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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers |
This is a model for named entity recognition of Japanese medical documents.
### How to use
Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- NER_medNLP.py
- predict.py
- text.txt (This is an input file which should be predicted, which could be changed.)
You can use t... | {"language": ["ja"], "license": ["cc-by-4.0"], "tags": ["NER", "medical documents"], "datasets": ["MedTxt-CR-JA-training-v2.xml"], "metrics": ["NTCIR-16 Real-MedNLP subtask 1"]} | sociocom/MedNER-CR-JA | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"NER",
"medical documents",
"ja",
"dataset:MedTxt-CR-JA-training-v2.xml",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T02:30:43+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a model for named entity recognition of Japanese medical documents.
### How to use
Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- NER_medNLP.py
- URL
- URL (This is an input file which should be predicted, which could be changed.)
You can use this model by... | [
"### How to use\n\nDownload the following five files and put into the same folder.\n- id_to_tags.pkl\n- key_attr.pkl\n- NER_medNLP.py\n- URL\n- URL (This is an input file which should be predicted, which could be changed.)\n\nYou can use this model by running URL.",
"### Input Example",
"### Output Example",
... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\nDownload the following five files and put into the same folder.\n- id_to_tags.p... |
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. -->
# English_Grammar_Checker
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "English_Grammar_Checker", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "cola", ... | abdulmatinomotoso/English_Grammar_Checker | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-23T02:43:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| English\_Grammar\_Checker
=========================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1117
* Matthews Correlation: 0.5324
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #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* lea... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model descrip... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds", "results": []}]} | VanHoan/codeparrot-ds | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-23T03:20:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codeparrot-ds
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hype... | [
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyper... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore info... |
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-chunking
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-chunking", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type... | ish97/bert-finetuned-chunking | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T03:55:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-chunking
=======================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1594
* Precision: 0.9230
* Recall: 0.9218
* F1: 0.9224
* Accuracy: 0.9619
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | tylersfoot/DialoGPT-medium-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-23T03:57:25+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
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. -->
# electramed-small-ADE-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/g... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-ADE-ner", "results": []}]} | chintagunta85/electramed-small-ADE-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T04:40:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-ADE-ner
========================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1548
* Precision: 0.8358
* Recall: 0.9064
* F1: 0.8697
* Accuracy: 0.9581
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eva... |
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. -->
# electramed-small-NCBI-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-NCBI-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_disease"... | chintagunta85/electramed-small-NCBI-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T05:28:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-NCBI-ner
=========================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0664
* Precision: 0.8083
* Recall: 0.8875
* F1: 0.8461
* Accuracy: 0.9821
Model descri... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1300449813
- CO2 Emissions (in grams): 0.0120
## Validation Metrics
- Loss: 1.004
- Accuracy: 0.710
- Precision: 0.542
- Recall: 0.413
- F1: 0.468
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Be... | {"language": ["en"], "tags": ["autotrain", "token-classification"], "datasets": ["nguyenkhoa2407/autotrain-data-default_model_favsbot_data"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.012034916031396342}} | nguyenkhoa2407/autotrain-bert-NER-favsbot | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:nguyenkhoa2407/autotrain-data-default_model_favsbot_data",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T05:35:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-nguyenkhoa2407/autotrain-data-default_model_favsbot_data #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1300449813
- CO2 Emissions (in grams): 0.0120
## Validation Metrics
- Loss: 1.004
- Accuracy: 0.710
- Precision: 0.542
- Recall: 0.413
- F1: 0.468
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1300449813\n- CO2 Emissions (in grams): 0.0120",
"## Validation Metrics\n\n- Loss: 1.004\n- Accuracy: 0.710\n- Precision: 0.542\n- Recall: 0.413\n- F1: 0.468",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-nguyenkhoa2407/autotrain-data-default_model_favsbot_data #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1300449813\n- ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-wnut17
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner-wnut17", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type":... | ish97/bert-finetuned-ner-wnut17 | 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 | 2022-08-23T05:59:50+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-finetuned-ner-wnut17
=========================
This model is a fine-tuned version of bert-base-cased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3444
* Precision: 0.5301
* Recall: 0.4844
* F1: 0.5062
* Accuracy: 0.9253
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-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\\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-ar-to-en
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base-finetuned-ar-to-en", "results": []}]} | Shamus/mt5-base-finetuned-ar-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-23T06:28:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-ar-to-en
===========================
This model is a fine-tuned version of google/mt5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Bleu: 0.0111
* Gen Len: 6.732
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# scibert-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/sc... | {"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "The widespread circular form of DNA molecules inside cells creates very serious topological problems during replication. Due to the helical structure of the double helix the parental s... | siddharthtumre/scibert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:jnlpba",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T06:28:51+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us
| scibert-finetuned-ner
=====================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the jnlpba dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4717
* Precision: 0.6737
* Recall: 0.7757
* F1: 0.7211
* Accuracy: 0.9226
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
image-to-text | transformers | Flamingo Model (tiny version) pretrained on Image Captioning on the Conceptual Captions (3M) dataset.
Source Code: https://github.com/dhansmair/flamingo-mini
Demo Space: https://huggingface.co/spaces/dhansmair/flamingo-tiny-cap
Flamingo-mini: https://huggingface.co/spaces/dhansmair/flamingo-mini-cap
| {"language": ["en"], "license": "apache-2.0", "tags": ["image-to-text", "image-captioning"], "datasets": ["conceptual_captions"]} | dhansmair/flamingo-tiny | null | [
"transformers",
"pytorch",
"image-to-text",
"image-captioning",
"en",
"dataset:conceptual_captions",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-23T06:58:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #image-to-text #image-captioning #en #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Flamingo Model (tiny version) pretrained on Image Captioning on the Conceptual Captions (3M) dataset.
Source Code: URL
Demo Space: URL
Flamingo-mini: URL
| [] | [
"TAGS\n#transformers #pytorch #image-to-text #image-captioning #en #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers | LayoutLMv1 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained using seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|-----... | {} | Sennodipoi/LayoutLMv1-FUNSD-ft | null | [
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:10:54+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlm #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv1 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained using seqeval in strict mode:
| [] | [
"TAGS\n#transformers #pytorch #layoutlm #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/swin | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-08-23T07:10:57+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## Swin Transformer model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the Swin Transformer model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Habana's custom Adam... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## Swin Transformer model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the Swin Transformer model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to spec... |
token-classification | transformers | LayoutLMv2 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained with seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|------... | {} | Sennodipoi/LayoutLMv2-FUNSD-ft | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:12:35+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv2 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained with seqeval in strict mode:
| [] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | LayoutLMv3 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained using seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|-----... | {} | Sennodipoi/LayoutLMv3-FUNSD-ft | null | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:14:07+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv3 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv3 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained using seqeval in strict mode:
| [] | [
"TAGS\n#transformers #pytorch #layoutlmv3 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Distilled version of the [RoBERTa](https://huggingface.co/textattack/roberta-base-SST-2) model fine-tuned on the SST-2 part of the GLUE dataset. It was obtained from the "teacher" RoBERTa model by using task-specific knowledge distillation. Since the teacher was fine-tuned on the SST-2, the final model as well is ready... | {} | azizbarank/distilroberta-base-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:27:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Distilled version of the RoBERTa model fine-tuned on the SST-2 part of the GLUE dataset. It was obtained from the "teacher" RoBERTa model by using task-specific knowledge distillation. Since the teacher was fine-tuned on the SST-2, the final model as well is ready to be used in sentiment analysis tasks.
Modifications... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-to-text | transformers | Flamingo Model pretrained on Image Captioning on the Conceptual Captions (3M) dataset.
Source Code: https://github.com/dhansmair/flamingo-mini
Demo Space: https://huggingface.co/spaces/dhansmair/flamingo-mini-cap
Flamingo-tiny: https://huggingface.co/spaces/dhansmair/flamingo-tiny-cap
| {"language": ["en"], "license": "apache-2.0", "tags": ["image-to-text", "image-captioning"], "datasets": ["conceptual_captions"]} | dhansmair/flamingo-mini | null | [
"transformers",
"pytorch",
"image-to-text",
"image-captioning",
"en",
"dataset:conceptual_captions",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-23T07:30:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #image-to-text #image-captioning #en #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Flamingo Model pretrained on Image Captioning on the Conceptual Captions (3M) dataset.
Source Code: URL
Demo Space: URL
Flamingo-tiny: URL
| [] | [
"TAGS\n#transformers #pytorch #image-to-text #image-captioning #en #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | morganchen1007/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1507
* Accuracy: 0.9342
Model description
------... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
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_murad
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2_murad", "results": []}]} | MBMMurad/wav2vec2_murad | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:43:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2_murad
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 288... | [
"# wav2vec2_murad\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2006\n- eval_wer: 0.2084\n- eval_runtime: 556.4634\n- eval_samples_per_second: 8.985\n- eval_steps_per_second: 0.562\n- epoch: 12.32\... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2_murad\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following... |
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. -->
# BART_corrector_15
This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_corrector_15", "results": []}]} | qBob/BART_corrector_15 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:50:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART\_corrector\_15
===================
This model is a fine-tuned version of ainize/bart-base-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0214
* Rouge1: 80.3263
* Rouge2: 78.1274
* Rougel: 80.3215
* Rougelsum: 80.3039
* Gen Len: 19.3993
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | guoguo/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T07:50:41+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sdg
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-finetuned-sdg", "results": []}]} | jonas/roberta-base-finetuned-sdg | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T08:11:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-sdg
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4993
* Acc: 0.9024
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eva... |
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-2000steps", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"t... | kws/Reinforce-2000steps | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-23T08:19:27+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... |
reinforcement-learning | ml-agents |
# **sac** Agent playing **PushBlock**
This is a trained model of a **sac** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comp... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock"]} | danieladejumo/MLAgents-PushBlock | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-PushBlock",
"region:us"
] | null | 2022-08-23T08:29:39+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us
|
# sac Agent playing PushBlock
This is a trained model of a sac agent playing PushBlock using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the train... | [
"# sac Agent playing PushBlock\n This is a trained model of a sac agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us \n",
"# sac Agent playing PushBlock\n This is a trained model of a sac agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Docu... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | adelgalu/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T08:31:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5602
* Wer: 0.3438
Model description
-----------------
More information needed
Intend... | [
"### 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... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | kws/Reinforce-Pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-23T08:43:11+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of ... |
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": []} | sycee/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-23T08:49:53+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 | This model used hfl/chinese-roberta-wwm-ext-large backbone and was trained on SNLI, MNLI, DNLI, KvPI, OCNLI, CMNLI data in Chinese version.
Model structures are as follows:
```python
class RobertaForSequenceClassification(nn.Module):
def __init__(self, tagset_size):
super(RobertaForSequenceClassification, ... | {} | Jaren/chinese-roberta-wwm-ext-large-NLI | null | [
"region:us"
] | null | 2022-08-23T08:51:27+00:00 | [] | [] | TAGS
#region-us
| This model used hfl/chinese-roberta-wwm-ext-large backbone and was trained on SNLI, MNLI, DNLI, KvPI, OCNLI, CMNLI data in Chinese version.
Model structures are as follows:
| [] | [
"TAGS\n#region-us \n"
] |
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-Cartpolev1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"... | kws/Reinforce-Cartpolev1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-23T09:00:53+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... |
null | null | One dream, one soul
One prize, one goal
One golden glance of what should be
It's a kind of magic | {} | g1gaman/One_dream_one_soul | null | [
"region:us"
] | null | 2022-08-23T10:35:58+00:00 | [] | [] | TAGS
#region-us
| One dream, one soul
One prize, one goal
One golden glance of what should be
It's a kind of magic | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | VanHoan/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T10:50:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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. -->
# models_sv_eric_1
This model is a fine-tuned version of [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "models_sv_eric_1", "results": []}]} | niclas/models_sv_eric_1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T10:54:16+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| models\_sv\_eric\_1
===================
This model is a fine-tuned version of facebook/wav2vec2-large-100k-voxpopuli on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1340
* Wer: 0.6241
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### 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* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-nc-4.0 #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\n* eval\\_... |
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. -->
# distilbart-cnn-12-6-samsum-ts-POC
This model is a fine-tuned version of [philschmid/distilbart-cnn-12-6-samsum](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbart-cnn-12-6-samsum-ts-POC", "results": []}]} | ArjunSarkhel/distilbart-cnn-12-6-samsum-ts-POC | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T11:19:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbart-cnn-12-6-samsum-ts-POC
This model is a fine-tuned version of philschmid/distilbart-cnn-12-6-samsum on an unknown dataset.
## Model description
DistilBart-Cnn-Samsum fine-tuned on custom dataset.
## Intended uses & limitations
More information needed
## Training and evaluation data
More informatio... | [
"# distilbart-cnn-12-6-samsum-ts-POC\n\nThis model is a fine-tuned version of philschmid/distilbart-cnn-12-6-samsum on an unknown dataset.",
"## Model description\n\nDistilBart-Cnn-Samsum fine-tuned on custom dataset.",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation d... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbart-cnn-12-6-samsum-ts-POC\n\nThis model is a fine-tuned version of philschmid/distilbart-cnn-12-6-samsum on an unknown dataset.",
"## Model... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detect-femicide-news-xlmr-nl-fft-freeze2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rob... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-xlmr-nl-fft-freeze2", "results": []}]} | gossminn/detect-femicide-news-xlmr-nl-fft-freeze2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T11:20:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| detect-femicide-news-xlmr-nl-fft-freeze2
========================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4119
* Accuracy: 0.8571
* Precision Neg: 0.85
* Precision Pos: 0.875
* Recall Neg: 0.9444... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
feature-extraction | transformers | This is XLM-Paralaw Pretrained Model
Usage:
```
from transformers import AutoModel
from transformers import AutoTokenizer
model = AutoModel.from_pretrained("nguyenthanhasia/XLM-Paralaw")
tokenizer = AutoTokenizer.from_pretrained("nguyenthanhasia/XLM-Paralaw")
```
The paper about this model will be released soon. | {} | nguyenthanhasia/XLM-Paralaw | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T11:46:34+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #endpoints_compatible #region-us
| This is XLM-Paralaw Pretrained Model
Usage:
The paper about this model will be released soon. | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
null | transformers | ## Model Details
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-aware Molecu... | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["jxie/guacamol", "AdrianM0/MUV"]} | UdS-LSV/simcse-smole-bert-muv-mlm | null | [
"transformers",
"pytorch",
"bert",
"dataset:jxie/guacamol",
"dataset:AdrianM0/MUV",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T12:17:48+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #endpoints_compatible #region-us
| ## Model Details
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-aware Molecu... | [
"## Model Details\n\nWe introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.",
"### Enumeration-aw... | [
"TAGS\n#transformers #pytorch #bert #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Model Details\n\nWe introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide rec... |
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. -->
# BiBert-Classification
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "BiBert-Classification", "results": []}]} | HCKLab/BiBert-Classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T12:46:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BiBert-Classification
=====================
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0853
* Accuracy: 0.7433
Model description
-----------------
More information needed
Intende... | [
"### 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 #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
This model is a fork of `sshleifer/tiny-mbart` exported to ONNX.
| {"license": "apache-2.0"} | fxmarty/sshleifer-tiny-mbart-onnx | null | [
"transformers",
"onnx",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T12:52:07+00:00 | [] | [] | TAGS
#transformers #onnx #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a fork of 'sshleifer/tiny-mbart' exported to ONNX.
| [] | [
"TAGS\n#transformers #onnx #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detect-femicide-news-xlmr-nl-mono-freeze2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-ro... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-xlmr-nl-mono-freeze2", "results": []}]} | gossminn/detect-femicide-news-xlmr-nl-mono-freeze2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T13:27:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| detect-femicide-news-xlmr-nl-mono-freeze2
=========================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6487
* Accuracy: 0.6429
* Precision Neg: 0.6429
* Precision Pos: 0.0
* Recall Neg: 1.0
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
null | null | # Face Verification
In this project i want to verify face image that means i want to findout is 2 face is simular or no!
# Code Directory
siamese.py ==>
It contain a class related to every things about model such build model structure,compile model etc
utils.py ==>
Contain a class that related to data and visual... | {} | alifthi/faceVerification | null | [
"region:us"
] | null | 2022-08-23T13:40:33+00:00 | [] | [] | TAGS
#region-us
| # Face Verification
In this project i want to verify face image that means i want to findout is 2 face is simular or no!
# Code Directory
URL ==>
It contain a class related to every things about model such build model structure,compile model etc
URL ==>
Contain a class that related to data and visualization
# ... | [
"# Face Verification\n\nIn this project i want to verify face image that means i want to findout is 2 face is simular or no!",
"# Code Directory\n\nURL ==> \n\nIt contain a class related to every things about model such build model structure,compile model etc\n\nURL ==>\n\nContain a class that related to data and... | [
"TAGS\n#region-us \n",
"# Face Verification\n\nIn this project i want to verify face image that means i want to findout is 2 face is simular or no!",
"# Code Directory\n\nURL ==> \n\nIt contain a class related to every things about model such build model structure,compile model etc\n\nURL ==>\n\nContain a class... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-NER-favsbot
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) o... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-base-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type": "fa... | nguyenkhoa2407/xlm-roberta-base-NER-favsbot | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T13:42:26+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-NER-favsbot
============================
This model is a fine-tuned version of xlm-roberta-base on the favsbot dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0572
* Precision: 0.5556
* Recall: 0.4722
* F1: 0.5105
* Accuracy: 0.6900
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-0... |
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",... | chris-santiago/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T13:47:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 0.7739
* Accuracy: 0.9152
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 #tensorboard #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* lea... |
text-classification | transformers |
# distilbert_rotten_tomatoes_sentiment_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7927
- Accuracy: 0.8386
## Model description
The goal w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["rotten_tomatoes"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "outputs", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "rotten_tomatoes",... | RJZauner/distilbert_rotten_tomatoes_sentiment_classifier | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T14:01:14+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_rotten\_tomatoes\_sentiment\_classifier
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the rotten\_tomatoes dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7927
* Accuracy: 0.8386
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparame... |
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-news-category
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "model-index": [{"name": "distilbert-base-uncased-finetuned-news-category", "results": []}]} | Neha2608/distilbert-base-uncased-finetuned-news-category | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T14:15:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-news-category
This model is a fine-tuned version of distilbert-base-uncased on the ag_news dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# distilbert-base-uncased-finetuned-news-category\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ag_news dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-news-category\n\nThis model is a fine-tuned version of distilbert-base-uncased o... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rob-base-gc1
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
## ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "quoref", "adversarial_qa", "duorc"], "model-index": [{"name": "rob-base-gc1", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adv... | nbroad/rob-base-gc1 | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"dataset:quoref",
"dataset:adversarial_qa",
"dataset:duorc",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T14:18:41+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us
|
# rob-base-gc1
This model is a fine-tuned version of roberta-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 following... | [
"# rob-base-gc1\n\nThis model is a fine-tuned version of roberta-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",
"### Training ... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us \n",
"# rob-base-gc1\n\nThis model is a fine-tuned version of roberta-base on th... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | MM2157/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T14:26:14+00:00 | [] | [] | TAGS
#transformers #pytorch #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 the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9771
* Precision: 0.1071
* Recall: 0.0612
* F1: 0.0779
* Accuracy: 0.8237
Model description
-----------------
More information neede... | [
"### 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 #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* ... |
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-base-cased-NER-favsbot
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type... | nguyenkhoa2407/bert-base-cased-NER-favsbot | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T14:57:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-favsbot #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-NER-favsbot
===========================
This model is a fine-tuned version of bert-base-cased on the favsbot dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1680
* Precision: 0.8462
* Recall: 0.88
* F1: 0.8627
* Accuracy: 0.9444
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-favsbot #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: 1.5e-0... |
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_test1.3b_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_test1.3b_barthez-orangesum-title_finetuned_for_summerization", "results": []}]} | bthomas/article2KW_test1.3b_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 | 2022-08-23T15:13:02+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test1.3b\_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.... | [
"### 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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-NER-favsbot
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the favsbo... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type": "favsbo... | nguyenkhoa2407/roberta-base-NER-favsbot | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T15:23:39+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-NER-favsbot
========================
This model is a fine-tuned version of roberta-base on the favsbot dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4812
* Precision: 0.6940
* Recall: 0.7056
* F1: 0.6997
* Accuracy: 0.8454
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n*... |
image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 57
- CO2 Emissions (in grams): 17.3923
## Validation Metrics
- Loss: 0.875
- Accuracy: 0.762
- Macro F1: 0.758
- Micro F1: 0.762
- Weighted F1: 0.758
- Macro Precision: 0.772
- Micro Precision: 0.762
- Weighted Precision: 0.772
-... | {"tags": ["autotrain", "vision", "image-classification"], "datasets": ["abhishek/autotrain-data-indian-food-image-classification"], "co2_eq_emissions": {"emissions": 17.39230724017564}} | abhishek/autotrain-indian-food-image-classification | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:abhishek/autotrain-data-indian-food-image-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-23T15:24:53+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain #vision #dataset-abhishek/autotrain-data-indian-food-image-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 57
- CO2 Emissions (in grams): 17.3923
## Validation Metrics
- Loss: 0.875
- Accuracy: 0.762
- Macro F1: 0.758
- Micro F1: 0.762
- Weighted F1: 0.758
- Macro Precision: 0.772
- Micro Precision: 0.762
- Weighted Precision: 0.772
-... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 57\n- CO2 Emissions (in grams): 17.3923",
"## Validation Metrics\n\n- Loss: 0.875\n- Accuracy: 0.762\n- Macro F1: 0.758\n- Micro F1: 0.762\n- Weighted F1: 0.758\n- Macro Precision: 0.772\n- Micro Precision: 0.762\n- Weight... | [
"TAGS\n#transformers #pytorch #vit #image-classification #autotrain #vision #dataset-abhishek/autotrain-data-indian-food-image-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- M... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | cemilcelik/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-23T15:34:55+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# camembert-base-NER-favsbot
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type": "favs... | nguyenkhoa2407/camembert-base-NER-favsbot | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T15:37:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-NER-favsbot
==========================
This model is a fine-tuned version of camembert-base on the favsbot dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7433
* Precision: 0.6
* Recall: 0.0121
* F1: 0.0238
* Accuracy: 0.4208
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\... |
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": []} | jsingh/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-23T15:56:59+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.",... |
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... | aleks0309/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-23T16:38:10+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. -->
# pubmedbert-finetuned-ner
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](h... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "The widespread circular form of DNA molecules inside cells creates very serious topological problems during replication. Due to the helical structure of the double he... | siddharthtumre/pubmedbert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:jnlpba",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T16:39:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| pubmedbert-finetuned-ner
========================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the jnlpba dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3766
* Precision: 0.6877
* Recall: 0.7833
* F1: 0.7324
* Accuracy: 0.9267
... | [
"### 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 #bert #token-classification #generated_from_trainer #dataset-jnlpba #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/bertFast_01"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question':... | {"widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of L... | JAlexis/bertFast_01 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T17:37:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
| [
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n",
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] |
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/1122922224820813824/z9zE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dadjokeapibot/1661328249695/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dadjokeapibot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-23T17:53:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dad Joke Bot
@dadjokeapibot
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"
] |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/bertFast_02"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question':... | {"widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of L... | JAlexis/bertFast_02 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T18:57:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
| [
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n",
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | andres-hsn/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-23T19:37:36+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
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-ner_cv
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-ner_cv", "results": []}]} | jhonparra18/distilbert-base-uncased-ner_cv | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T21:11:15+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-ner\_cv
===============================
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: 0.8548
* Precision: 0.3327
* Recall: 0.2358
* F1: 0.2760
* Accuracy: 0.7815
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #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: 1e-05\n* train\\_batch\\_size:... |
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": []} | r3dzon3/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-23T21:49:31+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.",... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | f4falalu/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-23T23:26:05+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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-base-chinese-ws-finetuned-ner
This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/c... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-ws-finetuned-ner", "results": []}]} | HYM/bert-base-chinese-ws-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T00:24:36+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-ws-finetuned-ner
==================================
This model is a fine-tuned version of ckiplab/bert-base-chinese-ws on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0567
* Precision: 0.9615
* Recall: 0.9630
* F1: 0.9623
* Accuracy: 0.9829
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 18\n* eval\\_batch\\_size: 18\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-gpl-3.0 #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: 18\n* ev... |
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. -->
# gpt2-NER-favsbot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the favsbot dataset.
It achieves t... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "gpt2-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type": "favsbot", "con... | nguyenkhoa2407/gpt2-NER-favsbot | null | [
"transformers",
"pytorch",
"gpt2",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T00:24:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-NER-favsbot
================
This model is a fine-tuned version of gpt2 on the favsbot dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5146
* Precision: 0.3782
* Recall: 0.3278
* F1: 0.3512
* Accuracy: 0.5597
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #gpt2 #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | zzj0402/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T01:00:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
null | espnet |
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_multilingual_asr_hubert_frontend`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/fleurs/asr1
./run.sh
```
<!-- Generated by scr... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-recognition"], "datasets": ["google/fleurs"]} | espnet/wanchichen_fleurs_multilingual_asr_hubert_frontend | null | [
"espnet",
"audio",
"speech-recognition",
"en",
"dataset:google/fleurs",
"license:cc-by-4.0",
"region:us"
] | null | 2022-08-24T01:05:15+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #speech-recognition #en #dataset-google/fleurs #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/wanchichen\_fleurs\_multilingual\_asr\_hubert\_frontend'
This model was trained by William Chen using the fleurs recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Aug 21 15:18:30 EDT 2022'
* python version... | [
"### 'espnet/wanchichen\\_fleurs\\_multilingual\\_asr\\_hubert\\_frontend'\n\n\nThis model was trained by William Chen using the fleurs recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Aug 21 15:18:30 EDT 2022'\n* python version: '3.8.6 (... | [
"TAGS\n#espnet #audio #speech-recognition #en #dataset-google/fleurs #license-cc-by-4.0 #region-us \n",
"### 'espnet/wanchichen\\_fleurs\\_multilingual\\_asr\\_hubert\\_frontend'\n\n\nThis model was trained by William Chen using the fleurs recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=====... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-roman-numeral
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-roman-numeral", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "farleyknigh... | farleyknight/vit-base-roman-numeral | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T01:13:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-roman-numeral
======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the farleyknight/roman\_numerals dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6891
* Accuracy: 0.8309
Model description
-----------------
More information neede... | [
"### 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: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-imagefolder #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*... |
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-distilled-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-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | chris-santiago/distilbert-base-uncased-distilled-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 | 2022-08-24T01:29:21+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-distilled-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: 0.2469
* Accuracy: 0.9458
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: 10",
"### Train... | [
"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:... |
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. -->
# secbert-finetuned-imdb
This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "secbert-finetuned-imdb", "results": []}]} | zzj0402/secbert-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T01:50:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| secbert-finetuned-imdb
======================
This model is a fine-tuned version of jackaduma/SecBERT on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4299
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_bat... |
null | espnet |
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_english_asr_wav2vec_frontend`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/fleurs/asr1
./run.sh
```
<!-- Generated by scripts/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-recognition"], "datasets": ["google/fleurs"]} | espnet/wanchichen_fleurs_english_asr_wav2vec_frontend | null | [
"espnet",
"audio",
"speech-recognition",
"en",
"dataset:google/fleurs",
"license:cc-by-4.0",
"region:us"
] | null | 2022-08-24T01:52:16+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #speech-recognition #en #dataset-google/fleurs #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/wanchichen\_fleurs\_english\_asr\_wav2vec\_frontend'
This model was trained by William Chen using the fleurs recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Aug 14 14:52:04 EDT 2022'
* python version: '3... | [
"### 'espnet/wanchichen\\_fleurs\\_english\\_asr\\_wav2vec\\_frontend'\n\n\nThis model was trained by William Chen using the fleurs recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Aug 14 14:52:04 EDT 2022'\n* python version: '3.8.6 (defa... | [
"TAGS\n#espnet #audio #speech-recognition #en #dataset-google/fleurs #license-cc-by-4.0 #region-us \n",
"### 'espnet/wanchichen\\_fleurs\\_english\\_asr\\_wav2vec\\_frontend'\n\n\nThis model was trained by William Chen using the fleurs recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\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. -->
# CoVBERT
CoVBERT is a protein language model which speaks the language of SARS-CoV-2 spike proteins!
Enter a sequence with mask a... | {"tags": ["generated_from_trainer"], "widget": [{"text": "MLLTS<mask>FFALVDSTI"}], "model-index": [{"name": "CoVBERT", "results": []}]} | hunarbatra/CoVBERT | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T02:05:28+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| CoVBERT
=======
CoVBERT is a protein language model which speaks the language of SARS-CoV-2 spike proteins!
Enter a sequence with mask and let CoVBERT predict the mutation at that position!
CoVBERT has been trained with 50K spike glycoprotein sequences scraped from GISAID
It achieves the following results on the ev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_... |
fill-mask | transformers | This project page is about the pytorch code implementation of GlyphBERT by the HITsz-TMG research group.
GlyphBERT is a Chinese pre-training model that includes Chinese character glyph features.It renders t... | {"language": ["zh"], "license": "afl-3.0", "tags": ["bert-base-chinese"]} | HIT-TMG/GlyphBERT | null | [
"transformers",
"bert",
"fill-mask",
"bert-base-chinese",
"zh",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T02:05:34+00:00 | [] | [
"zh"
] | TAGS
#transformers #bert #fill-mask #bert-base-chinese #zh #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| This project page is about the pytorch code implementation of GlyphBERT by the HITsz-TMG research group.
!URL
GlyphBERT is a Chinese pre-training model that includes Chinese character glyph features.It renders the input characters into images and designs them in the form of multi-channel location feature maps, ... | [] | [
"TAGS\n#transformers #bert #fill-mask #bert-base-chinese #zh #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-persian4-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-persian4-demo", "results": []}]} | hobab185/noora | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T02:36:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-persian4-demo
=================================
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: 0.4691
* Wer: 0.5166
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0003\n* train\\_batch\\_size: 1... |
text-generation | transformers |
#### Example
##### gen.py
```
from transformers import GPTNeoForCausalLM, AutoTokenizer
import torch
import sys
model_name = sys.argv[1]
model = GPTNeoForCausalLM.from_pretrained(model_name).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate(model, text, temperature=0.9, min_length=256,... | {"license": "mit"} | mycringefactory/spamtontalk_gpt_neo_xl_v10 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T03:03:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
#### Example
##### URL
| [
"#### Example",
"##### URL"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Example",
"##### URL"
] |
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. -->
# electramed-small-BC5CDR-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.c... | {"tags": ["generated_from_trainer"], "datasets": ["bc5_cdr"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-BC5CDR-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "bc5_cdr", "type": "bc5_cdr", "config": "... | chintagunta85/electramed-small-BC5CDR-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:bc5_cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T03:53:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-bc5_cdr #model-index #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-BC5CDR-ner
===========================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the bc5\_cdr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1227
* Precision: 0.8092
* Recall: 0.8829
* F1: 0.8444
* Accuracy: 0.9686
Model descrip... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-bc5_cdr #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
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. -->
# electramed-small-BC4CHEMD-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface... | {"tags": ["generated_from_trainer"], "datasets": ["bc4chemd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-BC4CHEMD-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "bc4chemd", "type": "bc4chemd", "confi... | chintagunta85/electramed-small-BC4CHEMD-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:bc4chemd",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T04:07:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-bc4chemd #model-index #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-BC4CHEMD-ner
=============================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the bc4chemd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0655
* Precision: 0.7716
* Recall: 0.6761
* F1: 0.7207
* Accuracy: 0.9771
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-bc4chemd #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* ... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | marvind434/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T04:20:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3026
* Accuracy: 1.0
Model description
---------... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
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_murad_with_some_new_data
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2_murad_with_some_new_data", "results": []}]} | MBMMurad/wav2vec2_murad_with_some_new_data | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T04:29:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2_murad_with_some_new_data
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2971
- eval_wer: 0.2084
- eval_runtime: 511.5492
- eval_samples_per_second: 9.774
- eval_steps_per_second: 0.612
- epoch... | [
"# wav2vec2_murad_with_some_new_data\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2971\n- eval_wer: 0.2084\n- eval_runtime: 511.5492\n- eval_samples_per_second: 9.774\n- eval_steps_per_second: 0.6... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2_murad_with_some_new_data\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt ach... |
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": []} | ichibanm/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T04:41:32+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.",... |
summarization | transformers | - This model is a [kobart-news](https://huggingface.co/ainize/kobart-news) finetuned on the [문서요약 텍스트/신문기사](https://aihub.or.kr/aidata/8054)
<<20220904 Commit>>
개인 스터디용으로 긴 문장의 요약 모델 특화된 모델을 만들기 위해 KoBART 모델을 Longformer Encoder Decoder로 변환한 모델입니다.
기존의 transformers를 라이브러리로 불러와서 모델을 실행하는 방법은 사용할 수는 없습니다.😂
같이 올려놓은 loa... | {"language": "ko", "tags": ["summarization", "bart", "longformer"]} | noahkim/KoBART_with_LED | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"longformer",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T04:50:49+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #longformer #ko #autotrain_compatible #endpoints_compatible #region-us
| - This model is a kobart-news finetuned on the 문서요약 텍스트/신문기사
<<20220904 Commit>>
개인 스터디용으로 긴 문장의 요약 모델 특화된 모델을 만들기 위해 KoBART 모델을 Longformer Encoder Decoder로 변환한 모델입니다.
기존의 transformers를 라이브러리로 불러와서 모델을 실행하는 방법은 사용할 수는 없습니다.
같이 올려놓은 load_model.py를 라이브러리화하여 모델을 로드하시면 사용 가능합니다.
추후 지속적인 업데이트로 별도의 불편함 없이 사용할 수 있도록 바꾸겠습... | [
"# Python Code\nfrom transformers import PreTrainedTokenizerFas\nfrom load_model import LongformerBartForConditionalGeneration\n\ntokenizer = PreTrainedTokenizerFast.from_pretrained(\"hohovisual/KoBART_with_LED\")\nmodel = LongformerBartForConditionalGeneration.from_pretrained(\"hohovisual/KoBART_with_LED model fil... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #longformer #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# Python Code\nfrom transformers import PreTrainedTokenizerFas\nfrom load_model import LongformerBartForConditionalGeneration\n\ntokenizer = PreTrainedTokenizerFast... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | Kulinwot/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T05:01:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0179
* Accuracy: 0.7656
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
text-to-speech | speechbrain |
# Vocoder with HiFIGAN trained on LJSpeech
This repository provides all the necessary tools for using a [ALLFA Public](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/SWAHILI).
The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model... | {"language": "sw", "license": "apache-2.0", "tags": ["Vocoder", "HiFIGAN", "text-to-speech", "TTS", "speech-synthesis", "speechbrain"], "datasets": ["LJSpeech"], "inference": false} | aioxlabs/hifigan-swahili | null | [
"speechbrain",
"Vocoder",
"HiFIGAN",
"text-to-speech",
"TTS",
"speech-synthesis",
"sw",
"dataset:LJSpeech",
"license:apache-2.0",
"region:us"
] | null | 2022-08-24T05:18:52+00:00 | [] | [
"sw"
] | TAGS
#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #sw #dataset-LJSpeech #license-apache-2.0 #region-us
|
# Vocoder with HiFIGAN trained on LJSpeech
This repository provides all the necessary tools for using a ALLFA Public.
The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram.
## Install Spee... | [
"# Vocoder with HiFIGAN trained on LJSpeech\n\nThis repository provides all the necessary tools for using a ALLFA Public. \n\nThe pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram.",
"## I... | [
"TAGS\n#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #sw #dataset-LJSpeech #license-apache-2.0 #region-us \n",
"# Vocoder with HiFIGAN trained on LJSpeech\n\nThis repository provides all the necessary tools for using a ALLFA Public. \n\nThe pre-trained model takes in input a spectrogram an... |
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. -->
# electramed-small-SPECIES800-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["species_800"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-SPECIES800-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "species_800", "type": "species_8... | chintagunta85/electramed-small-SPECIES800-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:species_800",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T05:32:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-species_800 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-SPECIES800-ner
===============================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the species\_800 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0513
* Precision: 0.6221
* Recall: 0.7471
* F1: 0.6789
* Accuracy: 0.9831
M... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-species_800 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
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_Tuning_XLSR_trained
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fine_Tuning_XLSR_trained", "results": []}]} | nnair25/Fine_Tuning_XLSR_trained | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T05:48:29+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# Fine_Tuning_XLSR_trained
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# Fine_Tuning_XLSR_trained\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Fine_Tuning_XLSR_trained\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMore informati... |
summarization | null | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... | {"language": "ko", "license": "apache-2.0", "tags": ["korean", "klue", "summarization"], "datasets": ["c4"]} | shwan/readme_test | null | [
"korean",
"klue",
"summarization",
"ko",
"dataset:c4",
"license:apache-2.0",
"region:us"
] | null | 2022-08-24T05:49:32+00:00 | [] | [
"ko"
] | TAGS
#korean #klue #summarization #ko #dataset-c4 #license-apache-2.0 #region-us
| KoMiniLM
========
Korean mini language model
Overview
--------
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight kore... | [
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]] were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project.",
"### Data sets",
"### C... | [
"TAGS\n#korean #klue #summarization #ko #dataset-c4 #license-apache-2.0 #region-us \n",
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]] were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of... |
token-classification | transformers | LayoutLMv1 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm)
Results obtained with seqeval in strict mode:
| | Precision | Recall| F1-sc... | {} | Sennodipoi/LayoutLMv1-kleisterNDA | null | [
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T06:33:25+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlm #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv1 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my Master Degree thesis
Results obtained with seqeval in strict mode:
| [] | [
"TAGS\n#transformers #pytorch #layoutlm #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | LayoutLMv2 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained with seqeval in strict mode:
| | Precision | Recall | F... | {} | Sennodipoi/LayoutLMv2-kleisterNDA | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T06:34:21+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv2 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained with seqeval in strict mode:
| [] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | Al020198zee/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-24T07:11:59+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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-scratch
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-scratch", "results": []}]} | hieule/distilbert-base-uncased-scratch | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T07:21:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-scratch
===============================
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: 6.6235
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# electramed-small-JNLPBA-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.c... | {"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-JNLPBA-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "jnlpba", "type": "jnlpba", "config": "jnl... | chintagunta85/electramed-small-JNLPBA-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:jnlpba",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T07:43:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us
| electramed-small-JNLPBA-ner
===========================
This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the jnlpba dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1167
* Precision: 0.8225
* Recall: 0.8782
* F1: 0.8494
* Accuracy: 0.9621
Model descripti... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BLOOM-560m-Beatles-Lyrics-finetuned
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscie... | {"license": "bigscience-bloom-rail-1.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "BLOOM-560m-Beatles-Lyrics-finetuned", "results": []}]} | wvangils/BLOOM-560m-Beatles-Lyrics-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bloom",
"text-generation",
"generated_from_trainer",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T07:51:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bloom #text-generation #generated_from_trainer #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| BLOOM-560m-Beatles-Lyrics-finetuned
===================================
This model is a fine-tuned version of bigscience/bloom-560m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3387
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bloom #text-generation #generated_from_trainer #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
text2text-generation | transformers | Idea is to build a model which will take keywords as inputs and generate jokes as outputs.
Potential use case can include:
- joke generator
- meme generator | {"language": "en", "license": "mit", "tags": ["keytojoke", "k2j", "Keywords to Jokes"], "thumbnail": "Keywords to Jokes"} | RyanQin/k2j | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytojoke",
"k2j",
"Keywords to Jokes",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T08:19:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytojoke #k2j #Keywords to Jokes #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Idea is to build a model which will take keywords as inputs and generate jokes as outputs.
Potential use case can include:
- joke generator
- meme generator | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytojoke #k2j #Keywords to Jokes #en #license-mit #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-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... | Blind2015/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 | 2022-08-24T08:25:19+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: 0.7846
* Matthews Correlation: 0.5189
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 | null | ---
license: afl-3.0
kawaii
FAZER | {} | komo0628/1 | null | [
"region:us"
] | null | 2022-08-24T08:51:42+00:00 | [] | [] | TAGS
#region-us
| ---
license: afl-3.0
kawaii
FAZER | [] | [
"TAGS\n#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. -->
# Sohini17/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-s... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Sohini17/mt5-small-finetuned-amazon-en-es", "results": []}]} | Sohini17/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T09:16:00+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
| Sohini17/mt5-small-finetuned-amazon-en-es
=========================================
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: 3.1301
* Validation Loss: 2.6937
* Epoch: 3
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': 92170, '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... |
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": []} | vinrougeed/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T10:02:31+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.",... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | ROBERTaCoder/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T10:17:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5452
* Wer: 0.3296
Model description
-----------------
More information needed
Intend... | [
"### 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... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | shed-e/MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T10:29:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4353
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | shed-e/NER | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T10:36:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0637
* Precision: 0.9335
* Recall: 0.9500
* F1: 0.9417
* Accuracy: 0.9862
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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