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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | Shenghao1993/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:02:56+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
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: 0.3859
* F1: 0.7032
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 #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\\_rate: 5e-05\n... |
text-generation | transformers |
# HarryPotterBot | {"tags": ["conversational"]} | quirkys/DialoGPT-small-harrypotter | null | [
"transformers",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T12:10:16+00:00 | [] | [] | TAGS
#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# HarryPotterBot | [
"# HarryPotterBot"
] | [
"TAGS\n#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# HarryPotterBot"
] |
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. -->
# gbert-large
This model is a fine-tuned version of deepset/gbert-large
It was fine-tuned on poetry from Projekt Gutenberg in or... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gbert-large", "results": []}]} | Anjoe/gbert-large | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:16:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| gbert-large
===========
This model is a fine-tuned version of deepset/gbert-large
It was fine-tuned on poetry from Projekt Gutenberg in order to do masked language modeling tasks in poetry generation (synonym creation for rythm and to find rhyming pairs)
* Loss: 2.1519
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #bert #fill-mask #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: 32\n* ... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras", "tags": ["language-modeling"]} | cakiki/fnet | null | [
"keras",
"tensorboard",
"language-modeling",
"has_space",
"region:us"
] | null | 2022-06-13T12:20:06+00:00 | [] | [] | TAGS
#keras #tensorboard #language-modeling #has_space #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #language-modeling #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | Shenghao1993/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:21:03+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1719
* F1: 0.8544
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 #xlm-roberta #token-classification #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: 5e-05\n* train\\_batch\\_size: 24\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. -->
# xlmr-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wiki... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlmr-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "a... | Andrey1989/xlmr-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:23:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-wikiann #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlmr-finetuned-ner
==================
This model is a fine-tuned version of xlm-roberta-base on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1395
* Precision: 0.9044
* Recall: 0.9137
* F1: 0.9090
* Accuracy: 0.9649
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-wikiann #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\\... |
token-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. -->
# jameswrbrookes/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jameswrbrookes/bert-finetuned-ner", "results": []}]} | jameswrbrookes/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:38:21+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jameswrbrookes/bert-finetuned-ner
=================================
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: 0.0280
* Validation Loss: 0.0539
* Epoch: 2
Model description
-----------------
More information... | [
"### 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': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-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\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mrpc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dataset.
It achi... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC"... | JeremiahZ/roberta-base-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T12:38:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mrpc
This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158
## Model description
More information needed
## Intended uses & limitations
More information needed... | [
"# mrpc\n\nThis model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4898\n- Accuracy: 0.9020\n- F1: 0.9296\n- Combined Score: 0.9158",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# mrpc\n\nThis model is a fine-tuned version of roberta-base on the GLUE M... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-regular-test
i was stupid and all the newline tokens are replaced with [/n] so be wary if you're using the demo on this page... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-regular-test", "results": []}]} | crumb/gpt2-regular-large | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T13:08:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt-regular-test
================
i was stupid and all the newline tokens are replaced with [/n] so be wary if you're using the demo on this page that that just means new line
This model is a fine-tuned version of gpt2-large on the entirety of Regular Show. It achieves the following results on the evaluation set (T... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batc... |
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | lariskelmer/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T13:30:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1505
* Gen Len: 34.1036
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: 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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #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-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-emotion
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the em... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "bert-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "m... | ericntay/bert-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T13:32:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-emotion
======================
This model is a fine-tuned version of bert-base-cased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1582
* Accuracy: 0.937
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: 10\n* eval\\_batch\\_size: 10\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 #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-finetuned", "results": []}]} | laboyle1/distilbert-finetuned | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T13:38:50+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-finetuned
====================
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: 1.9895
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: 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #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: 8\n* eval\... |
object-detection | null |
## Darknet Object Detection on the COCO dataset
This model uses a pretrained YOLO Darknet model to perform object detection on an input image. The model is able to identify 80 classes from the COCO dataset. The classes are listed here `config/coco.names`.
### Usage
Clone the repository using
```python
repo = Reposit... | {"tags": ["object-detection", "COCO", "YOLO", "Darknet"], "model-index": [{"name": "darknet-coco-object_detection", "results": [{"task": {"type": "object-detection", "name": "object-detection"}, "dataset": {"name": "COCO", "type": "COCO"}, "metrics": [{"type": "None", "value": "1", "name": "None"}]}]}]} | danieladejumo/darknet-coco-object_detection | null | [
"object-detection",
"COCO",
"YOLO",
"Darknet",
"model-index",
"region:us"
] | null | 2022-06-13T13:43:12+00:00 | [] | [] | TAGS
#object-detection #COCO #YOLO #Darknet #model-index #region-us
|
## Darknet Object Detection on the COCO dataset
This model uses a pretrained YOLO Darknet model to perform object detection on an input image. The model is able to identify 80 classes from the COCO dataset. The classes are listed here 'config/URL'.
### Usage
Clone the repository using
Run a detection by using the ... | [
"## Darknet Object Detection on the COCO dataset\n\nThis model uses a pretrained YOLO Darknet model to perform object detection on an input image. The model is able to identify 80 classes from the COCO dataset. The classes are listed here 'config/URL'.",
"### Usage\nClone the repository using\n\n\nRun a detection... | [
"TAGS\n#object-detection #COCO #YOLO #Darknet #model-index #region-us \n",
"## Darknet Object Detection on the COCO dataset\n\nThis model uses a pretrained YOLO Darknet model to perform object detection on an input image. The model is able to identify 80 classes from the COCO dataset. The classes are listed here ... |
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/1521075169611112448/S_w8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/salgotrader/1655131582645/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/salgotrader | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T13:45:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
URL
@salgotrader
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"
] |
token-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. -->
# jameswrbrookes/bert-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_keras_callback"], "model-index": [{"name": "jameswrbrookes/bert-finetuned-chunking", "results": []}]} | jameswrbrookes/bert-finetuned-chunking | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T13:51:23+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jameswrbrookes/bert-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:
* Train Loss: 0.1145
* Validation Loss: 0.1614
* Epoch: 2
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-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: {'inner\\_optimizer': {'class\\_name': '... |
question-answering | transformers | The roberta-base-ca-cased-qa is a Question Answering (QA) model for the Catalan language fine-tuned from the BERTa model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
Datasets
We used the Catalan QA data... | {"license": "cc0-1.0"} | crodri/roberta-base-ca-v2-qa-catalanqa | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T14:05:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #license-cc0-1.0 #endpoints_compatible #region-us
| The roberta-base-ca-cased-qa is a Question Answering (QA) model for the Catalan language fine-tuned from the BERTa model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
Datasets
We used the Catalan QA data... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #license-cc0-1.0 #endpoints_compatible #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... | thenewcompany/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T14:14:39+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... |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| {"license": "other", "title": "TextGen", "emoji": "\ud83d\udc20", "colorFrom": "yellow", "colorTo": "red", "sdk": "gradio", "sdk_version": "3.0.14", "app_file": "app.py", "pinned": false} | yanngraf/imnotarobot | null | [
"license:other",
"region:us"
] | null | 2022-06-13T14:29:10+00:00 | [] | [] | TAGS
#license-other #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#license-other #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/1244575861912883201/2J-E... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/egbertchannel/1655135356461/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/egbertchannel | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T14:40:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Egbert
@egbertchannel
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 |
# wav2vec2-xls-r-1b-ft-cy
Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) with the [Welsh Common Voice version 9 dataset](https://huggingface.co/datasets/common_voice).
Source code and scripts for training acoustic and KenLM language models, as well as examples of inference... | {"language": "cy", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "ken-lm", "robust-speech-event", "speech"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-1b-ft-cy with KenLM language model (by Bangor University)", "results"... | techiaith/wav2vec2-xls-r-1b-ft-cy | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"ken-lm",
"robust-speech-event",
"speech",
"cy",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T15:28:49+00:00 | [] | [
"cy"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #ken-lm #robust-speech-event #speech #cy #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-1b-ft-cy
Fine-tuned facebook/wav2vec2-xls-r-1b with the Welsh Common Voice version 9 dataset.
Source code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at URL
## Usage
The wav2vec2-xls-r-1... | [
"# wav2vec2-xls-r-1b-ft-cy\n\nFine-tuned facebook/wav2vec2-xls-r-1b with the Welsh Common Voice version 9 dataset.\n\nSource code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at URL",
"## Usage\n\nThe wav2v... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #ken-lm #robust-speech-event #speech #cy #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-1b-ft-cy\n\nFine-tuned facebook/wav2vec2-xls-r-1b with the Welsh ... |
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... | chiranthans23/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 | 2022-06-13T15:40:42+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: 0.1372
* F1: 0.8621
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\\_... |
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
from stable_baselines3 import ...
from huggingface_sb3 ... | {"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... | KCiebiera/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T16:09:01+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... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/americasnlp22-asr-gn`
This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't ... | {"language": "gn", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["americasnlp22"]} | espnet/americasnlp22-asr-gn | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"gn",
"dataset:americasnlp22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-13T16:11:45+00:00 | [
"1804.00015"
] | [
"gn"
] | TAGS
#espnet #audio #automatic-speech-recognition #gn #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/americasnlp22-asr-gn'
This model was trained by Pavel Denisov using americasnlp22 recipe in espnet.
### Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
RESULTS
=======
Environments
------------
* date... | [
"### 'espnet/americasnlp22-asr-gn'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Jun 5... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #gn #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/americasnlp22-asr-gn'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet inst... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/americasnlp22-asr-quy`
This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't... | {"language": "quy", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["americasnlp22"]} | espnet/americasnlp22-asr-quy | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"quy",
"dataset:americasnlp22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-13T16:12:18+00:00 | [
"1804.00015"
] | [
"quy"
] | TAGS
#espnet #audio #automatic-speech-recognition #quy #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/americasnlp22-asr-quy'
This model was trained by Pavel Denisov using americasnlp22 recipe in espnet.
### Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
RESULTS
=======
Environments
------------
* dat... | [
"### 'espnet/americasnlp22-asr-quy'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Jun ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #quy #dataset-americasnlp22 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/americasnlp22-asr-quy'\n\n\nThis model was trained by Pavel Denisov using americasnlp22 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet in... |
null | null | simcse_modelv3 | {} | PriaPillai/simcse_modelv3 | null | [
"region:us"
] | null | 2022-06-13T16:12:44+00:00 | [] | [] | TAGS
#region-us
| simcse_modelv3 | [] | [
"TAGS\n#region-us \n"
] |
null | null |
# my-cool-model
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases... | {"language": ["en"], "license": "mit", "tags": ["autogenerated-modelcard"]} | nateraw/modelcard-creator-test | null | [
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-06-13T16:13:05+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#autogenerated-modelcard #en #arxiv-1910.09700 #license-mit #region-us
|
# my-cool-model
## Table of Contents
- Model Details
- How To Get Started With the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out of Scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
- Evaluation Results
- Environmental Impact
- Licensing Information
- Citation ... | [
"# my-cool-model",
"## Table of Contents\n- Model Details\n- How To Get Started With the Model\n- Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out of Scope Use\n- Limitations and Biases\n- Training\n - Training Data\n - Training Procedure\n- Evaluation Results\n- Environmental Impact\n- Licensing In... | [
"TAGS\n#autogenerated-modelcard #en #arxiv-1910.09700 #license-mit #region-us \n",
"# my-cool-model",
"## Table of Contents\n- Model Details\n- How To Get Started With the Model\n- Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out of Scope Use\n- Limitations and Biases\n- Training\n - Training Data\... |
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. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2
This model is a fine-tuned version of [googl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T16:15:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-RAW\_data\_prep\_2021\_12\_26\_\_\_t22027\_162754.csv\_\_g\_mt5\_base\_L2
=================================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the eval... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #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* learning\\_rat... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-bam
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["bam", "bm"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-bam | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"bam",
"bm",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T16:20:50+00:00 | [] | [
"bam",
"bm"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #bam #bm #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-bam
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - BAM (Bamba... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #bam #bm #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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... | ianspektor/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T16:47:44+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... |
image-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. -->
# amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-2... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat", "results": []}]} | amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"tf",
"tensorboard",
"swin",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T16:48:09+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #swin #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat
==========================================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4117
* Validation Loss: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 5e-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 #tensorboard #swin #image-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',... |
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
from stable_baselines3 import ...
from huggingface_sb3 ... | {"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... | MerlinTK/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T16:54:26+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... |
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="Alian3785/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | Alian3785/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-13T17:25:23+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"
] |
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="Alian3785/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": "7.54 +/... | Alian3785/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-13T17:34:17+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"
] |
null | null | text_fuckyou | {} | gran0lah/wtfidoing | null | [
"region:us"
] | null | 2022-06-13T17:38:49+00:00 | [] | [] | TAGS
#region-us
| text_fuckyou | [] | [
"TAGS\n#region-us \n"
] |
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
from stable_baselines3 import ...
from huggingface_sb3 ... | {"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... | JMillan/ppo-LunarLander-v2-2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T17:56:12+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... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras", "tags": ["switch-transformer"]} | bndgyawali/switch-transformer | null | [
"keras",
"tensorboard",
"switch-transformer",
"region:us"
] | null | 2022-06-13T17:58:21+00:00 | [] | [] | TAGS
#keras #tensorboard #switch-transformer #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #switch-transformer #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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. -->
# AraT5-base-finetune-ar-wikilingua
This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT... | {"tags": ["summarization", "ar", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "AraT5-base-finetune-ar-wikilingua", "results": []}]} | eslamxm/AraT5-base-finetune-ar-wikilingua | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T18:22:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #ar #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AraT5-base-finetune-ar-wikilingua
=================================
This model is a fine-tuned version of UBC-NLP/AraT5-base on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6110
* Rouge-1: 19.97
* Rouge-2: 6.9
* Rouge-l: 18.25
* Gen Len: 18.45
* Bertscore: 69.44
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #ar #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters we... |
null | null | # DALL·E Mini Model Card
This dont is a copy, credits for https://huggingface.co/dalle-mini/dalle-mini/tree/main
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incor... | {} | caio13/dalle-mono | null | [
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"region:us"
] | null | 2022-06-13T18:27:12+00:00 | [
"2102.08981",
"2012.09841",
"1910.13461",
"1910.09700"
] | [] | TAGS
#arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #region-us
| # DALL·E Mini Model Card
This dont is a copy, credits for URL
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).
## Mod... | [
"# DALL·E Mini Model Card\n\nThis dont is a copy, credits for URL\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming)... | [
"TAGS\n#arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #region-us \n",
"# DALL·E Mini Model Card\n\nThis dont is a copy, credits for URL\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incor... |
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-base-finetuned-en-cnn
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the... | {"license": "apache-2.0", "tags": ["summarization", "en", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "mt5-base-finetuned-en-cnn", "results": []}]} | eslamxm/mt5-base-finetuned-en-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"en",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T18:48:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base-finetuned-en-cnn
This model is a fine-tuned version of google/mt5-base on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1286
- Rouge-1: 22.84
- Rouge-2: 10.11
- Rouge-l: 21.8
- Gen Len: 19.0
- Bertscore: 87.12
## Model description
More information needed
... | [
"# mt5-base-finetuned-en-cnn\n\nThis model is a fine-tuned version of google/mt5-base on the cnn_dailymail dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.1286\n- Rouge-1: 22.84\n- Rouge-2: 10.11\n- Rouge-l: 21.8\n- Gen Len: 19.0\n- Bertscore: 87.12",
"## Model description\n\nMore in... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-finetuned-en-cnn\n\nThis model i... |
null | transformers | This model, (DeLADE+[CLS])+, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone using hard negative mining and knowledge distillation with ColBERT teacher, which is detailed in the below paper.
*[A Dense Representation Framework for Lexical and Semantic Ma... | {} | jacklin/DeLADE-CLS-P | null | [
"transformers",
"pytorch",
"arxiv:2112.04666",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T19:18:47+00:00 | [
"2112.04666"
] | [] | TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us
| This model, (DeLADE+[CLS])+, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone using hard negative mining and knowledge distillation with ColBERT teacher, which is detailed in the below paper.
*A Dense Representation Framework for Lexical and Semantic Mat... | [] | [
"TAGS\n#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer X-Large (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-archite... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Transducer", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr", "fisher_corpus", "Switchboard-1", "WSJ-0", "WSJ-1", "National-Singapore-Cor... | nvidia/stt_en_conformer_transducer_xlarge | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"Transducer",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"arxiv:2005.08100",
"license:cc-by-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-13T19:21:18+00:00 | [
"2005.08100"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #arxiv-2005.08100 #license-cc-by-4.0 #model-index #has_space #region-us
| NVIDIA Conformer-Transducer X-Large (en-US)
===========================================
img {
display: inline;
}
| 
| 
| 
This model transcribes speech in lower case English alphabet along with spaces and apostrophe... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #arxiv-2005.08100 #license-cc-by-4.0 #model-index #has_space #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a samp... |
text-generation | transformers |
# kant-gpt2-large
This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large). It was trained on the "Akademie Ausgabe" of the works of Immanuel Kant.
It achieves the following results on the evaluation set:
- Loss: 3.4257
## Model description
A large version of gpt2... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "kant-gpt2-large", "results": []}]} | Anjoe/kant-gpt2-large | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T19:29:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| kant-gpt2-large
===============
This model is a fine-tuned version of benjamin/gerpt2-large. It was trained on the "Akademie Ausgabe" of the works of Immanuel Kant.
It achieves the following results on the evaluation set:
* Loss: 3.4257
Model description
-----------------
A large version of gpt2
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n*... |
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
from stable_baselines3 import ...
from huggingface_sb3 ... | {"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... | abubakar/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T19:43:47+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... |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | AngelUrq/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-13T20:14:16+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"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884
## Validation Metrics
- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.... | {"language": "en", "tags": "autotrain", "datasets": ["Jerimee/autotrain-data-dontknowwhatImdoing"], "widget": [{"text": "Jerimee", "example_title": "a weird human name"}, {"text": "Curtastica", "example_title": "a goblin name"}, {"text": "Fatima", "example_title": "a common human name"}], "co2_eq_emissions": 0.01214739... | Jerimee/autotrain-dontknowwhatImdoing-980432459 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:Jerimee/autotrain-data-dontknowwhatImdoing",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-13T21:23:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Jerimee/autotrain-data-dontknowwhatImdoing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884
## Validation Metrics
- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 980432459\n- CO2 Emissions (in grams): 0.012147398577917884",
"## Validation Metrics\n\n- Loss: 0.0469294898211956\n- Accuracy: 0.9917355371900827\n- Precision: 0.9936708860759493\n- Recall: 0.9936708860759493\n- AUC: 0.9990958... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Jerimee/autotrain-data-dontknowwhatImdoing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 980432459\n- C... |
null | null | from gpt2_client import *
gpt2 = GPT2()
streamlit_code_base = gpt2.generate(
prompt="Enter prompt here",
temperature=0.7,
top_p=0.9,
nsamples=1,
batch_size=1,
length=1000,
include_prefix=True
)
print(streamlit_code_base) | {} | TeamHaltmannSusanaHWCEO/DALL-X-1.0A | null | [
"region:us"
] | null | 2022-06-13T21:48:18+00:00 | [] | [] | TAGS
#region-us
| from gpt2_client import *
gpt2 = GPT2()
streamlit_code_base = gpt2.generate(
prompt="Enter prompt here",
temperature=0.7,
top_p=0.9,
nsamples=1,
batch_size=1,
length=1000,
include_prefix=True
)
print(streamlit_code_base) | [] | [
"TAGS\n#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... | alefarasin/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-13T22:05:06+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/1509372264424296448/HVPI... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/honiemun | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-13T22:11:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
𝘏𝘰𝘯𝘪𝘦
@honiemun
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 |
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
## This model is used for french summarization
- Problem type: Summarization
- Model ID: 980832493
- CO2... | {"language": "fr", "datasets": ["Chemsseddine/autotrain-data-bertSummGpt2"], "widget": [{"text": "Your text here"}], "co2_eq_emissions": 0.10685501288084795} | Chemsseddine/bert2gpt2SUMM | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"fr",
"dataset:Chemsseddine/autotrain-data-bertSummGpt2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T23:34:06+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #fr #dataset-Chemsseddine/autotrain-data-bertSummGpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Map of positive probabilities per country." width="200"/>
## This model is used for french summarization
- Problem type: Summarization
- Model ID: 980832493
- CO2 Emissions (in grams): 0.10685501288084795
## Validation Metrics
- Loss: 4.03749418258667
- Rouge1: 28.8384
- Rouge2: 10.7511
- RougeL:... | [
"## This model is used for french summarization\n- Problem type: Summarization\n- Model ID: 980832493\n- CO2 Emissions (in grams): 0.10685501288084795",
"## Validation Metrics\n\n- Loss: 4.03749418258667\n- Rouge1: 28.8384\n- Rouge2: 10.7511\n- RougeL: 27.0842\n- RougeLsum: 27.5118\n- Gen Len: 22.0625",
"## Usa... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #fr #dataset-Chemsseddine/autotrain-data-bertSummGpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"## This model is used for french summarization\n- Problem type: Summarization\n- Model ID: 980832493\n- CO2 Emis... |
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. -->
# xlmroberta2xlmroberta-finetune-summarization-ar
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum d... | {"tags": ["summarization", "ar", "encoder-decoder", "xlm-roberta", "Abstractive Summarization", "roberta", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "xlmroberta2xlmroberta-finetune-summarization-ar", "results": []}]} | ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"xlm-roberta",
"Abstractive Summarization",
"roberta",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-14T02:10:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #xlm-roberta #Abstractive Summarization #roberta #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlmroberta2xlmroberta-finetune-summarization-ar
===============================================
This model is a fine-tuned version of [](URL on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1298
* Rouge-1: 21.69
* Rouge-2: 8.73
* Rouge-l: 19.52
* Gen Len: 19.96
* Bertscore: 7... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #xlm-roberta #Abstractive Summarization #roberta #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyper... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-mnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base/) on the GLUE MNLI da... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "roberta-base-mnli", "results": []}]} | JeremiahZ/roberta-base-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T02:10:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-mnli
This model is a fine-tuned version of roberta-base on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7539
- eval_accuracy: 0.8697
- eval_runtime: 25.5655
- eval_samples_per_second: 384.581
- eval_steps_per_second: 48.073
- step: 0
## Model descrip... | [
"# roberta-base-mnli\n\nThis model is a fine-tuned version of roberta-base on the GLUE MNLI dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7539\n- eval_accuracy: 0.8697\n- eval_runtime: 25.5655\n- eval_samples_per_second: 384.581\n- eval_steps_per_second: 48.073\n- step: 0",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-mnli\n\nThis model is a fine-tuned version of roberta-base on the GLUE MNLI dataset.\nIt achieves the following results on ... |
null | transformers |
Image Captioning in Portuguese trained with ViT and GPT2
[DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese)
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | {"language": ["pt"]} | jcrbsa/pt-gpt2vit | null | [
"transformers",
"pytorch",
"jax",
"vision-encoder-decoder",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T02:16:18+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #region-us
|
Image Captioning in Portuguese trained with ViT and GPT2
DEMO
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | [] | [
"TAGS\n#transformers #pytorch #jax #vision-encoder-decoder #pt #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. -->
# roberta-base-sst2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "typ... | JeremiahZ/roberta-base-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T02:41:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-sst2
=================
This model is a fine-tuned version of roberta-base on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2314
* Accuracy: 0.9358
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: 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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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": "7.40 +/... | AngelUrq/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T03:09:02+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... | brad/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-14T03:40:55+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. -->
# ff_analysis_4
This model is a fine-tuned version of [zdreiosis/ff_analysis_4](https://huggingface.co/zdreiosis/ff_analysis_4) on... | {"license": "apache-2.0", "tags": ["gen_ffa", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "ff_analysis_4", "results": []}]} | zdreiosis/ff_analysis_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"gen_ffa",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T04:02:34+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\_4
===============
This model is a fine-tuned version of zdreiosis/ff\_analysis\_4 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0022
* F1: 1.0
* Roc Auc: 1.0
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limi... | [
"### 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: 30",
"### Train... | [
"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\... |
question-answering | transformers |
# INT8 DistilBERT base cased finetuned on Squad
### Post-training static quantization
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp3... | {"license": "apache-2.0", "tags": ["int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["squad"], "metrics": ["f1"]} | Intel/distilbert-base-cased-distilled-squad-int8-static | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T04:06:54+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| INT8 DistilBERT base cased finetuned on Squad
=============================================
### Post-training static quantization
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model distilbert... | [
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model distilbert-base-cased-distilled-squad.\n\n\nThe calibration dataloader is the train dataloade... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the ... |
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="danielcfho/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"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": ... | danielcfho/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T04:56:10+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"
] |
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/1844491454/horse-js_400x... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/horse_js/1655186387828/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/horse_js | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T04:59:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Horse JS
@horse\_js
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 |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-tiny-patch4-window8-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-14T05:00:27+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did not... | [
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v2... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256... |
image-classification | transformers |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-tiny-patch4-window16-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T05:17:52+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did not... | [
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v2... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It wa... |
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-base-finetuned-fa
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the pn_... | {"license": "apache-2.0", "tags": ["summarization", "fa", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "mt5-base-finetuned-fa", "results": []}]} | ahmeddbahaa/mt5-base-finetuned-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"fa",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T05:28:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-fa
=====================
This model is a fine-tuned version of google/mt5-base on the pn\_summary dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6477
* Rouge-1: 33.7
* Rouge-2: 21.28
* Rouge-l: 31.69
* Gen Len: 19.0
* Bertscore: 74.52
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe followin... |
text-generation | transformers |
#HarringtonBot dialogue model | {"tags": ["conversational"]} | markofhope/DialoGPT-medium-HarringtonBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T05:52:31+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#HarringtonBot dialogue model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sarcasm-detection-RoBerta-base-POS
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-RoBerta-base-POS", "results": []}]} | jkhan447/sarcasm-detection-RoBerta-base-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T05:56:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-RoBerta-base-POS
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6651
- Accuracy: 0.607
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training ... | [
"# sarcasm-detection-RoBerta-base-POS\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: 0.6651\n- Accuracy: 0.607",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-RoBerta-base-POS\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following resu... |
fill-mask | 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. -->
# TEdetection_distiBERT_mLM_final
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_mLM_final", "results": []}]} | FritzOS/TEdetection_distilBERT_mLM_final | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T06:08:10+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distiBERT_mLM_final
This model is a fine-tuned version of distilbert-base-uncased 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
M... | [
"# TEdetection_distiBERT_mLM_final\n\nThis model is a fine-tuned version of distilbert-base-uncased 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 an... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distiBERT_mLM_final\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following resul... |
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. -->
# MIX2_en-ja_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MIX2_en-ja_helsinki", "results": []}]} | twieland/MIX2_en-ja_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T06:24:46+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MIX2\_en-ja\_helsinki
=====================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-jap on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6703
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96\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: 4\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #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: 0.0003\n* train\\_batch\\_size: 96... |
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-CR-POS
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-CR-POS", "results": []}]} | jkhan447/sarcasm-detection-RoBerta-base-CR-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:00:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-RoBerta-base-CR-POS
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6933
- Accuracy: 0.4977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# sarcasm-detection-RoBerta-base-CR-POS\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: 0.6933\n- Accuracy: 0.4977",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-RoBerta-base-CR-POS\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following r... |
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-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ft-keyword-spotting", "results": []}]} | sampras343/wav2vec2-base-ft-keyword-spotting | null | [
"transformers",
"pytorch",
"tensorboard",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:00:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-ft-keyword-spotting
=================================
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: 0.0824
* Accuracy: 0.9826
Model description
-----------------
More information needed
Intende... | [
"### 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: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #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\\_size: 32\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. -->
# mrpc
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "... | Alireza1044/mobilebert_mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:06:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mrpc
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3672
- Accuracy: 0.8382
- F1: 0.8889
- Combined Score: 0.8636
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# mrpc\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3672\n- Accuracy: 0.8382\n- F1: 0.8889\n- Combined Score: 0.8636",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# mrpc\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE MRPC dataset.\nI... |
token-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. -->
# TEdetection_distiBERT_NER_final
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_NER_final", "results": []}]} | FritzOS/TEdetection_distilBERT_NER_final | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:18:51+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TEdetection\_distiBERT\_NER\_final
==================================
This model is a fine-tuned version of FritzOS/TEdetection\_distiBERT\_mLM\_final on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0031
* Validation Loss: 0.0035
* Epoch: 0
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #token-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', 'learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "col... | Alireza1044/mobilebert_cola | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:21:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# cola
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6337
- Matthews Correlation: 0.5278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training... | [
"# cola\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE COLA dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6337\n- Matthews Correlation: 0.5278",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# cola\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE COLA dataset.\nI... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE COLA dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE... | JeremiahZ/roberta-base-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:32:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-cola
=================
This model is a fine-tuned version of roberta-base on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0571
* Matthews Correlation: 0.6232
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: 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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used ... |
text2text-generation | transformers |
# LongT5 (transient-global attention, XL-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com... | {"language": "en", "license": "apache-2.0"} | google/long-t5-tglobal-xl | null | [
"transformers",
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:32:52+00:00 | [
"2112.07916",
"1912.08777",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LongT5 (transient-global attention, XL-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found i... | [
"# LongT5 (transient-global attention, XL-sized model)\n\nLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be f... | [
"TAGS\n#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LongT5 (transient-global attention, XL-sized model)\n\nLongT5 model pre-trained on English language. The mod... |
token-classification | spacy |
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.3.1,<3.4.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | MIT |
| **Aut... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | dksari/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2022-06-14T07:34:43+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #region-us
|
### Label Scheme
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-rte
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE RTE datas... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "met... | JeremiahZ/roberta-base-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:36:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-rte
================
This model is a fine-tuned version of roberta-base on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5446
* Accuracy: 0.7978
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #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\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-stsb
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE STSB dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "roberta-base-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "args": "stsb"}, ... | JeremiahZ/roberta-base-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:44:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-stsb
=================
This model is a fine-tuned version of roberta-base on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4155
* Pearson: 0.9101
* Spearmanr: 0.9079
* Combined Score: 0.9090
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #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\\_rat... |
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. -->
# xlmroberta2xlmroberta-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_ling... | {"tags": ["summarization", "ar", "encoder-decoder", "roberta", "xlmroberta2xlmroberta", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "xlmroberta2xlmroberta-finetuned-ar-wikilingua", "results": []}]} | ahmeddbahaa/xlmroberta2xlmroberta-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"roberta",
"xlmroberta2xlmroberta",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T07:51:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #roberta #xlmroberta2xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
| xlmroberta2xlmroberta-finetuned-ar-wikilingua
=============================================
This model is a fine-tuned version of [](URL on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7757
* Rouge-1: 11.2
* Rouge-2: 1.96
* Rouge-l: 10.28
* Gen Len: 19.8
* Bertscore: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #roberta #xlmroberta2xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following ... |
null | null | Birthday_Party_4.jpg
license: afl-3.0
---
| {} | Me2444444/Gghg | null | [
"region:us"
] | null | 2022-06-14T08:03:51+00:00 | [] | [] | TAGS
#region-us
| Birthday_Party_4.jpg
license: afl-3.0
---
| [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-boz
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["boz"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-boz | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"boz",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T08:16:13+00:00 | [] | [
"boz"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #boz #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-boz
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - BOZ (Bozo,... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #boz #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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. -->
# mnli
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metric... | Alireza1044/mobilebert_mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T08:30:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mnli
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4595
- Accuracy: 0.8230
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# mnli\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE MNLI dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4595\n- Accuracy: 0.8230",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# mnli\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE MNLI dataset.\nI... |
null | null | SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of **SPIRAL Base**, trained with 960-hour LibriSpeech data
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
```
@inproceedings{huang2022sp... | {} | huawei-noah/SPIRAL-base | null | [
"region:us"
] | null | 2022-06-14T08:40:29+00:00 | [] | [] | TAGS
#region-us
| SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of SPIRAL Base, trained with 960-hour LibriSpeech data
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
| [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 982832610
- CO2 Emissions (in grams): 258.9123940027299
## Validation Metrics
- Loss: 1.2983888387680054
- Rouge1: 39.1872
- Rouge2: 21.6625
- RougeL: 34.2362
- RougeLsum: 34.23
- Gen Len: 52.762
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autotrain", "datasets": ["mshoaibsarwar/autotrain-data-pdm-news"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 258.9123940027299} | mshoaibsarwar/pegasus-pdm-news | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"unk",
"dataset:mshoaibsarwar/autotrain-data-pdm-news",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T08:44:08+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #unk #dataset-mshoaibsarwar/autotrain-data-pdm-news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 982832610
- CO2 Emissions (in grams): 258.9123940027299
## Validation Metrics
- Loss: 1.2983888387680054
- Rouge1: 39.1872
- Rouge2: 21.6625
- RougeL: 34.2362
- RougeLsum: 34.23
- Gen Len: 52.762
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 982832610\n- CO2 Emissions (in grams): 258.9123940027299",
"## Validation Metrics\n\n- Loss: 1.2983888387680054\n- Rouge1: 39.1872\n- Rouge2: 21.6625\n- RougeL: 34.2362\n- RougeLsum: 34.23\n- Gen Len: 52.762",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #unk #dataset-mshoaibsarwar/autotrain-data-pdm-news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 982832610\n- CO2 Emissions (in gr... |
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-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", ... | saiharsha/vit-base-beans | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T08:44:21+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1824
* Accuracy: 0.9699
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: 24\n* eval\\_batch\\_size: 24\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Tr... | [
"TAGS\n#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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="Rekcul/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | Rekcul/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T08:53:13+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"
] |
text-generation | transformers |
This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Mayakovsky's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in the style of Mayakovsky.
![alt text](https://lh4.googleusercontent.com/PCDVFfjgy-76wZtVQiWYwgB... | {} | AnyaSchen/rugpt3_mayakovskij | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T08:57:03+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Mayakovsky's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in the style of Mayakovsky.
!alt text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-qnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QNLI dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "typ... | JeremiahZ/roberta-base-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T09:03:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-qnli
=================
This model is a fine-tuned version of roberta-base on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2992
* Accuracy: 0.9246
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: 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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used ... |
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="Rekcul/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
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 +/... | Rekcul/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T09:10:20+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"
] |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squadshifts-new_wiki-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://gith... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-small-squadshifts-new_wiki-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T09:32:53+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squadshifts-new\_wiki-qg'
======================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
*... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\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 fi... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #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: lmqg/t5-small-squad\n* Languag... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squadshifts-nyt-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asa... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-small-squadshifts-nyt-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T09:34:12+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squadshifts-nyt-qg'
================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language: en
* Tr... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\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\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #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: lmqg/t5-small-squad\n* Languag... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squadshifts-reddit-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.c... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-small-squadshifts-reddit-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T09:36:37+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squadshifts-reddit-qg'
===================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\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... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #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: lmqg/t5-small-squad\n* Languag... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squadshifts-amazon-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.c... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-small-squadshifts-amazon-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T09:37:51+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squadshifts-amazon-qg'
===================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\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... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #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: lmqg/t5-small-squad\n* Languag... |
image-classification | keras |
## Model description
This repo contains the trained model Self-supervised contrastive learning with SimSiam on CIFAR-10 Dataset.
Keras link: https://keras.io/examples/vision/simsiam/
Full credits to https://twitter.com/RisingSayak
## Intended uses & limitations
The trained model can be used as a learned representat... | {"library_name": "keras", "tags": ["computer-vision", "image-classification"]} | keras-io/SimSiam | null | [
"keras",
"tensorboard",
"computer-vision",
"image-classification",
"has_space",
"region:us"
] | null | 2022-06-14T09:45:29+00:00 | [] | [] | TAGS
#keras #tensorboard #computer-vision #image-classification #has_space #region-us
| Model description
-----------------
This repo contains the trained model Self-supervised contrastive learning with SimSiam on CIFAR-10 Dataset.
Keras link: URL
Full credits to URL
Intended uses & limitations
---------------------------
The trained model can be used as a learned representation for downstream tas... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #computer-vision #image-classification #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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/1436804952119132162/47Me... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/iamekagra/1655206726797/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/iamekagra | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T10:32:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ekagra Sinha
@iamekagra
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/1494814887410909195/1_cZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/duckybhai/1655207092084/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/duckybhai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T10:43:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Saad Ur Rehman
@duckybhai
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-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": ... | olivia371/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T10:52:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 0.2348
- Accuracy: 0.925
- F1: 0.9254
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# 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: 0.2348\n- Accuracy: 0.925\n- F1: 0.9254",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
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-atuscol
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-atuscol", "results": []}]} | kpeyton/distilbert-base-uncased-finetuned-atuscol | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T11:03:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-atuscol
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6200
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-hbb
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["hbb"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-hbb | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"hbb",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T11:25:56+00:00 | [] | [
"hbb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #hbb #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-hbb
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - HBB (Nya H... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #hbb #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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. -->
# qqp
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the G... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "met... | Alireza1044/mobilebert_qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T11:25:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# qqp
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2458
- Accuracy: 0.8989
- F1: 0.8670
- Combined Score: 0.8829
## Model description
More information needed
## Intended uses & limitations
More informa... | [
"# qqp\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE QQP dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2458\n- Accuracy: 0.8989\n- F1: 0.8670\n- Combined Score: 0.8829",
"## Model description\n\nMore information needed",
"## Intended uses & limita... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# qqp\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE QQP dataset.\nIt ... |
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/1526278959746392069/t3sM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/imrankhanpti | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T11:28:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Imran Khan
@imrankhanpti
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 |
# wav2vec2-bloom-speech-bzi
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["bzi"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-bzi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"bzi",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T11:43:27+00:00 | [] | [
"bzi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #bzi #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-bzi
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/BLOOM-SPEECH - BZI (Bisu)... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #bzi #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
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