n- Loss: 0.186362624168396\n- Accuracy: 0.9539955699437723\n- Precision: 0.9527454242928453\n- Recall: 0.9572049481778669\n- AUC: 0.9903929997079... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 478412765\n- CO2 Emissions (in grams): 69.8652... |
text-generation | transformers | # Ultron Small | {"tags": ["conversational"]} | Xeouz/Ultron-Small | 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
| # Ultron Small | [
"# Ultron Small"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
null | null | A VQGAN-compatible model trained on screenshots of cityscapes from 90s anime. To use, direct vqgan to the model as you would vqgan_imagenet_f16_1024, faceshq, etc. | {} | Xibanya/AestheticCities | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| A VQGAN-compatible model trained on screenshots of cityscapes from 90s anime. To use, direct vqgan to the model as you would vqgan_imagenet_f16_1024, faceshq, etc. | [] | [
"TAGS\n#region-us \n"
] |
text-to-image | null | # Sunset Cities
This is the [Malevich](https://huggingface.co/sberbank-ai/rudalle-Malevich) ruDALL-E model finetuned on anime screenshots of big cities at sunset.
<img style="text-align:center; display:block;" src="https://huggingface.co/Xibanya/sunset_city/resolve/main/citysunset.png" width="256">
### installatio... | {"language": ["ru", "en"], "license": "cc-by-sa-4.0", "tags": ["PyTorch", "Transformers"], "pipeline_tag": "text-to-image"} | Xibanya/sunset_city | null | [
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-sa-4.0 #region-us
| # Sunset Cities
This is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.
<img style="text-align:center; display:block;" src="URL width="256">
### installation
### How to use
Basic implementation to get a list of image data objects.
the Malevich model only recognizes ... | [
"# Sunset Cities\r\nThis is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.\r\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"256\">",
"### installation",
"### How to use\r\nBasic implementation to get a list of image data objects.\r\n\r\n\r\n\r\nthe Malev... | [
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"# Sunset Cities\r\nThis is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.\r\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"256\">",
"### installation",
"### How... |
text-generation | transformers |
# Harry | {"tags": ["conversational"]} | XuguangAi/DialoGPT-small-Harry | 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 | [
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"# Harry"
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text-generation | transformers |
# Leslie | {"tags": ["conversational"]} | XuguangAi/DialoGPT-small-Leslie | 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
|
# Leslie | [
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"# Leslie"
] |
text-generation | transformers |
# Rick | {"tags": ["conversational"]} | XuguangAi/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 | [
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text-classification | transformers |
# Toxic language detection
## Model description
A toxic language detection model trained on tweets. The base model is Roberta-large. For more information,
including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
Github [repo](https://github.com... | {"language": [], "tags": [], "datasets": [], "metrics": []} | Xuhui/ToxDect-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2102.00086",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.00086"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2102.00086 #autotrain_compatible #endpoints_compatible #region-us
|
# Toxic language detection
## Model description
A toxic language detection model trained on tweets. The base model is Roberta-large. For more information,
including the training data, limitations and bias, please refer to the paper and
Github repo for more details.
#### How to use
Note that LABEL_1 means toxic and... | [
"# Toxic language detection",
"## Model description\n\nA toxic language detection model trained on tweets. The base model is Roberta-large. For more information, \nincluding the training data, limitations and bias, please refer to the paper and\nGithub repo for more details.",
"#### How to use\nNote that LABEL_... | [
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"## Model description\n\nA toxic language detection model trained on tweets. The base model is Roberta-large. For more information, \nincluding ... |
text-generation | transformers | # 经典昆曲欣赏 期末作业
## KunquChat
Author: 1900012921 俞跃江
| {} | YYJ/KunquChat | 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
| # 经典昆曲欣赏 期末作业
## KunquChat
Author: 1900012921 俞跃江
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"# 经典昆曲欣赏 期末作业",
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"# 经典昆曲欣赏 期末作业",
"## KunquChat\nAuthor: 1900012921 俞跃江"
] |
text-classification | transformers |
# Model description
This model is an Arabic language sentiment analysis pretrained model.
The model is built on top of the CAMelBERT_msa_sixteenth BERT-based model.
We used the HARD dataset of hotels review to fine tune the model.
The dataset original labels based on a five-star rating were modified to a 3 label data... | {"language": "ar", "widget": [{"text": "\u0645\u0645\u062a\u0627\u0632"}, {"text": "\u0623\u0646\u0627 \u062d\u0632\u064a\u0646"}, {"text": "\u0644\u0627 \u0634\u064a\u0621"}]} | Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD | null | [
"transformers",
"tf",
"bert",
"text-classification",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #tf #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model description
This model is an Arabic language sentiment analysis pretrained model.
The model is built on top of the CAMelBERT_msa_sixteenth BERT-based model.
We used the HARD dataset of hotels review to fine tune the model.
The dataset original labels based on a five-star rating were modified to a 3 label data... | [
"# Model description\n\nThis model is an Arabic language sentiment analysis pretrained model.\nThe model is built on top of the CAMelBERT_msa_sixteenth BERT-based model.\nWe used the HARD dataset of hotels review to fine tune the model.\nThe dataset original labels based on a five-star rating were modified to a 3 l... | [
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"# Model description\n\nThis model is an Arabic language sentiment analysis pretrained model.\nThe model is built on top of the CAMelBERT_msa_sixteenth BERT-based model.\nWe used the HARD... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Yaia/distilbert-base-uncased-finetuned-emotion | null | [
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"dataset:emotion",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2086
* Accuracy: 0.9255
* F1: 0.9257
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
null | null | ONNX version of message-intent model. Will be used on GPU machine. | {} | Yanjie/message-intent-onnx | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #region-us
| ONNX version of message-intent model. Will be used on GPU machine. | [] | [
"TAGS\n#onnx #region-us \n"
] |
text-classification | transformers | This is the concierge intent model. Fined tuned on DistilBert uncased model. | {} | Yanjie/message-intent | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This is the concierge intent model. Fined tuned on DistilBert uncased model. | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is the concierge preamble model. Fined tuned on DistilBert uncased model. | {} | Yanjie/message-preamble | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This is the concierge preamble model. Fined tuned on DistilBert uncased model. | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#test | {"tags": ["conversational"]} | Yankee/test1234 | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
#test | [] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
Domain-adaptive pretraining of camembert-base using 15 GB of French Tweets | {"language": "fr"} | Yanzhu/bertweetfr-base | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fill-mask #fr #autotrain_compatible #endpoints_compatible #region-us
|
Domain-adaptive pretraining of camembert-base using 15 GB of French Tweets | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | French NER model for tweets. Fine-tuned on the CAP2017 dataset.
label_list = ['O',
'B-person',
'I-person',
'B-musicartist',
'I-musicartist',
'B-org',
'I-org',
'B-geoloc',
'I-geoloc',... | {} | Yanzhu/bertweetfr_ner | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| French NER model for tweets. Fine-tuned on the CAP2017 dataset.
label_list = ['O',
'B-person',
'I-person',
'B-musicartist',
'I-musicartist',
'B-org',
'I-org',
'B-geoloc',
'I-geoloc',... | [] | [
"TAGS\n#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | French roBERTa-base model fine-tuned for Offensive Language Identification on COVID-19 tweets. | {} | Yanzhu/bertweetfr_offensiveness | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| French roBERTa-base model fine-tuned for Offensive Language Identification on COVID-19 tweets. | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | null | # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training.
Whe... | {"language": "Bengali", "license": "cc-by-sa-4.0", "tags": ["bn", "audio", "automatic-speech-recognition", "speech"], "datasets": ["OpenSLR"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Bengali by Arijit", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dat... | YasinShihab/asr-en-bn-test | null | [
"bn",
"audio",
"automatic-speech-recognition",
"speech",
"dataset:OpenSLR",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Bengali"
] | TAGS
#bn #audio #automatic-speech-recognition #speech #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #region-us
| # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances from Bengali ASR training data set containing ~196K utterances. Tested WER using ~4200 held out from training.
When using this model, make sure that your speech input is sampled at 16kHz.
Train Script ca... | [
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances from Bengali ASR training data set containing ~196K utterances. Tested WER using ~4200 held out from training.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\nTrain S... | [
"TAGS\n#bn #audio #automatic-speech-recognition #speech #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #region-us \n",
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances from Bengali ASR training data set containing ~196K utterances. Tested ... |
automatic-speech-recognition | transformers |
# Ukrainian STT model (with Language Model)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2ve... | {"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "uk"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-uk-with-lm", "resul... | Yehor/wav2vec2-xls-r-1b-uk-with-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"uk",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Ukrainian STT model (with Language Model)
=========================================
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
... |
automatic-speech-recognition | transformers |
# Ukrainian STT model (with the Big Language Model formed on News Dataset)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https:/... | {"language": ["uk"], "license": "cc-by-nc-sa-4.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "uk"], "xdatasets": ["mozilla-foundation/common_voice_7_0"]} | Yehor/wav2vec2-xls-r-1b-uk-with-news-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"uk",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| Ukrainian STT model (with the Big Language Model formed on News Dataset)
========================================================================
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model is a fine-tuned version of faceboo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
automatic-speech-recognition | transformers |
# Ukrainian STT model (with Language Model)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
- Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-l... | {"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "uk"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-uk-with-lm", "results": [{"task": {"type": "automatic-speech-r... | Yehor/wav2vec2-xls-r-300m-uk-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"uk",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Ukrainian STT model (with Language Model)
=========================================
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
* Have a look on an updated 300m model: URL
* Have a look on a better model with more parameters: URL
Thi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following h... |
null | null | # ProteinLM | {} | Yijia-Xiao/ProteinLM | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # ProteinLM | [
"# ProteinLM"
] | [
"TAGS\n#region-us \n",
"# ProteinLM"
] |
question-answering | transformers |
# Question Answering model for Hindi and Tamil
This model is part of the ensemble that ranked 4/943 in the [Hindi and Tamil Question Answering](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition held by Google Research India at Kaggle.
```
from transformers import AutoTokenizer, AutoModelF... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | Yuchen/muril-large-cased-hita-qa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us
|
# Question Answering model for Hindi and Tamil
This model is part of the ensemble that ranked 4/943 in the Hindi and Tamil Question Answering competition held by Google Research India at Kaggle.
| [
"# Question Answering model for Hindi and Tamil\n\nThis model is part of the ensemble that ranked 4/943 in the Hindi and Tamil Question Answering competition held by Google Research India at Kaggle."
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Question Answering model for Hindi and Tamil\n\nThis model is part of the ensemble that ranked 4/943 in the Hindi and Tamil Question Answering competition held by Google Research India at Kaggle."
... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc", "results": []}]} | Yuri/xlm-roberta-base-finetuned-marc | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"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
===============================
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: 0.9825
* Mae: 0.4956
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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