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null | transformers | # oBERT-12-upstream-pruned-unstructured-90-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-v2 | null | [
"transformers",
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"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:22:37+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Table 2 - o... | [
"# oBERT-12-upstream-pruned-unstructured-90-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'T... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order P... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-v2 | null | [
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"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:25:30+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Table 2 - o... | [
"# oBERT-12-upstream-pruned-unstructured-97-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'T... | [
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"# oBERT-12-upstream-pruned-unstructured-97-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order P... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - SQuADv1 90%` (in the upcoming... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2 | null | [
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"region:us"
] | null | 2022-06-17T06:30:41+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 90%' (in the upcoming updated version of the paper).
T... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 90%' (in the upcoming updated version of the pa... | [
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null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - SQuADv1 97%` (in the upcoming... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1-v2 | null | [
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"sparsity",
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"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:30:56+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 97%' (in the upcoming updated version of the paper).
T... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 97%' (in the upcoming updated version of the pa... | [
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null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - MNLI 90%` (in the upcoming updat... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2 | null | [
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"sparsity",
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"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:31:17+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - MNLI 90%' (in the upcoming updated version of the paper).
The dev... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - MNLI 90%' (in the upcoming updated version of the paper).\... | [
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null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - MNLI 97%` (in the upcoming updat... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2 | null | [
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"sparsity",
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"dataset:mnli",
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"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:31:30+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - MNLI 97%' (in the upcoming updated version of the paper).
The dev... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - MNLI 97%' (in the upcoming updated version of the paper).\... | [
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"# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - QQP 90%` (in the upcoming updated... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2 | null | [
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"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:31:44+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - QQP 90%' (in the upcoming updated version of the paper).
The dev-s... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - QQP 90%' (in the upcoming updated version of the paper).\n\... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for L... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - QQP 97%` (in the upcoming updated... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2 | null | [
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"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:31:57+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - QQP 97%' (in the upcoming updated version of the paper).
The dev-s... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - QQP 97%' (in the upcoming updated version of the paper).\n\... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for L... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-gear2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0... | {"license": "apache-2.0", "tags": ["vision", "gear-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-gear2", "results": []}]} | marcomameli01/segformer-b0-finetuned-segments-gear2 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"gear-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:37:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #gear-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-gear2
=====================================
This model is a fine-tuned version of nvidia/mit-b0 on the marcomameli01/gear dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1268
* Mean Iou: 0.1254
* Mean Accuracy: 0.2509
* Overall Accuracy: 0.2509
* Per Categ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #gear-segmentation #generated_from_trainer #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: 6e-05\n* train\\_batch\\_size: 2\n*... |
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. -->
# checkpoint-124500-finetuned-squad
This model was trained from scratch on an unknown dataset.
It achieves the following results o... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "checkpoint-124500-finetuned-squad", "results": []}]} | botika/checkpoint-124500-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:41:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| checkpoint-124500-finetuned-squad
=================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 14.9594
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Yen-Ju_Lu_l3das22_enh_train_dprnntac_fasnet_valid.loss.ave`
This model was trained by neillu23 using l3das22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout da2266fea920e22bb74471565e1a41a89f4cf62c
pip install -... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["l3das22"]} | espnet/Yen-Ju_Lu_l3das22_enh_train_dprnntac_fasnet_valid.loss.ave | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:l3das22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-17T06:45:25+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #audio-to-audio #dataset-l3das22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'espnet/Yen-Ju\_Lu\_l3das22\_enh\_train\_dprnntac\_fasnet\_valid.URL'
This model was trained by neillu23 using l3das22 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Jun 16 09:52:57 UTC 2022'
* python version: ... | [
"### 'espnet/Yen-Ju\\_Lu\\_l3das22\\_enh\\_train\\_dprnntac\\_fasnet\\_valid.URL'\n\n\nThis model was trained by neillu23 using l3das22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jun 16 09:52:57 UTC 2022'\n* python version: '3.8.13 ... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-l3das22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/Yen-Ju\\_Lu\\_l3das22\\_enh\\_train\\_dprnntac\\_fasnet\\_valid.URL'\n\n\nThis model was trained by neillu23 using l3das22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tiny-random-bert-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluatio... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tiny-random-bert-sharded", "results": []}]} | ArthurZ/tiny-random-bert-sharded | null | [
"transformers",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:49:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# tiny-random-bert-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# tiny-random-bert-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# tiny-random-bert-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information ... |
text2text-generation | transformers |
# T5 for Chinese Spelling Correction Model
中文拼写纠错模型
`shibing624/mengzi-t5-base-chinese-correction` evaluate SIGHAN2015 test data：
- Sentence Level: precision:0.8321, recall:0.6390, f1:0.7229
训练使用的数据集为下方提供的“SIGHAN+Wang271K中文纠错数据集”，在SIGHAN2015的测试集上达到接近SOTA水平。
未改动模型结构，finetune中文纠错数据集，评估纠错效果很好，模型潜力巨大。
## Usage
本项目开... | {"language": ["zh"], "license": "apache-2.0", "tags": ["t5", "pytorch", "zh"]} | shibing624/mengzi-t5-base-chinese-correction | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T06:58:45+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5 for Chinese Spelling Correction Model
========================================
中文拼写纠错模型
'shibing624/mengzi-t5-base-chinese-correction' evaluate SIGHAN2015 test data：
* Sentence Level: precision:0.8321, recall:0.6390, f1:0.7229
训练使用的数据集为下方提供的“SIGHAN+Wang271K中文纠错数据集”，在SIGHAN2015的测试集上达到接近SOTA水平。
未改动模型结构，finet... | [
"### 训练数据集",
"#### SIGHAN+Wang271K中文纠错数据集\n\n\n\nSIGHAN+Wang271K中文纠错数据集，数据格式："
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### 训练数据集",
"#### SIGHAN+Wang271K中文纠错数据集\n\n\n\nSIGHAN+Wang271K中文纠错数据集，数据格式："
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M5_MLM
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "M5_MLM", "results": []}]} | S2312dal/M5_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:02:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| M5\_MLM
=======
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.0447
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_swedish_test
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner_swedish_test", "results": []}]} | Nonzerophilip/bert-finetuned-ner_swedish_test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:25:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner\_swedish\_test
=================================
This model is a fine-tuned version of KBLab/bert-base-swedish-cased-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0916
* Precision: 0.6835
* Recall: 0.6391
* F1: 0.6606
* Accuracy: 0.9788
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M6_MLM
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M6_MLM", "results": []}]} | S2312dal/M6_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:27:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M6\_MLM
=======
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0237
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\... |
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. -->
# sarcasm-detection-RoBerta-base-newdata
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-RoBerta-base-newdata", "results": []}]} | jkhan447/sarcasm-detection-RoBerta-base-newdata | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:28:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-RoBerta-base-newdata
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4844
- Accuracy: 0.7824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Trai... | [
"# sarcasm-detection-RoBerta-base-newdata\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4844\n- Accuracy: 0.7824",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-RoBerta-base-newdata\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following ... |
null | null |
- `v00_5pc_given` refers to a model trained with 0 to 10% given constraints
- `v00_80pc_given` refers to a model trained with 70 to 90% given constraints | {"license": "lgpl-3.0"} | sketchai/sketch-gnn | null | [
"tensorboard",
"license:lgpl-3.0",
"region:us"
] | null | 2022-06-17T07:30:03+00:00 | [] | [] | TAGS
#tensorboard #license-lgpl-3.0 #region-us
|
- 'v00_5pc_given' refers to a model trained with 0 to 10% given constraints
- 'v00_80pc_given' refers to a model trained with 70 to 90% given constraints | [] | [
"TAGS\n#tensorboard #license-lgpl-3.0 #region-us \n"
] |
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": []}]} | hjds0923/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 | 2022-06-17T07:34:00+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:
- eval_loss: 1.1675
- eval_runtime: 146.876
- eval_samples_per_second: 73.422
- eval_steps_per_second: 4.589
- epoch: 1.0
- step: 553... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1675\n- eval_runtime: 146.876\n- eval_samples_per_second: 73.422\n- eval_steps_per_second: 4.589\n- epoch: 1.0\n... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M7_MLM
This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M7_MLM", "results": []}]} | S2312dal/M7_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:40:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M7\_MLM
=======
This model is a fine-tuned version of sentence-transformers/all-distilroberta-v1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 8.2304
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M8_MLM
This model is a fine-tuned version of [sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M8_MLM", "results": []}]} | S2312dal/M8_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T07:52:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M8\_MLM
=======
This model is a fine-tuned version of sentence-transformers/paraphrase-albert-small-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 8.9140
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1365703183/YT_Croydon_Fl... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/iantdr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T08:09:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ian anderson
@iantdr
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rajendra-ml/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | rajendra-ml/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-17T08:15:13+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | null | SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of **SPIRAL LARGE**, trained with 60k-hour LibriLight data
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
```
@inproceedings{huang2022sp... | {} | huawei-noah/SPIRAL-Large | null | [
"region:us"
] | null | 2022-06-17T08:17:20+00:00 | [] | [] | TAGS
#region-us
| SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of SPIRAL LARGE, trained with 60k-hour LibriLight data
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
| [] | [
"TAGS\n#region-us \n"
] |
null | null | SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of **SPIRAL Base with Multi-Condition Training**, trained with 960-hour LibriSpeech data, and noise dataset from [ICASSP 2021 DNS Challenge](https://github.com/microsoft/DNS-Challenge/t... | {} | huawei-noah/SPIRAL-base-MCT | null | [
"region:us"
] | null | 2022-06-17T08:19:20+00:00 | [] | [] | TAGS
#region-us
| SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of SPIRAL Base with Multi-Condition Training, trained with 960-hour LibriSpeech data, and noise dataset from ICASSP 2021 DNS Challenge for noise robustness.
Citation
========
If you f... | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rajendra-ml/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | rajendra-ml/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-17T08:22:03+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1072880528712495106/ahuQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/techreview/1655458683048/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/techreview | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T08:28:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
MIT Technology Review
@techreview
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# convnext-base-224-22k-1k-orig-cats-vs-dogs
This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Accuracy: 0.9973
<p align="center">
... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["cats_vs_dogs"], "metrics": ["accuracy"], "model-index": [{"name": "convnext-base-224-22k-1k-orig-cats-vs-dogs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cats_vs_dogs", "... | efederici/convnext-base-224-22k-1k-orig-cats-vs-dogs | null | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"vision",
"dataset:cats_vs_dogs",
"arxiv:2201.03545",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-17T08:33:45+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #vision #dataset-cats_vs_dogs #arxiv-2201.03545 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# convnext-base-224-22k-1k-orig-cats-vs-dogs
This model is a fine-tuned version of facebook/convnext-base-224-22k-1k on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Accuracy: 0.9973
<p align="center">
<img src="URL width="600"> </br>
Jockum Nordström, Ca... | [
"# convnext-base-224-22k-1k-orig-cats-vs-dogs\n\nThis model is a fine-tuned version of facebook/convnext-base-224-22k-1k on the cats_vs_dogs dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0103\n- Accuracy: 0.9973\n\n<p align=\"center\">\n <img src=\"URL width=\"600\"> </br>\n Jo... | [
"TAGS\n#transformers #pytorch #tensorboard #convnext #image-classification #vision #dataset-cats_vs_dogs #arxiv-2201.03545 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# convnext-base-224-22k-1k-orig-cats-vs-dogs\n\nThis model is a fine-tuned version of ... |
text2text-generation | transformers | This model translate engilish to romanian language. This model has been build using Helsinki-NLP/opus-mt-en-ro prebuild model. The dataset has been used for this model was "wmt16", "ro-en" dataset. Tensorflow has been used for finetunning this model. Trainning epoch was 1 for this for finetunning model. | {} | Sanjeev49/mariaJ | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T09:28:33+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model translate engilish to romanian language. This model has been build using Helsinki-NLP/opus-mt-en-ro prebuild model. The dataset has been used for this model was "wmt16", "ro-en" dataset. Tensorflow has been used for finetunning this model. Trainning epoch was 1 for this for finetunning model. | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_swedish_test_NUMb_2
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner_swedish_test_NUMb_2", "results": []}]} | Nonzerophilip/bert-finetuned-ner_swedish_test_NUMb_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T09:40:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner\_swedish\_test\_NUMb\_2
==========================================
This model is a fine-tuned version of KBLab/bert-base-swedish-cased-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0676
* Precision: 0.75
* Recall: 0.7179
* F1: 0.7336
* Accuracy: 0.981... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 92.9 | 92.1 |
| test | 91.7 | 90.7 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-xnli_fr | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T10:38:30+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 92.9, F1macro: 92.1
Set: test, F1micro: 91.7, F1macro: 90.7
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | klue-bert-base에 스마일게이트 욕설데이터를 FineTune한 모델입니다. | {"license": "apache-2.0"} | powerwarez/kindword-klue_bert-base | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T11:06:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| klue-bert-base에 스마일게이트 욕설데이터를 FineTune한 모델입니다. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned-bert-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": ... | wiselinjayajos/finetuned-bert-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T11:08:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4755
* Accuracy: 0.8456
* F1: 0.8908
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Guillaume63/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Guillaume63/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-17T11:24:27+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | joitandr/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-17T11:56:12+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# M1_cross
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["spearmanr"], "model-index": [{"name": "M1_cross", "results": []}]} | S2312dal/M1_cross | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T12:06:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M1\_cross
=========
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: 0.0066
* Pearson: 0.9828
* Spearmanr: 0.9147
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 25\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch... |
null | null | Word2Vec model obtained by training the model of [1] on a dataset of 17,500 Italian news articles related to crime events
[1] Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) An Analysis of Word2Vec for the Italian Language. In: Esposito A., Faundez-Zanuy M., Morabito F., Pas... | {"license": "cc0-1.0"} | frollo/word2vec-for-crime-categorization | null | [
"license:cc0-1.0",
"region:us"
] | null | 2022-06-17T12:45:21+00:00 | [] | [] | TAGS
#license-cc0-1.0 #region-us
| Word2Vec model obtained by training the model of [1] on a dataset of 17,500 Italian news articles related to crime events
[1] Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) An Analysis of Word2Vec for the Italian Language. In: Esposito A., Faundez-Zanuy M., Morabito F., Pas... | [] | [
"TAGS\n#license-cc0-1.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | skyline22/RL | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-17T12:57:12+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/aiww-bbcworld-elonmusk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T13:04:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & BBC News (World) & 艾未未 Ai Weiwei
@aiww-bbcworld-elonmusk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, che... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
All credit to this repo: https://huggingface.co/spaces/carolineec/informativedrawings | {"license": "mit"} | backnotprop/informative-drawings-image-to-opensketch-onnx | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2022-06-17T13:07:35+00:00 | [] | [] | TAGS
#onnx #license-mit #region-us
|
All credit to this repo: URL | [] | [
"TAGS\n#onnx #license-mit #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_swedish_test_large_set
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://hugg... | {"tags": ["generated_from_trainer"], "datasets": ["suc3"], "model-index": [{"name": "bert-finetuned-ner_swedish_test_large_set", "results": []}]} | Nonzerophilip/bert-finetuned-ner_swedish_test_large_set | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:suc3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T13:12:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-suc3 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-ner_swedish_test_large_set
This model is a fine-tuned version of KBLab/bert-base-swedish-cased-ner on the suc3 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0265
- eval_precision: 0.8542
- eval_recall: 0.8468
- eval_f1: 0.8505
- eval_accuracy: 0.9919
- eval_runtim... | [
"# bert-finetuned-ner_swedish_test_large_set\n\nThis model is a fine-tuned version of KBLab/bert-base-swedish-cased-ner on the suc3 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0265\n- eval_precision: 0.8542\n- eval_recall: 0.8468\n- eval_f1: 0.8505\n- eval_accuracy: 0.9919\n- ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-suc3 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-ner_swedish_test_large_set\n\nThis model is a fine-tuned version of KBLab/bert-base-swedish-cased-ner on the suc3 dataset.\nIt ... |
translation | 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. -->
# marian-finetuned-kde4-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-ar", "results": []}]} | anibahug/marian-finetuned-kde4-en-to-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-17T13:21:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# marian-finetuned-kde4-en-to-ar
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the kde4 dataset.
## Model description
if you want to learn about the model used check Helsinki-NLP Model
## Intended uses & limitations
## Training and evaluation data
More information needed
## Training proce... | [
"# marian-finetuned-kde4-en-to-ar\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the kde4 dataset.",
"## Model description\nif you want to learn about the model used check Helsinki-NLP Model",
"## Intended uses & limitations",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# marian-finetuned-kde4-en-to-ar\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-... |
object-detection | transformers |
# YOLOS (small-sized) model
The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [t... | {"license": "apache-2.0", "tags": ["object-detection", "face-mask-detection"], "datasets": ["coco", "face-mask-detection"], "metrics": ["average precision", "recall", "IOU"], "widget": [{"src": "https://drive.google.com/uc?id=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro", "example_title": "City Folk"}, {"src": "https://huggingfac... | nickmuchi/yolos-small-finetuned-masks | null | [
"transformers",
"pytorch",
"safetensors",
"yolos",
"object-detection",
"face-mask-detection",
"dataset:coco",
"dataset:face-mask-detection",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-17T13:29:47+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #safetensors #yolos #object-detection #face-mask-detection #dataset-coco #dataset-face-mask-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| YOLOS (small-sized) model
=========================
The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repositor... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe YOLOS model was pre-trained on ImageNet-1k and fine-tuned on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/... | [
"TAGS\n#transformers #pytorch #safetensors #yolos #object-detection #face-mask-detection #dataset-coco #dataset-face-mask-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **PongNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **PongNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Basel... | {"library_name": "stable-baselines3", "tags": ["PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PongNoFrameskip-v4", "type":... | joitandr/dqn-PongNoFrameskip-v4 | null | [
"stable-baselines3",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-17T13:53:22+00:00 | [] | [] | TAGS
#stable-baselines3 #PongNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing PongNoFrameskip-v4
This is a trained model of a DQN agent playing PongNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usa... | [
"# DQN Agent playing PongNoFrameskip-v4\nThis is a trained model of a DQN agent playing PongNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents includ... | [
"TAGS\n#stable-baselines3 #PongNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing PongNoFrameskip-v4\nThis is a trained model of a DQN agent playing PongNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a traini... |
question-answering | transformers |
# deberta-large-japanese-aozora-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora) and [UD_Japane... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b... | KoichiYasuoka/deberta-large-japanese-aozora-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"japanese",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T14:00:25+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# deberta-large-japanese-aozora-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-large-japanese-aozora and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when specifyi... | [
"# deberta-large-japanese-aozora-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-large-japanese-aozora and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity whe... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# deberta-large-japanese-aozora-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for depen... |
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-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | RayMelius/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T14:56:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on the conll2003 dataset.",
"## Model d... |
feature-extraction | transformers | # Model Card for flax-tiny-random-bert-sharded
# Model Details
## Model Description
This model is used to check that the sharding of a flax_model works properly. See [`test_checkpoint_sharding_from_hub`](https://github.com/huggingface/transformers/blob/main/tests/test_modeling_flax_common.py#L1049).
# Uses
The ... | {"tags": ["flax"]} | ArthurZ/flax-tiny-random-bert-sharded | null | [
"transformers",
"jax",
"bert",
"feature-extraction",
"flax",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T15:08:40+00:00 | [] | [] | TAGS
#transformers #jax #bert #feature-extraction #flax #endpoints_compatible #region-us
| # Model Card for flax-tiny-random-bert-sharded
# Model Details
## Model Description
This model is used to check that the sharding of a flax_model works properly. See 'test_checkpoint_sharding_from_hub'.
# Uses
The model is not designed to be used and serves a testing purpose.
### Software
- Transformers 4.21.... | [
"# Model Card for flax-tiny-random-bert-sharded",
"# Model Details",
"## Model Description\n This model is used to check that the sharding of a flax_model works properly. See 'test_checkpoint_sharding_from_hub'.",
"# Uses\n\nThe model is not designed to be used and serves a testing purpose.",
"### Software\... | [
"TAGS\n#transformers #jax #bert #feature-extraction #flax #endpoints_compatible #region-us \n",
"# Model Card for flax-tiny-random-bert-sharded",
"# Model Details",
"## Model Description\n This model is used to check that the sharding of a flax_model works properly. See 'test_checkpoint_sharding_from_hub'.",
... |
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-53-Enlgish-FT-ASCEND-colab
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-engl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ascend"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab", "results": []}]} | Ryna/wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:ascend",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T15:16:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-ascend #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the ascend dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the ascend dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-ascend #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-engl... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BeardedJohn/bert-finetuned-seq-classification-fake-news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BeardedJohn/bert-finetuned-seq-classification-fake-news", "results": []}]} | BeardedJohn/bert-finetuned-seq-classification-fake-news | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T15:58:33+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BeardedJohn/bert-finetuned-seq-classification-fake-news
=======================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0719
* Validation Loss: 0.0214
* Epoch: 0
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 332, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1291192333199958017/SvH8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hillaryclinton/1672988569477/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hillaryclinton | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T16:47:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Hillary Clinton
@hillaryclinton
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1246469365089939456/jAjE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pdchina/1655488982839/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pdchina | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T17:01:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
People's Daily, China
@pdchina
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | 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. -->
# MBART-finetuned-Spanish
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-la... | {"tags": ["summarization", "Mbart", "seq2seq", "es", "abstractive summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "MBART-finetuned-Spanish", "results": []}]} | eslamxm/MBART-finetuned-Spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"Mbart",
"seq2seq",
"es",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T17:03:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #Mbart #seq2seq #es #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
|
# MBART-finetuned-Spanish
This model is a fine-tuned version of facebook/mbart-large-50 on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7435
- Rouge-1: 23.72
- Rouge-2: 7.61
- Rouge-l: 22.97
- Gen Len: 51.33
- Bertscore: 70.78
## Model description
More information ne... | [
"# MBART-finetuned-Spanish\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.7435\n- Rouge-1: 23.72\n- Rouge-2: 7.61\n- Rouge-l: 22.97\n- Gen Len: 51.33\n- Bertscore: 70.78",
"## Model description\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #Mbart #seq2seq #es #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"# MBART-finetuned-Spanish\n\nThis model is a fine-tuned version of faceboo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **VideoPinball-v4**
This is a trained model of a **DQN** agent playing **VideoPinball-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["VideoPinball-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "VideoPinball-v4", "type": "Vide... | skyline22/RLpinball | null | [
"stable-baselines3",
"VideoPinball-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-17T17:20:38+00:00 | [] | [] | TAGS
#stable-baselines3 #VideoPinball-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing VideoPinball-v4
This is a trained model of a DQN agent playing VideoPinball-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (wi... | [
"# DQN Agent playing VideoPinball-v4\nThis is a trained model of a DQN agent playing VideoPinball-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #VideoPinball-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing VideoPinball-v4\nThis is a trained model of a DQN agent playing VideoPinball-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framew... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | johntang/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T17:54:34+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3426
- Accuracy: 0.8767
- F1: 0.8787
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3426\n- Accuracy: 0.8767\n- F1: 0.8787",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
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. -->
# M6_cross
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["spearmanr"], "model-index": [{"name": "M6_cross", "results": []}]} | S2312dal/M6_cross | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T18:41:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M6\_cross
=========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0084
* Pearson: 0.9811
* Spearmanr: 0.9075
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 20\n* seed: 25\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 #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T18:41:49+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you ... | [
"# gemasphi/laprador\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-fine-tuned-cola", "results": []}]} | c17hawke/bert-fine-tuned-cola | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T18:51:35+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-cola
====================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5013
* Validation Loss: 0.4341
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
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. -->
# M1_MLM_cross
This model is a fine-tuned version of [S2312dal/M1_MLM](https://huggingface.co/S2312dal/M1_MLM) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["spearmanr"], "model-index": [{"name": "M1_MLM_cross", "results": []}]} | S2312dal/M1_MLM_cross | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T18:52:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M1\_MLM\_cross
==============
This model is a fine-tuned version of S2312dal/M1\_MLM on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0106
* Pearson: 0.9723
* Spearmanr: 0.9112
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 25\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 #tensorboard #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: 2e-05\n* train\\_batch... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1502217816421941249/jOIq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/itsamedevdev | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T19:01:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ItAMeDevDev
@itsamedevdev
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-dataset_asr-demo-colab
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-sp... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "model-index": [{"name": "wav2vec2-base-dataset_asr-demo-colab", "results": []}]} | aminnaghavi/wav2vec2-base-dataset_asr-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T19:17:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-dataset\_asr-demo-colab
=====================================
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 295.0834
* Wer: 0.8282
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-superb #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: 0.001\n* train\\_ba... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="danieladejumo/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | danieladejumo/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-17T19:20:23+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | ericklerouge123/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T19:42:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
audio-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. -->
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intend... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "tmp_trainer", "results": []}]} | Mahmoud1816Yasser/tmp_trainer | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T20:05:23+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #endpoints_compatible #region-us
|
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamete... | [
"# tmp_trainer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameter... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #endpoints_compatible #region-us \n",
"# tmp_trainer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information need... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador_f
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes eas... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador_f | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T20:10:48+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador_f
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then yo... | [
"# gemasphi/laprador_f\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador_f\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clusterin... |
text-classification | transformers |
### finetuned-distilbert-adult-content-catgorization
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the adult_content dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0065
- F1_score(weighted): 0.90
### Model description
M... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-distilbert-adult-content-detection", "results": []}]} | valurank/finetuned-distilbert-adult-content-detection | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-17T20:34:03+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### finetuned-distilbert-adult-content-catgorization
This model is a fine-tuned version of distilbert-base-uncased on the adult\_content dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0065
* F1\_score(weighted): 0.90
### Model description
More information needed
### Intended uses ... | [
"### finetuned-distilbert-adult-content-catgorization\n\n\nThis model is a fine-tuned version of distilbert-base-uncased on the adult\\_content dataset.\nIt achieves the following results on the evaluation set:\n\n\n* Loss: 0.0065\n* F1\\_score(weighted): 0.90",
"### Model description\n\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### finetuned-distilbert-adult-content-catgorization\n\n\nThis model is a fine-tuned version of distilbert-base-uncased on the adult\\_conte... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1319790462966837248/wzix... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/datgameryolo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T20:54:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
DatGamerYolo
@datgameryolo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1000833138
- CO2 Emissions (in grams): 999.838587232387
## Validation Metrics
- Loss: 2.4244203567504883
- Rouge1: 25.7023
- Rouge2: 8.5872
- RougeL: 18.6776
- RougeLsum: 19.821
- Gen Len: 39.732
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autotrain", "datasets": ["ouiame/autotrain-data-orangesum"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 999.838587232387} | ouiame/bert2gpt2frenchSumm | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain",
"unk",
"dataset:ouiame/autotrain-data-orangesum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T22:10:00+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-orangesum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1000833138
- CO2 Emissions (in grams): 999.838587232387
## Validation Metrics
- Loss: 2.4244203567504883
- Rouge1: 25.7023
- Rouge2: 8.5872
- RougeL: 18.6776
- RougeLsum: 19.821
- Gen Len: 39.732
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1000833138\n- CO2 Emissions (in grams): 999.838587232387",
"## Validation Metrics\n\n- Loss: 2.4244203567504883\n- Rouge1: 25.7023\n- Rouge2: 8.5872\n- RougeL: 18.6776\n- RougeLsum: 19.821\n- Gen Len: 39.732",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-orangesum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1000833138\n- CO2 Emissions (in... |
null | null | license: afl-3.0
it makes fart noise
| {} | HHHHHHHHHHHHHHHHHHHHHHHHH/Fart | null | [
"region:us"
] | null | 2022-06-17T23:48:25+00:00 | [] | [] | TAGS
#region-us
| license: afl-3.0
it makes fart noise
| [] | [
"TAGS\n#region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-mri
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-mri", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mriDataSet", "type": "imagefold... | raedinkhaled/vit-base-mri | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-17T23:58:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-mri
============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mriDataSet dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0453
* Accuracy: 0.9827
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | chandrasutrisnotjhong/bert-finetuned-ner | 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 | 2022-06-18T01:02:31+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-ner
==================
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: 0.0637
* Precision: 0.9337
* Recall: 0.9509
* F1: 0.9422
* Accuracy: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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* learning... |
text-generation | transformers | # The world machine DialoGPT model | {"tags": ["conversational"]} | ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T01:05:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # The world machine DialoGPT model | [
"# The world machine DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# The world machine DialoGPT model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | chandrasutrisnotjhong/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T01:42:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
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": []}]} | sasuke/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 | 2022-06-18T02:00:37+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: 1.1458
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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... |
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. -->
# ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1
This model is a fine-tuned version of [gary109/ai-light-dance_singing_... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1", "results": []}]} | gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T02:12:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53-5gram-v1
============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53-5gram on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING dataset.
It achieves the following results o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | skpawar1305/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T02:23:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0903
* Accuracy: 0.9834
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-chunking
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-finetuned-chunking", "results": []}]} | mariolinml/roberta-large-finetuned-chunking | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T03:04:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-chunking
================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4192
* Precision: 0.3222
* Recall: 0.3161
* F1: 0.3191
* Accuracy: 0.8632
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* ... |
text-generation | transformers |
# AgedBlaine DialoGPT Model 2 | {"tags": ["conversational"]} | AlyxTheKitten/DialoGPT-medium-Jimmis-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T03:12:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# AgedBlaine DialoGPT Model 2 | [
"# AgedBlaine DialoGPT Model 2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# AgedBlaine DialoGPT Model 2"
] |
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. -->
# muppet-roberta-base-finetuned-squad
This model is a fine-tuned version of [facebook/muppet-roberta-base](https://huggingface.co/... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "muppet-roberta-base-finetuned-squad", "results": []}]} | janeel/muppet-roberta-base-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T03:37:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| muppet-roberta-base-finetuned-squad
===================================
This model is a fine-tuned version of facebook/muppet-roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9017
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased-finetuned-NLP-IE-2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](htt... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-NLP-IE-2", "results": []}]} | Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T04:31:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-spanish-wwm-cased-finetuned-NLP-IE-2
==============================================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5279
* Accuracy: 0.7836
Model description
------------... | [
"### 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 #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_... |
text-classification | transformers | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... | {} | BM-K/KoMiniLM-68M | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"arxiv:2002.10957",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T06:20:19+00:00 | [
"2002.10957"
] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #arxiv-2002.10957 #autotrain_compatible #endpoints_compatible #region-us
| KoMiniLM
========
Korean mini language model
Overview
--------
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight kore... | [
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project.",
"### Data sets",
"#... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #arxiv-2002.10957 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | kjunelee/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T07:01:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\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. -->
# M4_MLM_cross
This model is a fine-tuned version of [S2312dal/M4_MLM](https://huggingface.co/S2312dal/M4_MLM) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["spearmanr"], "model-index": [{"name": "M4_MLM_cross", "results": []}]} | S2312dal/M4_MLM_cross | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T07:13:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M4\_MLM\_cross
==============
This model is a fine-tuned version of S2312dal/M4\_MLM on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0222
* Pearson: 0.9472
* Spearmanr: 0.8983
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 25\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 #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... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | pinot/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T07:16:34+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-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7642
* Wer: 0.5894
Model description
-----------------
More informat... | [
"### 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: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
# :(
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | 29thDay/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-18T07:50:04+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# M6_MLM_cross
This model is a fine-tuned version of [S2312dal/M6_MLM](https://huggingface.co/S2312dal/M6_MLM) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["spearmanr"], "model-index": [{"name": "M6_MLM_cross", "results": []}]} | S2312dal/M6_MLM_cross | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T07:51:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M6\_MLM\_cross
==============
This model is a fine-tuned version of S2312dal/M6\_MLM on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0197
* Pearson: 0.9680
* Spearmanr: 0.9098
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 25\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 #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/991329326846087169/vxoth... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/andrewdoyle_com-conceptualjames-titaniamcgrath/1655543501221/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/andrewdoyle_com-conceptualjames-titaniamcgrath | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T08:11:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Titania McGrath & Andrew Doyle & James Lindsay, weaponizing your mom
@andrewdoyle\_com-conceptualjames-titaniamcgrath
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola_2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-fine-tuned-cola_2", "results": []}]} | c17hawke/bert-fine-tuned-cola_2 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T08:20:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-cola\_2
=======================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3078
* Validation Loss: 0.4072
* Epoch: 1
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | eugenetanjc/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T08:33:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# wav2vec2-base-timit-demo-google-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-google-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model desc... |
text-generation | transformers |
# DialoGPT-ElonMusk: Chat with Elon Musk
This is a conversational language model of Elon Musk. The bot's conversation abilities come from Microsoft's [DialoGPT-small conversational model](https://huggingface.co/microsoft/DialoGPT-small) fine-tuned on conversation transcripts of 22 interviews with Elon Musk from [here... | {"license": "mit", "tags": ["conversational"]} | dennis-fast/DialoGPT-ElonMusk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T08:43:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DialoGPT-ElonMusk: Chat with Elon Musk
This is a conversational language model of Elon Musk. The bot's conversation abilities come from Microsoft's DialoGPT-small conversational model fine-tuned on conversation transcripts of 22 interviews with Elon Musk from here.
| [
"# DialoGPT-ElonMusk: Chat with Elon Musk\n\nThis is a conversational language model of Elon Musk. The bot's conversation abilities come from Microsoft's DialoGPT-small conversational model fine-tuned on conversation transcripts of 22 interviews with Elon Musk from here."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT-ElonMusk: Chat with Elon Musk\n\nThis is a conversational language model of Elon Musk. The bot's conversation abilities co... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nevepam/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | nevepam/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-18T09:00:00+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
question-answering | transformers |
# deberta-base-japanese-unidic-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic) and [UD_Japanese-... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b... | KoichiYasuoka/deberta-base-japanese-unidic-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"japanese",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T09:20:24+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# deberta-base-japanese-unidic-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-unidic and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when specifying... | [
"# deberta-base-japanese-unidic-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-unidic and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when ... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# deberta-base-japanese-unidic-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for depend... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | sgraf202/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T09:41:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7404
- Accuracy: 0.4688
- F1: 0.5526
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7404\n- Accuracy: 0.4688\n- F1: 0.5526",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt a... |
summarization | 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. -->
# mt5-small-test-amazon
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an un... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-test-amazon", "results": []}]} | nestoralvaro/mt5-small-test-amazon | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T10:05:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-test-amazon
=====================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9515
* Rouge1: 30.3066
* Rouge2: 3.3019
* Rougel: 30.1887
* Rougelsum: 30.0314
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
null | null | Pikachu riding a skateboard
| {} | HanniJoe/Pikachu | null | [
"region:us"
] | null | 2022-06-18T10:24:21+00:00 | [] | [] | TAGS
#region-us
| Pikachu riding a skateboard
| [] | [
"TAGS\n#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. -->
# xlm-roberta-large-xnli-finetuned-mnli-SJP-v2
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["swiss_judgment_prediction"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli-SJP-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "swiss_judgment_pre... | tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:swiss_judgment_prediction",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T10:53:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-large-xnli-finetuned-mnli-SJP-v2
============================================
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss\_judgment\_prediction dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8093
* Accuracy: 0.5954
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3 import PPO
from huggingface_sb3 i... | {"library_name": "stable-baselines3", "tags": ["MountainCarContinuous-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCarContinuous-... | danieladejumo/ppo-mountan_car_continuous | null | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-18T11:03:38+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCarContinuous-v0
This is a trained model of a PPO agent playing MountainCarContinuous-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
| [
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] | [
"TAGS\n#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Sta... |
summarization | 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. -->
# mt5-small-test-amazon-v2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-test-amazon-v2", "results": []}]} | nestoralvaro/mt5-small-test-amazon-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T11:12:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-test-amazon-v2
========================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0555
* Rouge1: 27.8124
* Rouge2: 15.3682
* Rougel: 27.8646
* Rougelsum: 27.9044
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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. -->
# ff_analysis_5
This model is a fine-tuned version of [zdreiosis/ff_analysis_5](https://huggingface.co/zdreiosis/ff_analysis_5) on... | {"license": "apache-2.0", "tags": ["gen_ffa", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "ff_analysis_5", "results": []}]} | zdreiosis/ff_analysis_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"gen_ffa",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-18T11:46:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #gen_ffa #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ff\_analysis\_5
===============
This model is a fine-tuned version of zdreiosis/ff\_analysis\_5 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0824
* F1: 0.9306
* Roc Auc: 0.9483
* Accuracy: 0.8137
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #gen_ffa #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\... |
null | null | # poetry-generation-firstline-mbart-all-fi-unsorted
* `firstline`: generates the first poem line from keywords
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `all`: trained on data from Project Gutenberg, Wikisource, Poesia publishing house
* `fi`: Finnish ... | {} | varie/poetry-generation-firstline-mbart-all-fi-unsorted | null | [
"pytorch",
"region:us"
] | null | 2022-06-18T11:47:34+00:00 | [] | [] | TAGS
#pytorch #region-us
| # poetry-generation-firstline-mbart-all-fi-unsorted
* 'firstline': generates the first poem line from keywords
* 'mbart': base model is facebook/mbart-large-cc25
* 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house
* 'fi': Finnish language
* 'unsorted': the order of input keywords ... | [
"# poetry-generation-firstline-mbart-all-fi-unsorted\n\n * 'firstline': generates the first poem line from keywords\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house\n * 'fi': Finnish language\n * 'unsorted': the order of inpu... | [
"TAGS\n#pytorch #region-us \n",
"# poetry-generation-firstline-mbart-all-fi-unsorted\n\n * 'firstline': generates the first poem line from keywords\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house\n * 'fi': Finnish language... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-squad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | lmqg/mt5-small-squad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-18T11:55:14+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-squad-qg'
=======================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: en
* Training data: lmqg/qg\_squa... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: en\n*... |
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