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text-generation | transformers |
#harry potter DialoGPT model | {"tags": ["conversational"]} | RizqFarIDN/DialoGPT-medium-harrypotter | null | [
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|
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text-generation | transformers |
#harry potter DialoGPT model | {"tags": ["conversational"]} | RizqFarIDN/DialoGPT-small-harrypotter | null | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-finetuned-chunk
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-chunk", "results": []}]} | RobW/distilbert-base-cased-finetuned-chunk | null | [
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"token-classification",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased-finetuned-chunk
=====================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5180
* Precision: 0.8615
* Recall: 0.9088
* F1: 0.8845
* Accuracy: 0.8239
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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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. -->
# deberta-base-mnli-finetuned-cola
This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/micr... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "deberta-base-mnli-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"},... | Roberta55/deberta-base-mnli-finetuned-cola | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-base-mnli-finetuned-cola
================================
This model is a fine-tuned version of microsoft/deberta-base-mnli on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8205
* Matthews Correlation: 0.6282
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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... | [
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text-generation | transformers |
# Mikoto Jinba DialoGPT Model | {"tags": ["conversational"]} | RobinMari/DialoGPT-small-mikoto | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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multiple-choice | 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. -->
# Rocketknight1/bert-base-cased-finetuned-swag
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/bert-base-cased-finetuned-swag", "results": []}]} | Rocketknight1/bert-base-cased-finetuned-swag | null | [
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"tensorboard",
"bert",
"multiple-choice",
"generated_from_keras_callback",
"license:apache-2.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #multiple-choice #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Rocketknight1/bert-base-cased-finetuned-swag
============================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8709
* Train Accuracy: 0.6465
* Validation Loss: 0.6167
* Validation Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 9192, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/bert-base-cased-finetuned-wikitext2", "results": []}]} | Rocketknight1/bert-base-cased-finetuned-wikitext2 | null | [
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"fill-mask",
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#transformers #tf #tensorboard #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/bert-base-cased-finetuned-wikitext2
=================================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 6.3982
* Validation Loss: 6.2664
* Epoch: 1
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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multiple-choice | 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. -->
# Rocketknight1/bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/bert-base-uncased-finetuned-swag", "results": []}]} | Rocketknight1/bert-base-uncased-finetuned-swag | null | [
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"multiple-choice",
"generated_from_keras_callback",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #multiple-choice #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Rocketknight1/bert-base-uncased-finetuned-swag
==============================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8360
* Train Accuracy: 0.6631
* Validation Loss: 0.5885
* Validation A... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/distilbert-base-uncased-finetuned-cola", "results": []}]} | Rocketknight1/distilbert-base-uncased-finetuned-cola | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/distilbert-base-uncased-finetuned-cola
====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3182
* Validation Loss: 0.4914
* Train Matthews Corr... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/distilbert-base-uncased-finetuned-ner", "results": []}]} | Rocketknight1/distilbert-base-uncased-finetuned-ner | null | [
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"generated_from_keras_callback",
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| Rocketknight1/distilbert-base-uncased-finetuned-ner
===================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2026
* Validation Loss: 0.0726
* Train Precision: 0.89... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/distilbert-base-uncased-finetuned-squad", "results": []}]} | Rocketknight1/distilbert-base-uncased-finetuned-squad | null | [
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Rocketknight1/distilbert-base-uncased-finetuned-squad
=====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.5124
* Train End Logits Accuracy: 0.6041
* Train S... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11064, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/distilgpt2-finetuned-wikitext2", "results": []}]} | Rocketknight1/distilgpt2-finetuned-wikitext2 | null | [
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"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Rocketknight1/distilgpt2-finetuned-wikitext2
============================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.8577
* Validation Loss: 3.6752
* Epoch: 0
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | Rocketknight1/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
... | [
"# distilroberta-base-finetuned-wikitext2\n\nThis model is a fine-tuned version of distilroberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training ... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/gbert-base-germaner
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/gbert-base-germaner", "results": []}]} | Rocketknight1/gbert-base-germaner | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/gbert-base-germaner
=================================
This model is a fine-tuned version of deepset/gbert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0340
* Validation Loss: 0.0881
* Epoch: 2
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/gpt2-finetuned-wikitext2", "results": []}]} | Rocketknight1/gpt2-finetuned-wikitext2 | null | [
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"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Rocketknight1/gpt2-finetuned-wikitext2
======================================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 7.3062
* Validation Loss: 6.7676
* Epoch: 0
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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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. -->
# Rocketknight1/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/marian-finetuned-kde4-en-to-fr", "results": []}]} | Rocketknight1/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/marian-finetuned-kde4-en-to-fr
============================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6862
* Validation Loss: 0.8050
* Epoch: 2
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': 5e-05, 'decay\\_steps': 17733, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/model-card-callback-test-new
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/model-card-callback-test-new", "results": []}]} | Rocketknight1/model-card-callback-test-new | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/model-card-callback-test-new
==========================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0031
* Train Accuracy: 1.0
* Validation Loss: 0.0000
* Validation Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# model_card_test2
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "model_card_test2", "results": []}]} | Rocketknight1/model_card_test2 | null | [
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"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| model\_card\_test2
==================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0031
* Train Accuracy: 1.0
* Validation Loss: 0.0000
* Validation Accuracy: 1.0
* Epoch: 1
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
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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. -->
# Rocketknight1/opus-mt-en-ROMANCE-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ROMANCE](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/opus-mt-en-ROMANCE-finetuned-en-to-ro", "results": []}]} | Rocketknight1/opus-mt-en-ROMANCE-finetuned-en-to-ro | null | [
"transformers",
"tf",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/opus-mt-en-ROMANCE-finetuned-en-to-ro
===================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ROMANCE on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7140
* Validation Loss: 1.2757
* Train Bleu: 2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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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. -->
# Rocketknight1/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/t5-small-finetuned-xsum", "results": []}]} | Rocketknight1/t5-small-finetuned-xsum | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Rocketknight1/t5-small-finetuned-xsum
=====================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7172
* Validation Loss: 2.3977
* Train Rouge1: 28.7469
* Train Rouge2: 7.9005
* Train Rougel: 22.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test-model-tf
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following resul... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "test-model-tf", "results": []}]} | Rocketknight1/test-model-tf | null | [
"transformers",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# test-model-tf
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training... | [
"# test-model-tf\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informati... | [
"TAGS\n#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# transformers-qa
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "transformers-qa", "results": []}]} | Rocketknight1/transformers-qa | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| transformers-qa
===============
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9300
* Validation Loss: 1.1437
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### F... | [
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null | null | # Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue,... | {"title": "CLIP-Guided-Diffusion", "emoji": "\ud83d\udca9", "colorFrom": "purple", "colorTo": "red", "sdk": "gradio", "app_file": "app.py", "pinned": false} | Rodrigo/teste5 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| # Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue,... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "model-index": [{"name": "", "results": []}]} | Rolv-Arild/xls-r-300m-npsc-4 | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1957
* Wer: 0.1697
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-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 epsil... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.296... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "", "results": []}]} | Rolv-Arild/xls-r-300m-npsc-seq2seq | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2965
* Wer: 0.3144
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation ... | [
"### 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* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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fill-mask | transformers |
# ProtBert model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it on... | {"tags": ["protein language model", "protein"], "datasets": ["Uniref100"]} | Rostlab/prot_bert | null | [
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#transformers #pytorch #fill-mask #protein language model #protein #dataset-Uniref100 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ProtBert model
==============
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.
Model description
--------------... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given protein sequence in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe ProtBert model was pretrained on Uniref100, a dataset consisting of 217 million... | [
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fill-mask | transformers |
# ProtBert-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: i... | {"language": "protein", "tags": ["protein language model"], "datasets": ["BFD"]} | Rostlab/prot_bert_bfd | null | [
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| ProtBert-BFD model
==================
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.
Model description
------... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given protein sequence in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe ProtBert-BFD model was pretrained on BFD, a dataset consisting of 2.1 billion p... | [
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text2text-generation | transformers |
# ProtT5-XL-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: ... | {"language": "protein", "tags": ["protein language model"], "datasets": ["BFD"]} | Rostlab/prot_t5_xl_bfd | null | [
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| ProtT5-XL-BFD model
===================
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.
Model description
----... | [
"### How to use\n\n\nHere is how to use this model to extract the features of a given protein sequence in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe ProtT5-XL-BFD model was pretrained on BFD, a dataset consisting of 2.1 billion protein sequences.\n\n\nTraining procedure\n------------------",
"### Preproc... | [
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text2text-generation | transformers |
# ProtT5-XL-UniRef50 model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino ac... | {"tags": ["protein language model"], "datasets": ["UniRef50"]} | Rostlab/prot_t5_xl_uniref50 | null | [
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| ProtT5-XL-UniRef50 model
========================
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.
Model descri... | [
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"##... | [
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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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | Roy029/distilroberta-base-finetuned-wikitext2 | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2005
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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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. -->
# japanese-roberta-base-finetuned-wikitext2
This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingfac... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "japanese-roberta-base-finetuned-wikitext2", "results": []}]} | Roy029/japanese-roberta-base-finetuned-wikitext2 | null | [
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| japanese-roberta-base-finetuned-wikitext2
=========================================
This model is a fine-tuned version of rinna/japanese-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2302
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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text-generation | transformers |
# Almas DialoGPT Model | {"tags": ["conversational"]} | Royce23/DialoGPT-small-almas | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## Usage
The model can be used directly (without a language model) as follows:
```p... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "apache-2.0", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "PyTorch"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Rubens XLSR Wav2Vec2 Large 53 Por... | Rubens/Wav2Vec2-Large-XLSR-53-Portuguese | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the Common Voice dataset.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Portuguese test data of Common Voice.
Test ... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## Usage
The model can be used directly (without a language model) as follows:
```p... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "apache-2.0", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "PyTorch"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Rubens XLSR Wav2Vec2 Large 53 Por... | Rubens/Wav2Vec2-Large-XLSR-53-a-Portuguese | null | [
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|
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the Common Voice dataset.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Portuguese test data of Common Voice.
Test ... | [
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"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Portuguese test data of Common Vo... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Rush11/DialoGPT-small-HarryPotter | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice Maltese Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "RuudVelo/XLSR-Wav2Vec2-Maltese-1"
device = "cuda"
chars_to_ignore_regex = '[\\... | {"language": "mt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "model-index": [{"name": "XLSR Wav2Vec2 Maltese by RuudVelo", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice m... | RuudVelo/XLSR-Wav2Vec2-Maltese-1 | null | [
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"audio",
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
## Evaluation on Common Voice Maltese Test
Result: 30.0 % | [
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] |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-cv8-mt-lm
This model is a fine-tuned version of [wav2vec2-large-xls-r-1b-cv8-mt-lm](https://huggingface.c... | {"language": ["mt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "mt", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-... | RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt-lm | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
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"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"... | null | 2022-03-02T23:29:04+00:00 | [] | [
"mt"
] | TAGS
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|
# wav2vec2-large-xls-r-1b-cv8-mt-lm
This model is a fine-tuned version of wav2vec2-large-xls-r-1b-cv8-mt-lm on the common_voice 8 dataset.
It achieves the following results on the test set:
- Loss: 0.2210
- Wer: 0.1974
Note that the above test results come from the original model without LM (language model) which c... | [
"# wav2vec2-large-xls-r-1b-cv8-mt-lm\n\nThis model is a fine-tuned version of wav2vec2-large-xls-r-1b-cv8-mt-lm on the common_voice 8 dataset.\nIt achieves the following results on the test set:\n- Loss: 0.2210\n- Wer: 0.1974\n\nNote that the above test results come from the original model without LM (language mode... | [
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automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-cv8-mt
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook... | {"language": ["mt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "mt", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-... | RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt | null | [
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"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"... | null | 2022-03-02T23:29:04+00:00 | [] | [
"mt"
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| wav2vec2-large-xls-r-1b-cv8-mt
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2210
* Wer: 0.1974
Model description
-----------------
Note: another version of this mod... | [
"### Training hyperparameters\n\n\nThe following config and hyperparameters were used during training:\n\n\nmodel = Wav2Vec2ForCTC.from\\_pretrained(\n\"facebook/wav2vec2-xls-r-1b\",\nattention\\_dropout=0.05,\nhidden\\_dropout=0.05,\nfeat\\_proj\\_dropout=0.05,\nmask\\_time\\_prob=0.55,\nmask\\_feature\\_prob=0.10... | [
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automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-nl-lm
This model is a fine-tuned version of [wav2vec2-large-xls-r-1b-nl-lm](https://huggingface.co/facebo... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-... | RuudVelo/wav2vec2-large-xls-r-1b-nl-lm | null | [
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"wav2vec2",
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"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
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|
# wav2vec2-large-xls-r-1b-nl-lm
This model is a fine-tuned version of wav2vec2-large-xls-r-1b-nl-lm on the common_voice 8 dataset.
It achieves the following results on the test set:
- Loss: 0.1479
- Wer: 0.1156
Note that the above test results come from the original model without LM (language model) which can be fo... | [
"# wav2vec2-large-xls-r-1b-nl-lm\n\nThis model is a fine-tuned version of wav2vec2-large-xls-r-1b-nl-lm on the common_voice 8 dataset.\nIt achieves the following results on the test set:\n- Loss: 0.1479\n- Wer: 0.1156\n\nNote that the above test results come from the original model without LM (language model) which... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-... | RuudVelo/wav2vec2-large-xls-r-1b-nl | null | [
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"license:apache-2.0",
"... | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at URL The Common Voice 8 Dutch test Wer is 9.73 of that model.
It achieves the following... | [
"### Training hyperparameters\n\n\nModel parameters can be found under Files and versions in the URL file.",
"### Training results",
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-cv8-nl
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-... | RuudVelo/wav2vec2-large-xls-r-300m-cv8-nl | null | [
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"license:apache-2.0",
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"... | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-cv8-nl
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. In addition a 6gram KenLM model was trained and used. The KenLM model was based on train+validation Common Voice 8
It achieves results depicted on the rigth side on the model card (test... | [
"# wav2vec2-large-xls-r-300m-cv8-nl\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. In addition a 6gram KenLM model was trained and used. The KenLM model was based on train+validation Common Voice 8\nIt achieves results depicted on the rigth side on the model card ... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-nl
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "nl", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-nl", "results": [{"task": {"type": "aut... | RuudVelo/wav2vec2-large-xls-r-300m-nl | null | [
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"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #model_for_talk #nl #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-nl
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the test set:
* Loss: 0.3923
* Wer: 0.1748
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\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 epsilo... | [
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automatic-speech-recognition | transformers |
## Evaluation on Common Voice Frisian Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "RuudVelo/wav2vec2-large-xlsr-53-frisian"
device = "cuda"
chars_to_ignore_regex... | {"language": "fy-NL", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "model-index": [{"name": "wav2vec2-large-xlsr-53-frisian by RuudVelo", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Co... | RuudVelo/wav2vec2-large-xlsr-53-frisian | null | [
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"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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## Evaluation on Common Voice Frisian Test
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text-generation | transformers |
# Zeldabot | {"tags": ["conversational"]} | Ryanar/DialoGPT-medium-Zelda | null | [
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null | null | Wkwkwkwk
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text-generation | transformers |
# Rick DialoGPT model
| {"tags": ["conversational"]} | Ryukie/DialoGPT-small-Rick | null | [
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# Rick DialoGPT model
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-medium-Glass_Of_Water | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-medium-Mona | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-small-Harry282 | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-small-MJOLNIR_Soul | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-small-cursedryno | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-small-pikamew362 | null | [
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text-generation | transformers |
# DialoGPT chat bot model using discord messages as data | {"tags": ["conversational"]} | S34NtheGuy/DialoGPT-small-wetterlettuce | null | [
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text-classification | transformers | # Model Card for Password-Model
# Model Details
## Model Description
The Password Model is intended to be used with [Credential Digger](https://github.com/SAP/credential-digger) in order to automatically filter false positive password discoveries.
- **Developed by:** SAP OSS
- **Shared by [Optional]:** Huggi... | {"language": ["en"]} | SAPOSS/password-model | null | [
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"roberta",
"text-classification",
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"arxiv:1910.09700",
"autotrain_compatible",
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#transformers #tf #roberta #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
| # Model Card for Password-Model
# Model Details
## Model Description
The Password Model is intended to be used with Credential Digger in order to automatically filter false positive password discoveries.
- Developed by: SAP OSS
- Shared by [Optional]: Hugging Face
- Model type: Text Classification
- Language... | [
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summarization | transformers |
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its o... | {"tags": ["summarization"], "widget": [{"text": "parse the uses licence node of this package , if any , and returns the license definition if theres"}]} | SEBIS/code_trans_t5_base_api_generation | null | [
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| CodeTrans model for api recommendation generation
=================================================
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' ... | [
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summarization | transformers |
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its o... | {"tags": ["summarization"], "widget": [{"text": "parse the uses licence node of this package , if any , and returns the license definition if theres"}]} | SEBIS/code_trans_t5_base_api_generation_multitask | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #has_space #text-generation-inference #region-us
| CodeTrans model for api recommendation generation
=================================================
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' ... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
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summarization | transformers |
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its o... | {"tags": ["summarization"], "widget": [{"text": "parse the uses licence node of this package , if any , and returns the license definition if theres"}]} | SEBIS/code_trans_t5_base_api_generation_multitask_finetune | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for api recommendation generation
=================================================
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' ... | [
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summarization | transformers |
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its o... | {"tags": ["summarization"], "widget": [{"text": "parse the uses licence node of this package , if any , and returns the license definition if theres"}]} | SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for api recommendation generation
=================================================
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
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Model description
-----------------
This CodeTrans model is based on the 't5-base' ... | [
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summarization | transformers |
# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functio... | {"tags": ["summarization"], "widget": [{"text": "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"}]} | SEBIS/code_trans_t5_base_code_comment_generation_java | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code comment generation java
================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokenized java func... | [
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summarization | transformers |
# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functio... | {"tags": ["summarization"], "widget": [{"text": "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"}]} | SEBIS/code_trans_t5_base_code_comment_generation_java_multitask | null | [
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"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code comment generation java
================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokenized java func... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
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"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functio... | {"tags": ["summarization"], "widget": [{"text": "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"}]} | SEBIS/code_trans_t5_base_code_comment_generation_java_multitask_finetune | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code comment generation java
================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokenized java func... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
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"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functio... | {"tags": ["summarization"], "widget": [{"text": "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"}]} | SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code comment generation java
================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokenized java func... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions... | {"tags": ["summarization"], "widget": [{"text": "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_go | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation go
====================================================
Pretrained model on programming language go using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized go code functions: it works best with tokenized go fu... | [
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n---------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrai... |
summarization | transformers |
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions... | {"tags": ["summarization"], "widget": [{"text": "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_go_multitask | null | [
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation go
====================================================
Pretrained model on programming language go using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized go code functions: it works best with tokenized go fu... | [
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n---------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n... | [
36,
81,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrai... |
summarization | transformers |
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions... | {"tags": ["summarization"], "widget": [{"text": "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_go_multitask_finetune | null | [
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"jax",
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"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation go
====================================================
Pretrained model on programming language go using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized go code functions: it works best with tokenized go fu... | [
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n---------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n... | [
36,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrai... |
summarization | transformers |
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions... | {"tags": ["summarization"], "widget": [{"text": "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_go_transfer_learning_finetune | null | [
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"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation go
====================================================
Pretrained model on programming language go using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized go code functions: it works best with tokenized go fu... | [
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n---------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate go function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrai... |
summarization | transformers |
# CodeTrans model for code documentation generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java f... | {"tags": ["summarization"], "widget": [{"text": "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_java | null | [
"transformers",
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"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation java
======================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
36,
130
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java fu... | {"tags": ["summarization"], "widget": [{"text": "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask | null | [
"transformers",
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"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation java
======================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java fu... | {"tags": ["summarization"], "widget": [{"text": "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask_finetune | null | [
"transformers",
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"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation java
======================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
36,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation java
Pretrained model on programming language java using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java f... | {"tags": ["summarization"], "widget": [{"text": "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_java_transfer_learning_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation java
======================================================
Pretrained model on programming language java using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized java code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
36,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate java function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation javascript
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized javascript code functions: it works best wit... | {"tags": ["summarization"], "widget": [{"text": "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document... | SEBIS/code_trans_t5_base_code_documentation_generation_javascript | null | [
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"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation javascript
============================================================
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized javascript code functions: it... | [
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab noteb... | [
36,
131
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... |
summarization | transformers |
# CodeTrans model for code documentation generation javascript
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized javascript code functions: it works best wi... | {"tags": ["summarization"], "widget": [{"text": "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document... | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation javascript
============================================================
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized javascript code functions: it... | [
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab noteb... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... |
summarization | transformers |
# CodeTrans model for code documentation generation javascript
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized javascript code functions: it works best wi... | {"tags": ["summarization"], "widget": [{"text": "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document... | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask_finetune | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation javascript
============================================================
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized javascript code functions: it... | [
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------... | [
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"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab noteb... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... |
summarization | transformers |
# CodeTrans model for code documentation generation javascript
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized javascript code functions: it works best wit... | {"tags": ["summarization"], "widget": [{"text": "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document... | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_transfer_learning_finetune | null | [
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"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation javascript
============================================================
Pretrained model on programming language javascript using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized javascript code functions: it... | [
"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------... | [
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"### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab noteb... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... |
summarization | transformers |
# CodeTrans model for code documentation generation php
Pretrained model on programming language php using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php funct... | {"tags": ["summarization"], "widget": [{"text": "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater =... | SEBIS/code_trans_t5_base_code_documentation_generation_php | null | [
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"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation php
=====================================================
Pretrained model on programming language php using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized php code functions: it works best with tokenized p... | [
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n--------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTra... |
summarization | transformers |
# CodeTrans model for code documentation generation php
Pretrained model on programming language php using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php functi... | {"tags": ["summarization"], "widget": [{"text": "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater =... | SEBIS/code_trans_t5_base_code_documentation_generation_php_multitask | null | [
"transformers",
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"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation php
=====================================================
Pretrained model on programming language php using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized php code functions: it works best with tokenized p... | [
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n--------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTra... |
summarization | transformers |
# CodeTrans model for code documentation generation php
Pretrained model on programming language php using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php functi... | {"tags": ["summarization"], "widget": [{"text": "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater =... | SEBIS/code_trans_t5_base_code_documentation_generation_php_multitask_finetune | null | [
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"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation php
=====================================================
Pretrained model on programming language php using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized php code functions: it works best with tokenized p... | [
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n--------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\... | [
36,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTra... |
summarization | transformers |
# CodeTrans model for code documentation generation php
Pretrained model on programming language php using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php functi... | {"tags": ["summarization"], "widget": [{"text": "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater =... | SEBIS/code_trans_t5_base_code_documentation_generation_php_transfer_learning_finetune | null | [
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"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation php
=====================================================
Pretrained model on programming language php using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized php code functions: it works best with tokenized p... | [
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n--------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate php function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTra... |
summarization | transformers |
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized ... | {"tags": ["summarization"], "widget": [{"text": "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_python | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #has_space #text-generation-inference #region-us
| CodeTrans model for code documentation generation python
========================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in cola... | [
40,
130
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #has_space #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab note... |
summarization | transformers |
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized ... | {"tags": ["summarization"], "widget": [{"text": "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_python_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation python
========================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
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155
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized ... | {"tags": ["summarization"], "widget": [{"text": "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_python_multitask_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation python
========================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
36,
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85,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized ... | {"tags": ["summarization"], "widget": [{"text": "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_python_transfer_learning_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation python
========================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
36,
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85,
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] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for code documentation generation ruby
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized ruby code functions: it works best with tokenized ruby f... | {"tags": ["summarization"], "widget": [{"text": "def add ( severity , progname , & block ) return true if io . nil? || severity < level message = format_message ( severity , progname , yield ) MUTEX . synchronize { io . write ( message ) } true end"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_ruby | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation ruby
======================================================
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized ruby code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
36,
130
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation ruby
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized ruby code functions: it works best with tokenized ruby ... | {"tags": ["summarization"], "widget": [{"text": "def add ( severity , progname , & block ) return true if io . nil? || severity < level message = format_message ( severity , progname , yield ) MUTEX . synchronize { io . write ( message ) } true end"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_ruby_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation ruby
======================================================
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized ruby code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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summarization | transformers |
# CodeTrans model for code documentation generation ruby
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized ruby code functions: it works best with tokenized ruby ... | {"tags": ["summarization"], "widget": [{"text": "def add ( severity , progname , & block ) return true if io . nil? || severity < level message = format_message ( severity , progname , yield ) MUTEX . synchronize { io . write ( message ) } true end"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_ruby_multitask_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation ruby
======================================================
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized ruby code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for code documentation generation ruby
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized ruby code functions: it works best with tokenized ruby f... | {"tags": ["summarization"], "widget": [{"text": "def add ( severity , progname , & block ) return true if io . nil? || severity < level message = format_message ( severity , progname , yield ) MUTEX . synchronize { io . write ( message ) } true end"}]} | SEBIS/code_trans_t5_base_code_documentation_generation_ruby_transfer_learning_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for code documentation generation ruby
======================================================
Pretrained model on programming language ruby using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized ruby code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n... | [
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] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate ruby function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTr... |
summarization | transformers |
# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.
## Model description
... | {"tags": ["summarization"], "widget": [{"text": "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"}]} | SEBIS/code_trans_t5_base_commit_generation | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for git commit message generation
=================================================
Pretrained model on git commit using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.
Model description... | [
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n------------------\n\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrain... | [
36,
134
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining da... |
summarization | transformers |
# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.
## Model description
... | {"tags": ["summarization"], "widget": [{"text": "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"}]} | SEBIS/code_trans_t5_base_commit_generation_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for git commit message generation
=================================================
Pretrained model on git commit using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.
Model description... | [
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------",
... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrain... | [
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] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining da... |
summarization | transformers |
# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.
## Model description
... | {"tags": ["summarization"], "widget": [{"text": "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"}]} | SEBIS/code_trans_t5_base_commit_generation_multitask_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for git commit message generation
=================================================
Pretrained model on git commit using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.
Model description... | [
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------",
... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrain... | [
36,
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86,
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] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining da... |
summarization | transformers |
# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.
## Model description
... | {"tags": ["summarization"], "widget": [{"text": "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"}]} | SEBIS/code_trans_t5_base_commit_generation_transfer_learning_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for git commit message generation
=================================================
Pretrained model on git commit using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.
Model description... | [
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------",
... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrain... | [
36,
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123
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate git commit message using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining da... |
summarization | transformers |
# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own S... | {"tags": ["summarization"], "widget": [{"text": "you are given an array of numbers a and a number b , compute the difference of elements in a and b"}]} | SEBIS/code_trans_t5_base_program_synthese | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for program synthesis
=====================================
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' model. It has it... | [
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n------------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nT... | [
36,
133
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrainin... |
summarization | transformers |
# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own S... | {"tags": ["summarization"], "widget": [{"text": "you are given an array of numbers a and a number b , compute the difference of elements in a and b"}]} | SEBIS/code_trans_t5_base_program_synthese_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for program synthesis
=====================================
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' model. It has it... | [
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nT... | [
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155
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrainin... |
summarization | transformers |
# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own S... | {"tags": ["summarization"], "widget": [{"text": "you are given an array of numbers a and a number b , compute the difference of elements in a and b"}]} | SEBIS/code_trans_t5_base_program_synthese_multitask_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for program synthesis
=====================================
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' model. It has it... | [
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nT... | [
36,
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86,
124
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrainin... |
summarization | transformers |
# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own S... | {"tags": ["summarization"], "widget": [{"text": "you are given an array of numbers a and a number b , compute the difference of elements in a and b"}]} | SEBIS/code_trans_t5_base_program_synthese_transfer_learning_finetune | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for program synthesis
=====================================
Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in
this repository.
Model description
-----------------
This CodeTrans model is based on the 't5-base' model. It has it... | [
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n------------------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nT... | [
36,
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86,
124
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTrainin... |
summarization | transformers |
# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csh... | {"tags": ["summarization"], "widget": [{"text": "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLoca... | SEBIS/code_trans_t5_base_source_code_summarization_csharp | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization csharp
====================================================
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized csharp code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
36,
135
] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csh... | {"tags": ["summarization"], "widget": [{"text": "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLoca... | SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization csharp
====================================================
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized csharp code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csha... | {"tags": ["summarization"], "widget": [{"text": "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLoca... | SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune | null | [
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#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization csharp
====================================================
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized csharp code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csha... | {"tags": ["summarization"], "widget": [{"text": "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLoca... | SEBIS/code_trans_t5_base_source_code_summarization_csharp_transfer_learning_finetune | null | [
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"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization csharp
====================================================
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized csharp code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for source code summarization python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized pyt... | {"tags": ["summarization"], "widget": [{"text": "'with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( ) with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf : if line == \" ; Include this text \" : line = line + \" Include below \" out... | SEBIS/code_trans_t5_base_source_code_summarization_python | null | [
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"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization python
====================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nEvaluation results\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
summarization | transformers |
# CodeTrans model for source code summarization python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized pyt... | {"tags": ["summarization"], "widget": [{"text": "'with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( ) with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf : if line == \" ; Include this text \" : line = line + \" Include below \" out... | SEBIS/code_trans_t5_base_source_code_summarization_python_multitask | null | [
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"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us
| CodeTrans model for source code summarization python
====================================================
Pretrained model on programming language python using the t5 base model architecture. It was first released in
this repository. This model is trained on tokenized python code functions: it works best with tokeniz... | [
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\nTraining data\n-------------\n\n\nThe supervised training tasks datasets can be downloaded on Link\n\n\nTraining procedure\n-----------... | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.... | [
36,
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"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to generate python function documentation using Transformers SummarizationPipeline:\n\n\nRun this example in colab notebook.\n\n\n... |
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