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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. -->
# bert-small-finetuned-wnut17-ner-longer10
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-wnut17-ner-longer... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-wnut17-ner-longer10", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wn... | muhtasham/bert-small-finetuned-wnut17-ner-longer10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:22:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-wnut17-ner-longer10
========================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-wnut17-ner-longer6 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4693
* Precision: 0.5547
* Recall: 0.4306
* F1: 0.4848... | [
"### 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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-test2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-test2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "ty... | Jethuestad/distilbert-base-uncased-test2 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:22:08+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-test2
=============================
This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3055
* Precision: 0.5278
* Recall: 0.3957
* F1: 0.4523
* Accuracy: 0.9462
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #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: ... |
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. -->
# ner-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "ner-classification", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_... | oyvindgrutle/ner-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:29:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| ner-classification
==================
This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2699
* Precision: 0.5422
* Recall: 0.3336
* F1: 0.4131
* Accuracy: 0.9439
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #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* lear... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-xglue-ner
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "t... | muhtasham/bert-small-finetuned-xglue-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:37:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-xglue-ner
==============================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3663
* Precision: 0.5932
* Recall: 0.3959
* F1: 0.4749
* Accuracy: 0.9252
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-xglue-ner-longer6
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner-longer6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut... | muhtasham/bert-small-finetuned-xglue-ner-longer6 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:45:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-xglue-ner-longer6
======================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4087
* Precision: 0.5620
* Recall: 0.4282
* F1: 0.4861
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-xglue-ner-longer10
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer6]... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner-longer10", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnu... | muhtasham/bert-small-finetuned-xglue-ner-longer10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:49:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-xglue-ner-longer10
=======================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer6 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4645
* Precision: 0.5437
* Recall: 0.4318
* F1: 0.4813
* ... | [
"### 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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-generation | transformers |
A [bloom-350m](https://huggingface.co/bigscience/bloom-350m) model trained from scratch on German data. | {"language": "de", "license": "mit", "pipeline_tag": "text-generation"} | malteos/bloom-350m-german | null | [
"transformers",
"pytorch",
"tensorboard",
"bloom",
"feature-extraction",
"text-generation",
"de",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T11:54:01+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #bloom #feature-extraction #text-generation #de #license-mit #endpoints_compatible #text-generation-inference #region-us
|
A bloom-350m model trained from scratch on German data. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bloom #feature-extraction #text-generation #de #license-mit #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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | mooface/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:55:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2173
* Accuracy: 0.927
* F1: 0.9271
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-xglue-ner-longer20
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer10... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner-longer20", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnu... | muhtasham/bert-small-finetuned-xglue-ner-longer20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:04:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-xglue-ner-longer20
=======================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer10 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5839
* Precision: 0.5783
* Recall: 0.4330
* F1: 0.4952
*... | [
"### 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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | Satoshi-ONOUE/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:08:13+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7720
* Accuracy: 0.9148
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: 48\n* eval\\_batch\\_size: 48\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 #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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:... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-xglue-ner-longer50
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer20... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner-longer50", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnu... | muhtasham/bert-small-finetuned-xglue-ner-longer50 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:11:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-xglue-ner-longer50
=======================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer20 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7236
* Precision: 0.6182
* Recall: 0.4222
* F1: 0.5018
*... | [
"### 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: 30",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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\\... |
null | null |
# Maverick <br>
Developed during my internship at [**Vela Partners**](https://vela.partners/). <br>
The paper presenting Maverick can be found on my [GitHub](https://github.com/lukasec/Maverick). <br>
Maverick consists of two sub-models published here on Hugging Face : [MAV-Moneyball](https://huggingface.co/lukasec/M... | {"language": ["en"]} | lukasec/Maverick | null | [
"en",
"region:us"
] | null | 2022-08-17T12:17:39+00:00 | [] | [
"en"
] | TAGS
#en #region-us
|
# Maverick <br>
Developed during my internship at Vela Partners. <br>
The paper presenting Maverick can be found on my GitHub. <br>
Maverick consists of two sub-models published here on Hugging Face : MAV-Moneyball & MAV-Midas.
Abstract <br>
Maverick is a LLM to guide Venture Capital investment in startups. Its ulti... | [
"# Maverick <br> \nDeveloped during my internship at Vela Partners. <br>\nThe paper presenting Maverick can be found on my GitHub. <br>\nMaverick consists of two sub-models published here on Hugging Face : MAV-Moneyball & MAV-Midas.\n\nAbstract <br>\nMaverick is a LLM to guide Venture Capital investment in startups... | [
"TAGS\n#en #region-us \n",
"# Maverick <br> \nDeveloped during my internship at Vela Partners. <br>\nThe paper presenting Maverick can be found on my GitHub. <br>\nMaverick consists of two sub-models published here on Hugging Face : MAV-Moneyball & MAV-Midas.\n\nAbstract <br>\nMaverick is a LLM to guide Venture C... |
text-classification | transformers | # Maverick-Moneyball
MAV-Moneyball is a submodel of [**Maverick**](https://huggingface.co/lukasec/Maverick) - please refer to its model card for further information.<br>
Developed in my internship at [**Vela Partners**](https://vela.partners/). <br>
The paper presenting Maverick can be found on my [GitHub](https://gith... | {"language": ["en"]} | lukasec/Maverick-Moneyball | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-17T12:21:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Maverick-Moneyball
MAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>
Developed in my internship at Vela Partners. <br>
The paper presenting Maverick can be found on my GitHub. <br>
| [
"# Maverick-Moneyball\nMAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>\nDeveloped in my internship at Vela Partners. <br>\nThe paper presenting Maverick can be found on my GitHub. <br>"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Maverick-Moneyball\nMAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>\nDeveloped in my internship at Vela Partners. <br>\nThe p... |
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. -->
# bert-small-finetuned-parsed20
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-parsed20", "results": []}]} | muhtasham/bert-small-finetuned-parsed20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:31:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-parsed20
=============================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1193
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128... |
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. -->
# bert-small-finetuned-finetuned-parsed-longer50
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-finetuned-parsed-longer50", "results": []}]} | muhtasham/bert-small-finetuned-parsed-longer50 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:39:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-finetuned-parsed-longer50
==============================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-parsed20 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9278
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\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: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dat259-wav2vec2-en
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_1_0"], "model-index": [{"name": "dat259-wav2vec2-en", "results": []}]} | Jethuestad/dat259-wav2vec2-en | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T12:43:40+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us
| dat259-wav2vec2-en
==================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice\_1\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5042
* Wer: 0.5793
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_ba... |
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. -->
# bert-small-finetuned-finetuned-parsed-longer100
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-finetuned-parsed-longer100", "results": []}]} | muhtasham/bert-small-finetuned-parsed-longer100 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:01:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-finetuned-parsed-longer100
===============================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-finetuned-parsed-longer50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6346
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\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: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128... |
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. -->
# bert-small-nan-labels-500
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/googl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-nan-labels-500", "results": []}]} | muhtasham/bert-small-nan-labels-500 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:03:13+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-nan-labels-500
=========================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.1664
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 500",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
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. -->
# article2KW_test1.2_barthez-orangesum-title_finetuned_for_summerization
This model is a fine-tuned version of [moussaKam/barthez-... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "article2KW_test1.2_barthez-orangesum-title_finetuned_for_summerization", "results": []}]} | bthomas/article2KW_test1.2_barthez-orangesum-title_finetuned_for_summerization | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:12:00+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test1.2\_barthez-orangesum-title\_finetuned\_for\_summerization
===========================================================================
This model is a fine-tuned version of moussaKam/barthez-orangesum-title on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.12... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #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: 5.6e-05\n* train\\_b... |
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="EvanMath/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | EvanMath/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-17T13:20:50+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"
] |
image-to-text | generic |
# Fork of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) for an `OCR` Inference endpoint.
This repository implements a `custom` task for `ocr-detection` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/trocr... | {"library_name": "generic", "tags": ["trocr", "image-to-text", "endpoints-template"]} | philschmid/trocr-base-printed | null | [
"generic",
"pytorch",
"vision-encoder-decoder",
"trocr",
"image-to-text",
"endpoints-template",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:24:24+00:00 | [] | [] | TAGS
#generic #pytorch #vision-encoder-decoder #trocr #image-to-text #endpoints-template #endpoints_compatible #region-us
|
# Fork of microsoft/trocr-base-printed for an 'OCR' Inference endpoint.
This repository implements a 'custom' task for 'ocr-detection' for Inference Endpoints. The code for the customized pipeline is in the URL.
To use deploy this model as an Inference Endpoint, you have to select 'Custom' as the task to use the 'U... | [
"# Fork of microsoft/trocr-base-printed for an 'OCR' Inference endpoint.\n\nThis repository implements a 'custom' task for 'ocr-detection' for Inference Endpoints. The code for the customized pipeline is in the URL.\n\nTo use deploy this model as an Inference Endpoint, you have to select 'Custom' as the task to us... | [
"TAGS\n#generic #pytorch #vision-encoder-decoder #trocr #image-to-text #endpoints-template #endpoints_compatible #region-us \n",
"# Fork of microsoft/trocr-base-printed for an 'OCR' Inference endpoint.\n\nThis repository implements a 'custom' task for 'ocr-detection' for Inference Endpoints. The code for the cus... |
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. -->
# roberta-base-IMDB_roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-IMDB_roberta", "results": []}]} | Billwzl/roberta-base-IMDB_roberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:27:12+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-IMDB\_roberta
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1897
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_norm300_aug9
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm300_aug9", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300_aug9 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T13:33:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm300\_aug9
=======================================================
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: 1.0639
* Mse: 4.2557
* Mae: 1.3660
* R2: 0.4773
* Accu... | [
"### 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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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... | butchland/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-17T13:55:16+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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | JiaDian/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T14:10:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3619
* Precision: 0.7737
* Recall: 0.7568
* F1: 0.7651
* Accuracy: 0.8876
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #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\... |
summarization | transformers |
# mT5 Persian Summary
This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including [BBC news](https://huggingface.co/datasets/csebuetnlp/xlsum) and [pn_summary](https://huggingface.co/datasets/pn_summary).
## Usage
```python
from... | {"language": "fa", "license": "mit", "tags": ["summarization"], "datasets": ["pn_summary", "csebuetnlp/xlsum"], "metrics": ["bleu", "rouge", "bertscore"], "widget": [{"text": "\u0628\u0647 \u06af\u0632\u0627\u0631\u0634 \u0634\u0627\u0646\u0627 \u0628\u0647 \u0646\u0642\u0644 \u0627\u0632 \u0634\u0631\u06a9\u062a \u067... | nafisehNik/mt5-persian-summary | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"fa",
"dataset:pn_summary",
"dataset:csebuetnlp/xlsum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T14:14:10+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #dataset-csebuetnlp/xlsum #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# mT5 Persian Summary
This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including BBC news and pn_summary.
## Usage
If you find this model useful, make a link to the huggingface model. | [
"# mT5 Persian Summary\n\nThis model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including BBC news and pn_summary.",
"## Usage\n\n\n\n\nIf you find this model useful, make a link to the huggingface model."
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #dataset-csebuetnlp/xlsum #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# mT5 Persian Summary\n\nThis model is fine-tuned to generate summaries based on... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dat259-wav2vec2-en2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_1_0"], "model-index": [{"name": "dat259-wav2vec2-en2", "results": []}]} | Jethuestad/dat259-wav2vec2-en2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T14:16:56+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us
| dat259-wav2vec2-en2
===================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice\_1\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4036
* Wer: 0.5090
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_ba... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.com/... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mappi... | research-backup/roberta-large-semeval2012-v2-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T14:27:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (data... | [
"# relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Que... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "co... | BojanSimoski/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T14:48:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5196
* Matthews Correlation: 0.5491
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | allennlp |
# TODO: Fill this model card
| {"language": "en", "tags": ["allennlp"]} | allenai/elmo_decomposable-attention_snli | null | [
"allennlp",
"en",
"region:us"
] | null | 2022-08-17T15:27:08+00:00 | [] | [
"en"
] | TAGS
#allennlp #en #region-us
|
# TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#allennlp #en #region-us \n",
"# TODO: Fill this model card"
] |
text-classification | transformers |
# BERT Regard classification model
This model is the result of a project entitled [Towards Controllable Biases in Language Generation](https://github.com/ewsheng/controllable-nlg-biases). It consists of a BERT classifier (no ensemble) trained on 1.7K samples of biased language.
*Regard* measures language polarity... | {"license": "cc-by-4.0"} | sasha/regardv3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-17T15:58:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT Regard classification model
This model is the result of a project entitled Towards Controllable Biases in Language Generation. It consists of a BERT classifier (no ensemble) trained on 1.7K samples of biased language.
*Regard* measures language polarity towards and social perceptions of a demographic (comp... | [
"# BERT Regard classification model \n\nThis model is the result of a project entitled Towards Controllable Biases in Language Generation. It consists of a BERT classifier (no ensemble) trained on 1.7K samples of biased language. \n\n*Regard* measures language polarity towards and social perceptions of a demograph... | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT Regard classification model \n\nThis model is the result of a project entitled Towards Controllable Biases in Language Generation. It consists of a BERT classi... |
text-classification | transformers | # Maverick-Midas
MAV-Moneyball is a submodel of [**Maverick**](https://huggingface.co/lukasec/Maverick) - please refer to its model card for further information.<br>
Developed in my internship at [**Vela Partners**](https://vela.partners/). <br>
The paper presenting Maverick can be found on my [GitHub](https://github.c... | {"language": ["en"]} | lukasec/Maverick-Midas | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T16:04:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
| # Maverick-Midas
MAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>
Developed in my internship at Vela Partners. <br>
The paper presenting Maverick can be found on my GitHub. <br> | [
"# Maverick-Midas\nMAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>\nDeveloped in my internship at Vela Partners. <br>\nThe paper presenting Maverick can be found on my GitHub. <br>"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Maverick-Midas\nMAV-Moneyball is a submodel of Maverick - please refer to its model card for further information.<br>\nDeveloped in my internship at Vela Partners. <br>\nThe paper presenting... |
text-generation | null |
# RWKV-4 430M
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 430M is a L24-D1024 causa... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["the_pile"]} | BlinkDL/rwkv-4-pile-430m | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"dataset:the_pile",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-17T16:42:56+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us
|
# RWKV-4 430M
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 430M is a L24-D1024 causa... | [
"# RWKV-4 430M",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"## Model Description\n\nRWKV-4 4... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us \n",
"# RWKV-4 430M",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | helgespieker/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-17T17:53:17+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# diffusion_cifar
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugging... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "CIFAR10", "metrics": []} | shalpin87/diffusion_cifar | null | [
"diffusers",
"en",
"dataset:CIFAR10",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-17T17:56:19+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-CIFAR10 #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# diffusion_cifar
## Model description
This diffusion model is trained with the Diffusers library
on the 'CIFAR10' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the da... | [
"# diffusion_cifar",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CIFAR10' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training dat... | [
"TAGS\n#diffusers #en #dataset-CIFAR10 #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# diffusion_cifar",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CIFAR10' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations ... |
question-answering | 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. -->
# skandaonsolve/roberta-finetuned-timeentities
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.c... | {"license": "cc-by-4.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "skandaonsolve/roberta-finetuned-timeentities", "results": []}]} | svo2/roberta-finetuned-timeentities | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T18:32:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us
| skandaonsolve/roberta-finetuned-timeentities
============================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0239
* Epoch: 9
Model description
-----------------
More inf... | [
"### 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': 3030, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #pytorch #tf #roberta #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'clas... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-lar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "train_model", "results": []}]} | hadiqa123/train_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T19:01:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| train\_model
============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0825
* Wer: 0.9077
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0001\n* train\\_batch\\_size: 8... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.com/... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mappi... | research-backup/roberta-large-semeval2012-v2-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T19:31:16+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (data... | [
"# relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Que... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.... |
text-classification | transformers | This is a fine-tuned version of [UniXcoder](https://huggingface.co/microsoft/unixcoder-base-nine), a unified cross-modal pre-trained model for programming languages, on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code. You can also find the code for... | {"language": "code", "license": "apache-2.0", "datasets": ["codeparrot/codecomplex"]} | codeparrot/unixcoder-java-complexity-prediction | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"code",
"dataset:codeparrot/codecomplex",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-17T19:48:16+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #roberta #text-classification #code #dataset-codeparrot/codecomplex #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is a fine-tuned version of UniXcoder, a unified cross-modal pre-trained model for programming languages, on CodeComplex, a dataset for complexity prediction of Java code. You can also find the code for the fine-tuning in this repository | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #code #dataset-codeparrot/codecomplex #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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", "config"... | mnarasim/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T19:56:00+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3140
- Accuracy: 0.88
- F1: 0.8816
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# 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: 0.3140\n- Accuracy: 0.88\n- F1: 0.8816",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
feature-extraction | transformers |
This is a copy of the original [BLOOM weights](https://huggingface.co/bigscience/bloom) that is more efficient to use with the [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). In this repo the original tensors are split into ... | {"license": "bigscience-bloom-rail-1.0"} | microsoft/bloom-deepspeed-inference-fp16 | null | [
"transformers",
"bloom",
"feature-extraction",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T20:01:05+00:00 | [] | [] | TAGS
#transformers #bloom #feature-extraction #license-bigscience-bloom-rail-1.0 #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a copy of the original BLOOM weights that is more efficient to use with the DeepSpeed-MII and DeepSpeed-Inference. In this repo the original tensors are split into 8 shards to target 8 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism.
For specific details about the BLOOM... | [] | [
"TAGS\n#transformers #bloom #feature-extraction #license-bigscience-bloom-rail-1.0 #endpoints_compatible #has_space #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/1556703446040215552/SMjM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/karemaki/1660772261096/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/karemaki | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T20:35:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Karemaki Chan
@karemaki
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. -->
# distilbert-base-uncased-finetuned-code-snippet-quality-scoring
This model is a fine-tuned version of [distilbert-base-uncased](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-code-snippet-quality-scoring", "results": []}]} | Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T20:52:10+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-code-snippet-quality-scoring
==============================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4070
* Accuracy: 0.8568
Model descript... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
null | null | # Projeto Final - Modelos Preditivos Conexionistas
### Victor Queiroga Crescêncio da Costa
|**Tipo de Projeto**|**Modelo Selecionado**|**Linguagem**|
|--|--|--|
|Classificação de Imagens|Modelo construído utilizando o keras|Tensorflow|
## Performance
O modelo treinado possui performance de **75%**.
### Output do b... | {} | vqcc/dogs_cats_birds | null | [
"region:us"
] | null | 2022-08-17T22:10:01+00:00 | [] | [] | TAGS
#region-us
| Projeto Final - Modelos Preditivos Conexionistas
================================================
### Victor Queiroga Crescêncio da Costa
Tipo de Projeto: Classificação de Imagens, Modelo Selecionado: Modelo construído utilizando o keras, Linguagem: Tensorflow
Performance
-----------
O modelo treinado possui pe... | [
"### Victor Queiroga Crescêncio da Costa\n\n\nTipo de Projeto: Classificação de Imagens, Modelo Selecionado: Modelo construído utilizando o keras, Linguagem: Tensorflow\n\n\nPerformance\n-----------\n\n\nO modelo treinado possui performance de 75%.",
"### Output do bloco de treinamento\n\n\n\nClick to expand!",
... | [
"TAGS\n#region-us \n",
"### Victor Queiroga Crescêncio da Costa\n\n\nTipo de Projeto: Classificação de Imagens, Modelo Selecionado: Modelo construído utilizando o keras, Linguagem: Tensorflow\n\n\nPerformance\n-----------\n\n\nO modelo treinado possui performance de 75%.",
"### Output do bloco de treinamento\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/1323879355672489984/z2nk... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/henrytcontreras/1660782293140/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/henrytcontreras | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T23:23:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Henry T. Contreras
@henrytcontreras
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 d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Spike Spiegel DialoGPT Model | {"tags": ["conversational"]} | yummyhat/DialoGPT-small-spike | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T23:55:46+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# Spike Spiegel DialoGPT Model | [
"# Spike Spiegel DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# Spike Spiegel DialoGPT Model"
] |
fill-mask | transformers |
# DictBERT model (uncased)
-- This is the model checkpoint of our [ACL 2022](https://www.2022.aclweb.org/) paper "*Dict-BERT: Enhancing Language Model Pre-training with Dictionary*" [\[PDF\]](https://aclanthology.org/2022.findings-acl.150/).
In this paper, we propose DictBERT, which is a novel pre-trained language m... | {"license": "cc-by-4.0"} | wyu1/DictBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T00:11:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| DictBERT model (uncased)
========================
-- This is the model checkpoint of our ACL 2022 paper "*Dict-BERT: Enhancing Language Model Pre-training with Dictionary*" [[PDF]](URL
In this paper, we propose DictBERT, which is a novel pre-trained language model by leveraging rare word definitions in English dictio... | [
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
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", "config"... | sudhab1988/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 | 2022-08-18T00:33:55+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: 0.3045
- Accuracy: 0.8867
- F1: 0.8882
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3045\n- Accuracy: 0.8867\n- F1: 0.8882",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"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... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.com/... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mappi... | research-backup/roberta-large-semeval2012-v2-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T00:33:59+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (data... | [
"# relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Que... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.... |
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/1557046163840180224/mKT0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/nazar1328 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T00:40:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
NAZAR #Nioh2
@nazar1328
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 |
<!-- 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. -->
# bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-t5-limitations
This model is a fine-tuned version of [google/mt5-small](htt... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-t5-limitations", "results": []}]} | bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-t5-limitations | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T00:50:43+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-t5-limitations
=========================================================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3041
* Validation... | [
"### 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': 5.6e-05, 'decay\\_steps': 15304, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #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* optimizer: {'name': 'Adam... |
text-generation | transformers | # Conversational Fluttershy bot made with Microsoft's GTP template.
| {"tags": ["conversational", "mylittlepony", "fluttershy"]} | EllyPony/flutterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"mylittlepony",
"fluttershy",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T01:59:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #mylittlepony #fluttershy #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Conversational Fluttershy bot made with Microsoft's GTP template.
| [
"# Conversational Fluttershy bot made with Microsoft's GTP template."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #mylittlepony #fluttershy #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Conversational Fluttershy bot made with Microsoft's GTP template."
] |
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. -->
# test-finetuned-ner
This model is a fine-tuned version of [hfl/chinese-pert-large](https://huggingface.co/hfl/chinese-pert-large)... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test-finetuned-ner", "results": []}]} | HYM/test-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T02:16:47+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| test-finetuned-ner
==================
This model is a fine-tuned version of hfl/chinese-pert-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1687
* Precision: 0.7449
* Recall: 0.7717
* F1: 0.7581
* Accuracy: 0.9546
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #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: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | wanko/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T02:32:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2183
* Accuracy: 0.9285
* F1: 0.9285
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
# T5 for Chinese Couplet(t5-chinese-couplet) Model
T5中文对联生成模型
`t5-chinese-couplet` evaluate couplet test data：
The overall performance of T5 on couplet **test**:
|prefix|input_text|target_text|pred|
|:-- |:--- |:--- |:-- |
|对联：|春回大地，对对黄莺鸣暖树|日照神州，群群紫燕衔新泥|福至人间,家家紫燕舞和风|
在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，而语义对仗工整和平... | {"language": ["zh"], "license": "apache-2.0", "tags": ["t5", "pytorch", "zh", "Text2Text-Generation"], "widget": [{"text": "\u5bf9\u8054\uff1a\u4e39\u67ab\u6c5f\u51b7\u4eba\u521d\u53bb"}]} | shibing624/t5-chinese-couplet | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"zh",
"Text2Text-Generation",
"arxiv:2110.06696",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T02:38:32+00:00 | [
"2110.06696"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #zh #Text2Text-Generation #arxiv-2110.06696 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5 for Chinese Couplet(t5-chinese-couplet) Model
================================================
T5中文对联生成模型
't5-chinese-couplet' evaluate couplet test data：
The overall performance of T5 on couplet test:
在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，而语义对仗工整和平仄合律还不满足。
T5的网络结构(原生T5)：
!arch
Usage
-----
本项目开源在文本生... | [
"### 训练数据集",
"#### 中文对联数据集\n\n\n* 数据：对联github、清洗过的对联github\n* 相关内容\n\t+ Huggingface\n\t+ LangZhou Chinese MengZi T5 pretrained Model and paper\n\t+ textgen\n\n\n数据格式：\n\n\n如果需要训练T5模型，请参考https://URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #zh #Text2Text-Generation #arxiv-2110.06696 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### 训练数据集",
"#### 中文对联数据集\n\n\n* 数据：对联github、清洗过的对联github\n* 相关内容\n\t+ Huggingfa... |
text-classification | transformers |
# fine-tune-bert-chinese-sent-analysis-movies-sample-new
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the 14190 samples of the [Douban Movies Short Comments](https://www.kaggle.com/datasets/utmhikari/doubanmovieshortcomments) from [Kaggle](https://www.kaggle.c... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fine-tune-bert-chinese-sent-analysis-movies-sample-new", "results": []}]} | Ayazhankad/fine-tune-bert-chinese-sent-analysis | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T03:41:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# fine-tune-bert-chinese-sent-analysis-movies-sample-new
This model is a fine-tuned version of bert-base-chinese on the 14190 samples of the Douban Movies Short Comments from Kaggle.
URL (Chinese: 豆瓣; pinyin: Dòubàn), launched on 6 March 2005, is a Chinese social networking service website that allows registered use... | [
"# fine-tune-bert-chinese-sent-analysis-movies-sample-new\n\nThis model is a fine-tuned version of bert-base-chinese on the 14190 samples of the Douban Movies Short Comments from Kaggle.\n\nURL (Chinese: 豆瓣; pinyin: Dòubàn), launched on 6 March 2005, is a Chinese social networking service website that allows regist... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# fine-tune-bert-chinese-sent-analysis-movies-sample-new\n\nThis model is a fine-tuned version of bert-base-chinese on the 14190 samples of the Douban Movies Short Comments... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tune-Wav2Vec2-XLS-R-300M-Indonesia
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "fine-tune-Wav2Vec2-XLS-R-300M-Indonesia", "results": []}]} | faraahahaha/fine-tune-Wav2Vec2-XLS-R-300M-Indonesia | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T03:49:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us
| fine-tune-Wav2Vec2-XLS-R-300M-Indonesia
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7924
* Wer: 0.4307
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001... |
text-generation | transformers |
This is a tiny random {mname_tiny} model to be used for basic testing
| {"tags": ["custom"], "inference": false} | ronvolutional/tiny-xlm-roberta-copy | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-generation",
"custom",
"autotrain_compatible",
"region:us"
] | null | 2022-08-18T04:34:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-generation #custom #autotrain_compatible #region-us
|
This is a tiny random {mname_tiny} model to be used for basic testing
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-generation #custom #autotrain_compatible #region-us \n"
] |
null | keras | # Question Difficulty Classification Model
## Introduction
This project aims to classify question answer pairs based on it's difficulty as easy,Medium or hard.You can pass a single question-answer pair seperated by comma or a list of question-answer pairs to the model.
I have fine tuned [bert-base-cased](https://hug... | {} | AbinJilson/bert-question-difficulty-classification-model | null | [
"keras",
"region:us"
] | null | 2022-08-18T04:41:06+00:00 | [] | [] | TAGS
#keras #region-us
| # Question Difficulty Classification Model
## Introduction
This project aims to classify question answer pairs based on it's difficulty as easy,Medium or hard.You can pass a single question-answer pair seperated by comma or a list of question-answer pairs to the model.
I have fine tuned bert-base-cased model with pr... | [
"# Question Difficulty Classification Model",
"## Introduction\nThis project aims to classify question answer pairs based on it's difficulty as easy,Medium or hard.You can pass a single question-answer pair seperated by comma or a list of question-answer pairs to the model.\nI have fine tuned bert-base-cased mode... | [
"TAGS\n#keras #region-us \n",
"# Question Difficulty Classification Model",
"## Introduction\nThis project aims to classify question answer pairs based on it's difficulty as easy,Medium or hard.You can pass a single question-answer pair seperated by comma or a list of question-answer pairs to the model.\nI have... |
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. -->
# my-awesome-model
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the No... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "my-awesome-model", "results": []}]} | namruto/my-awesome-model | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T04:41:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# my-awesome-model
This model is a fine-tuned version of vinai/phobert-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
"# my-awesome-model\n\nThis model is a fine-tuned version of vinai/phobert-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# my-awesome-model\n\nThis model is a fine-tuned version of vinai/phobert-base on the None dataset.",
"## Model description\n\nMore information needed",
... |
text-classification | transformers |
# Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "endpoints-template"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"]} | philschmid/distilbert-base-uncased-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"endpoints-template",
"en",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T04:56:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #emotion #endpoints-template #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Fork of bhadresh-savani/distilbert-base-uncased-emotion | [
"# Fork of bhadresh-savani/distilbert-base-uncased-emotion"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #emotion #endpoints-template #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fork of bhadresh-savani/distilbert-base-uncased-emotion"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.com/... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mappi... | research-backup/roberta-large-semeval2012-v2-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T05:38:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (data... | [
"# relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Que... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.... |
null | null | See <https://github.com/k2-fsa/icefall/pull/479>
| {} | Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18 | null | [
"tensorboard",
"region:us"
] | null | 2022-08-18T07:03:19+00:00 | [] | [] | TAGS
#tensorboard #region-us
| See <URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text-generation | transformers |
# Mengzi-GPT-neo model (Chinese)
Pretrained model on 300G Chinese corpus.
## Usage
```python
import torch
import sentencepiece as spm
from transformers import GPTNeoForCausalLM
tokenizer = spm.SentencePieceProcessor(model_file="mengzi_gpt.model")
model = GPTNeoForCausalLM.from_pretrained("Langboat/mengzi-gpt-neo-bas... | {"language": ["zh"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm"]} | Langboat/mengzi-gpt-neo-base | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"causal-lm",
"zh",
"doi:10.57967/hf/0022",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-18T07:08:30+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #zh #doi-10.57967/hf/0022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Mengzi-GPT-neo model (Chinese)
Pretrained model on 300G Chinese corpus.
## Usage
| [
"# Mengzi-GPT-neo model (Chinese)\nPretrained model on 300G Chinese corpus.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #zh #doi-10.57967/hf/0022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Mengzi-GPT-neo model (Chinese)\nPretrained model on 300G Chinese corpus.",
"## Usage"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | FusionLi/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-18T07:24:39+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
null | null | See https://github.com/k2-fsa/icefall/pull/536
| {} | teapoly/icefall-aishell-pruned-transducer-stateless2-2022-08-18 | null | [
"tensorboard",
"region:us"
] | null | 2022-08-18T07:34:52+00:00 | [] | [] | TAGS
#tensorboard #region-us
| See URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-juznevesti
This model for Serbian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 58 hours of audio and transcripts from [Južne vesti](https://www.juznevesti.com/), programme '15 minuta'.
For more info on the datas... | {"language": "sr", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["juznevesti-sr"], "widget": [{"example_title": "Croatian example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a"}, {"example_title": "Croatian example 2", "src": "https://huggingface.co/classla... | classla/wav2vec2-xls-r-juznevesti-sr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"sr",
"dataset:juznevesti-sr",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T07:38:24+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #sr #dataset-juznevesti-sr #endpoints_compatible #region-us
| wav2vec2-xls-r-juznevesti
=========================
This model for Serbian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 58 hours of audio and transcripts from Južne vesti, programme '15 minuta'.
For more info on the dataset creation see this repo.
Metrics
-------
Evaluation is ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #sr #dataset-juznevesti-sr #endpoints_compatible #region-us \n"
] |
summarization | 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. -->
# gbharathi80/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt... | {"language": ["es", "en"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["amazon_reviews_multi"], "metrics": ["bleu", "rouge"], "pipeline_tag": "summarization", "model-index": [{"name": "gbharathi80/mt5-small-finetuned-amazon-en-es", "results": []}]} | gbharathi80/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"summarization",
"es",
"en",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T07:55:47+00:00 | [] | [
"es",
"en"
] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #summarization #es #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gbharathi80/mt5-small-finetuned-amazon-en-es
============================================
This model is a fine-tuned version of google/mt5-small on an amazon reviews dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.2325
* Validation Loss: 3.4452
* Epoch: 7
Model description
------... | [
"### 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': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #summarization #es #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were u... |
text-to-speech | null |
## SpeechT5 TTS Manifest
| [**Github**](https://github.com/microsoft/SpeechT5) | [**Huggingface**](https://huggingface.co/mechanicalsea/speecht5-tts) |
This manifest is an attempt to recreate the Text-to-Speech recipe used for training [SpeechT5](https://aclanthology.org/2022.acl-long.393). This manifest was constru... | {"license": "mit", "tags": ["speech", "text", "cross-modal", "unified model", "self-supervised learning", "SpeechT5", "Text-to-Speech"], "datasets": ["LibriTTS"], "pipeline_tag": "text-to-speech"} | mechanicalsea/speecht5-tts | null | [
"speech",
"text",
"cross-modal",
"unified model",
"self-supervised learning",
"SpeechT5",
"Text-to-Speech",
"text-to-speech",
"dataset:LibriTTS",
"license:mit",
"has_space",
"region:us"
] | null | 2022-08-18T08:05:43+00:00 | [] | [] | TAGS
#speech #text #cross-modal #unified model #self-supervised learning #SpeechT5 #Text-to-Speech #text-to-speech #dataset-LibriTTS #license-mit #has_space #region-us
|
## SpeechT5 TTS Manifest
| Github | Huggingface |
This manifest is an attempt to recreate the Text-to-Speech recipe used for training SpeechT5. This manifest was constructed using LibriTTS clean datasets, including train-clean-100 and train-clean-360 for training, dev-clean for validation, and test-clean for evaluat... | [
"## SpeechT5 TTS Manifest\n\n| Github | Huggingface |\n\nThis manifest is an attempt to recreate the Text-to-Speech recipe used for training SpeechT5. This manifest was constructed using LibriTTS clean datasets, including train-clean-100 and train-clean-360 for training, dev-clean for validation, and test-clean for... | [
"TAGS\n#speech #text #cross-modal #unified model #self-supervised learning #SpeechT5 #Text-to-Speech #text-to-speech #dataset-LibriTTS #license-mit #has_space #region-us \n",
"## SpeechT5 TTS Manifest\n\n| Github | Huggingface |\n\nThis manifest is an attempt to recreate the Text-to-Speech recipe used for trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# training_45k
This model is a fine-tuned version of [Sameen53/cv_bn_bestModel_1](https://huggingface.co/Sameen53/cv_bn_bestModel_... | {"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "training_45k", "results": []}]} | Sameen53/training_45k | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T08:13:50+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| training\_45k
=============
This model is a fine-tuned version of Sameen53/cv\_bn\_bestModel\_1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: inf
* Wer: 0.1497
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* see... |
null | null |
This repo contains the model weights for the project described at https://github.com/lovhag/measure-visual-commonsense-knowledge.
| {"license": "mit"} | Lo/measure-visual-commonsense-knowledge-model-weights | null | [
"license:mit",
"region:us"
] | null | 2022-08-18T08:36:48+00:00 | [] | [] | TAGS
#license-mit #region-us
|
This repo contains the model weights for the project described at URL
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-generation | transformers | # BLOOM, a version for Petals
This model is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom)
post-processed to be run at home using the [Petals](https://github.com/bigscience-workshop/petals#readme) swarm.
Please check out:
- The [original model card](https://huggingface.co/bigscience/bloom)
... | {} | bigscience/bloom-petals | null | [
"transformers",
"pytorch",
"bloom",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T08:59:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # BLOOM, a version for Petals
This model is a version of bigscience/bloom
post-processed to be run at home using the Petals swarm.
Please check out:
- The original model card
to learn about the model's capabilities, specifications, and terms of use.
- The Petals repository
to learn how to install Petals and run ... | [
"# BLOOM, a version for Petals\n\nThis model is a version of bigscience/bloom\npost-processed to be run at home using the Petals swarm.\n\nPlease check out:\n\n- The original model card\n to learn about the model's capabilities, specifications, and terms of use.\n- The Petals repository\n to learn how to install ... | [
"TAGS\n#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# BLOOM, a version for Petals\n\nThis model is a version of bigscience/bloom\npost-processed to be run at home using the Petals swarm.\n\nPlease check out:\n\n- ... |
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. -->
# chinese-pert-large-finetuned-med-zh
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-pert-large-finetuned-med-zh", "results": []}]} | HYM/chinese-pert-large-finetuned-med-zh | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T09:12:38+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| chinese-pert-large-finetuned-med-zh
===================================
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4370
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #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: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_b... |
automatic-speech-recognition | transformers | # AMI - Wav2Vec2-Large-LV60
Trained on < 2 epochs, see [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh).
Results:
**Validation**:
```
{
"eval_loss": 0.599422812461853,
"eval_runtime": 197.9157,
"eval_samples": 12383,
"eval_samples_per_second": 62.567,
"eva... | {} | patrickvonplaten/ami-wav2vec2-large-lv60 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T09:14:32+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| # AMI - Wav2Vec2-Large-LV60
Trained on < 2 epochs, see URL.
Results:
Validation:
Eval:
| [
"# AMI - Wav2Vec2-Large-LV60\n\nTrained on < 2 epochs, see URL.\n\nResults:\nValidation:\n\n\nEval:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# AMI - Wav2Vec2-Large-LV60\n\nTrained on < 2 epochs, see URL.\n\nResults:\nValidation:\n\n\nEval:"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-test2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-test2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "ty... | oyvindgrutle/distilbert-base-uncased-test2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T09:28:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-test2
=============================
This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2937
* Precision: 0.5410
* Recall: 0.3976
* F1: 0.4583
* Accuracy: 0.9469
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #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* lear... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-v01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": ... | Alex-yang/Reinforce-v01 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-18T09:31:19+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text2text-generation | transformers |
# Mt5-large for Few-shot Czech+English Generative Question Answering
This is the [mt5-large](https://huggingface.co/google/mt5-large) model with an LM head for a generation of extractive answers,
given a small set of 2-5 demonstrations (i.e. primes).
## Few-shot (i.e. priming)
Note that **this is primarily a few-s... | {"language": ["multilingual", "cs", "en"], "tags": ["generation"], "widget": [{"text": "Ot\u00e1zka: Jak\u00fd je d\u016fvod dotazu z\u00e1kazn\u00edka?\nKontext: Dobr\u00fd den, \u017d\u00e1d\u00e1me zasl\u00e1n\u00ed nov\u00e9 smlouvy kv\u016fli \u0159e\u0161en\u00ed pojistn\u00e9 ud\u00e1losti. Za\u0161lete na tento... | gaussalgo/mt5-large-priming-QA_en-cs | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generation",
"multilingual",
"cs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T09:39:35+00:00 | [] | [
"multilingual",
"cs",
"en"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mt5-large for Few-shot Czech+English Generative Question Answering
This is the mt5-large model with an LM head for a generation of extractive answers,
given a small set of 2-5 demonstrations (i.e. primes).
## Few-shot (i.e. priming)
Note that this is primarily a few-shot model that expects a set of demonstration... | [
"# Mt5-large for Few-shot Czech+English Generative Question Answering\n\nThis is the mt5-large model with an LM head for a generation of extractive answers, \ngiven a small set of 2-5 demonstrations (i.e. primes).",
"## Few-shot (i.e. priming)\n\nNote that this is primarily a few-shot model that expects a set of ... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mt5-large for Few-shot Czech+English Generative Question Answering\n\nThis is the mt5-large model with an LM head for a ... |
text2text-generation | transformers |
# T5-small-nl16 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The H... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-small-nl16-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"finnish",
"t5x",
"seq2seq",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-08-18T09:51:43+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| T5-small-nl16 for Finnish
=========================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fin... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\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... | jackoyoungblood/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-18T10:26:13+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... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.com/... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mappi... | research-backup/roberta-large-semeval2012-v2-mask-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T10:42:44+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (data... | [
"# relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Que... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dat259-wav2vec2-en2-copy
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_1_0"], "model-index": [{"name": "dat259-wav2vec2-en2-copy", "results": []}]} | Jethuestad/dat259-wav2vec2-en2-copy | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T11:00:17+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us
| dat259-wav2vec2-en2-copy
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice\_1\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5429
* Wer: 0.5569
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_ba... |
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-id-en-finetuned-id-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-id-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["id_panl_bppt"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-id-en-finetuned-id-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "id_panl_bppt", "type... | PontifexMaximus/opus-mt-id-en-finetuned-id-to-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:id_panl_bppt",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-18T11:01:39+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-id_panl_bppt #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-id-en-finetuned-id-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-id-en on the id\_panl\_bppt dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6469
* Bleu: 30.557
* Gen Len: 29.8247
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\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\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-id_panl_bppt #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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. -->
# distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10
This model is a fine-tuned version of [distilbert-base-uncased](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T11:18:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm300\_aug5\_10
===========================================================
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: 1.0781
* Mse: 4.3123
* Mae: 1.3743
* R2: 0.470... | [
"### 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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# test-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args"... | jimypbr/test-ner | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T11:18:29+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# test-ner
This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# test-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0398\n- Precision: 0.9468\n- Recall: 0.9579\n- F1: 0.9523\n- Accuracy: 0.9921",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves ... |
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/1469851548922564612/oOe9... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/timgill924/1660825845162/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/timgill924 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T11:30:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tim Gill
@timgill924
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"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-IMDB_disbert1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-IMDB_disbert1", "results": []}]} | Billwzl/distilbert-base-uncased-IMDB_disbert1 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T11:37:46+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-IMDB\_disbert1
======================================
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: 2.0461
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: 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: 12",
"### Trainin... | [
"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: 5e-05\n* train\\_batch\\_size: 4\n* eval\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-New_data_bert1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-New_data_bert1", "results": []}]} | Billwzl/bert-base-uncased-New_data_bert1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T12:08:59+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-New\_data\_bert1
==================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9215
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 16",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batc... |
text-generation | 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. -->
# distilgpt2-finetuned-eap
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt2-finetuned-eap", "results": []}]} | ndemoes/distilgpt2-finetuned-eap | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T12:14:39+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-eap
========================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3557
* Train Accuracy: 0.0010
* Validation Loss: 7.4820
* Validation Accuracy: 0.0
* Epoch: 49
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 0.0002, '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 #gpt2 #text-generation #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeig... |
object-detection | transformers | # YOLOS (small-sized) model
This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the [licesne-plate-recognition](https://app.roboflow.com/objectdetection-jhgr1/license-plates-recognition/2) dataset from Roboflow which contains 5200 images in the training set and 380 i... | {"language": ["en"], "tags": ["object-detection", "license-plate-detection", "vehicle-detection"], "metrics": ["average precision", "recall", "IOU"], "widget": [{"src": "https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ", "example_title": "Skoda 1"}, {"src": "https://drive.google.com/uc?id=1p9wJIqRz3W50e... | nickmuchi/yolos-small-finetuned-license-plate-detection | null | [
"transformers",
"pytorch",
"yolos",
"object-detection",
"license-plate-detection",
"vehicle-detection",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-18T12:21:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #yolos #object-detection #license-plate-detection #vehicle-detection #en #endpoints_compatible #has_space #region-us
| YOLOS (small-sized) model
=========================
This model is a fine-tuned version of hustvl/yolos-small on the licesne-plate-recognition dataset from Roboflow which contains 5200 images in the training set and 380 in the validation set.
The original YOLOS model was fine-tuned on COCO 2017 object detection (118k ... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe YOLOS model was pre-trained on ImageNet-1k and fine-tuned on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/... | [
"TAGS\n#transformers #pytorch #yolos #object-detection #license-plate-detection #vehicle-detection #en #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nT... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-53-torgo-origin-parameters-checkpoint3750
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-53-torgo-origin-parameters-checkpoint3750", "results": []}]} | ying-tina/wav2vec2-xlsr-53-torgo-origin-parameters-checkpoint3750 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T12:28:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-53-torgo-origin-parameters-checkpoint3750
=======================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the Torgo dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2104
* Cer: 0.3903
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0001\n* train\\_batch\\_size: 4... |
null | null | a stylegan2ada network checkpoint trained on synthetic 256x256 images of curated generated icons.
more information here : [https://github.com/CorvaeOboro/gen_ability_icon](https://github.com/CorvaeOboro/gen_ability_icon).
![00_icon_gen_20220407_comp](https://raw.githubusercontent.com/CorvaeOboro/gen_ability_icon/mas... | {"license": "cc0-1.0"} | CorvaeOboro/gen_ability_icon | null | [
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-08-18T12:35:35+00:00 | [] | [] | TAGS
#license-cc0-1.0 #has_space #region-us
| a stylegan2ada network checkpoint trained on synthetic 256x256 images of curated generated icons.
more information here : URL
!00_icon_gen_20220407_comp
| [] | [
"TAGS\n#license-cc0-1.0 #has_space #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/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": ... | pimnara/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-18T12:39:08+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/1517549694762860552/6CPh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pseud0anon/1660830250717/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pseud0anon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T12:40:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Pseudo
@pseud0anon
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 |
<!-- 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. -->
# lol
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the followi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lol", "results": []}]} | mphamsioo/lol | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T12:46:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| lol
===
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6366
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evalua... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 100\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #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\\_rate... |
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/1479100020968374272/C_Z4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/n8jonesy/1660830925841/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/n8jonesy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T12:53:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
N🜁🝨🝗
@n8jonesy
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 |
# Michael Scott (The Office US) DialoGPT Model | {"tags": ["conversational"]} | Suryansh-23/DialoGPT-small-MichaelScottOffice | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-18T13:28:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Michael Scott (The Office US) DialoGPT Model | [
"# Michael Scott (The Office US) DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott (The Office US) DialoGPT Model"
] |
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. -->
# article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization
This model is a fine-tuned version of [moussaKam/barthez-... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization", "results": []}]} | bthomas/article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T14:19:42+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test1.3\_barthez-orangesum-title\_finetuned\_for\_summerization
===========================================================================
This model is a fine-tuned version of moussaKam/barthez-orangesum-title on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.12... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #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: 5.6e-05\n* train\\_b... |
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. -->
# tiny-bert-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "tiny-bert-sst2-distilled", "results": []}]} | ibrahim2030/tiny-bert-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T15:07:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# tiny-bert-sst2-distilled
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
"# tiny-bert-sst2-distilled\n\nThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tiny-bert-sst2-distilled\n\nThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.",
... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://github.c... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_ma... | research-backup/roberta-large-semeval2012-v2-average-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T15:46:29+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (d... | [
"# relbert/roberta-large-semeval2012-v2-average-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_... |
feature-extraction | transformers | This is random-wav2vec2-base, an unpretrained version of wav2vec 2.0. The weight of this model is randomly initialized, and can be used for establishing randomized baselines or training a model from scratch. The code used to do so is adapted from: https://huggingface.co/saibo/random-roberta-base. | {} | tinglxn/random-wav2vec2-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-08-18T15:55:00+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us
| This is random-wav2vec2-base, an unpretrained version of wav2vec 2.0. The weight of this model is randomly initialized, and can be used for establishing randomized baselines or training a model from scratch. The code used to do so is adapted from: URL | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us \n"
] |
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