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automatic-speech-recognition | transformers | ## Wav2Vec2 Fine-Tuned on English dataset Timit
The model was fine-tuned in a google colab for demonstration purposes.
Please refer to [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information about the model. | {} | patrickvonplaten/wav2vec2-base-timit-demo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Wav2Vec2 Fine-Tuned on English dataset Timit
The model was fine-tuned in a google colab for demonstration purposes.
Please refer to this blog for more information about the model. | [
"## Wav2Vec2 Fine-Tuned on English dataset Timit\n\nThe model was fine-tuned in a google colab for demonstration purposes.\nPlease refer to this blog for more information about the model."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Wav2Vec2 Fine-Tuned on English dataset Timit\n\nThe model was fine-tuned in a google colab for demonstration purposes.\nPlease refer to this blog for more information about the model."
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-fine-tuned
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "wav2vec2-base-timit-fine-tuned", "results": []}]} | patrickvonplaten/wav2vec2-base-timit-fine-tuned | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-fine-tuned
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3457
* Wer: 0.2151
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0... |
null | transformers |
# Wav2Vec2-Base
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a down... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | patrickvonplaten/wav2vec2-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base
Facebook's Wav2Vec2
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more information.
P... | [
"# Wav2Vec2-Base \n\nFacebook's Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more informa... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base \n\nFacebook's Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech... |
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-common_voice-ab-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["ab"], "license": "apache-2.0", "tags": ["speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-common_voice-ab-demo", "results": []}]} | patrickvonplaten/wav2vec2-common_voice-ab-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"common_voice",
"generated_from_trainer",
"ab",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #common_voice #generated_from_trainer #ab #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-common_voice-ab-demo
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 15.1812
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# wav2vec2-common_voice-ab-demo\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 15.1812\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #common_voice #generated_from_trainer #ab #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-common_voice-ab-demo\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on th... |
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-common_voice-tamil
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/faceb... | {"language": ["ta"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tamil", "results": []}]} | patrickvonplaten/wav2vec2-common_voice-tamil | null | [
"transformers",
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"tensorboard",
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"common_voice",
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"ta",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ta #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tamil
============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1172
* Wer: 1.0070
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ta #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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-common_voice-tr-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.c... | {"language": ["tr"], "license": "apache-2.0", "tags": ["speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | patrickvonplaten/wav2vec2-common_voice-tr-demo-dist | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo-dist
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3856
* Wer: 0.3581
* Cer: 0.0805
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* num\\_gpus: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 1\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | patrickvonplaten/wav2vec2-common_voice-tr-demo | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-common\_voice-tr-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3856
* Wer: 0.3556
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
automatic-speech-recognition | transformers | Fine-tuning of `wav2vec2-large-lv60` on 100h of Librispeech training data. Results are a bit worse than those reported in the Appendix in Table 3 of the original [paper](https://arxiv.org/pdf/2006.11477.pdf).
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
- 2 GPUs Titan RTX
- Tota... | {} | patrickvonplaten/wav2vec2-large-lv60h-100h-2nd-try | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #arxiv-2006.11477 #endpoints_compatible #region-us
| Fine-tuning of 'wav2vec2-large-lv60' on 100h of Librispeech training data. Results are a bit worse than those reported in the Appendix in Table 3 of the original paper.
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
* 2 GPUs Titan RTX
* Total update steps 17500
* Batch size per ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #arxiv-2006.11477 #endpoints_compatible #region-us \n"
] |
null | transformers | https://wandb.ai/patrickvonplaten/pretraining-wav2vec2/reports/Wav2Vec2-Large--VmlldzoxMTAwODM4?accessToken=wm3qzcnldrwsa31tkvf2pdmilw3f63d4twtffs86ou016xjbyilh55uoi3mo1qzc | {} | patrickvonplaten/wav2vec2-large-repro-960h-libri-120k-steps | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggin... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "xls_r_repro_common_voice_tr"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-common_voice-tr-ft", "results": []}]} | patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"xls_r_repro_common_voice_tr",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-common\_voice-tr-ft
=============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4179
* Wer: 0.3071
* Cer: 0.0736
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3864
* Wer: 0.3570
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-129-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-129](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-129-turkish-colab", "results": []}]} | patrickvonplaten/wav2vec2-large-xlsr-129-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xlsr-129-turkish-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-129 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3149
* Wer: 0.4748
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfa... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "xls_r_repro_common_voice_tr"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-common_voice-tr-ft", "results": []}]} | patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"xls_r_repro_common_voice_tr",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-common_voice-tr-ft
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4231
- Wer: 0.3104
- Cer: 0.0737
## Model description
More information needed
## Intended uses & limi... | [
"# wav2vec2-large-xlsr-53-common_voice-tr-ft\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4231\n- Wer: 0.3104\n- Cer: 0.0737",
"## Model description\n\nMore information needed",
"## In... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-common_voice-tr-ft\n\nThis model is a fine-tuned versi... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Spanish-With-LM
This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish)
that has language model support.
This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) int... | {"language": "es", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-XLSR-53-Spanish-With-LM
======================================
This is a model copy of Wav2Vec2-Large-XLSR-53-Spanish
that has language model support.
This model card can be seen as a demo for the pyctcdecode integration
with Transformers led by this PR. The PR explains in-detail how the
integration ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-turkish-demo-colab", "results": []}]} | patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-turkish-demo-colab
======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4055
* Wer: 0.4800
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers | ## XLSR-Wav2Vec2 Fine-Tuned on Turkish Common Voice dataset
The model was fine-tuned in a google colab for demonstration purposes.
Please refer to [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information about the model. | {} | patrickvonplaten/wav2vec2-large-xlsr-turkish-demo | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## XLSR-Wav2Vec2 Fine-Tuned on Turkish Common Voice dataset
The model was fine-tuned in a google colab for demonstration purposes.
Please refer to this blog for more information about the model. | [
"## XLSR-Wav2Vec2 Fine-Tuned on Turkish Common Voice dataset\n\nThe model was fine-tuned in a google colab for demonstration purposes.\nPlease refer to this blog for more information about the model."
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## XLSR-Wav2Vec2 Fine-Tuned on Turkish Common Voice dataset\n\nThe model was fine-tuned in a google colab for demonstration purposes.\nPlease refer to this blog for more information about the model."
... |
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-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingfa... | {"license": "apache-2.0", "tags": ["speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-librispeech-clean-100h-demo-dist", "results": []}]} | patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-librispeech-clean-100h-demo-dist
=========================================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0572
* Wer: 0.0417
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
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-random
This model is a fine-tuned version of [patrickvonplaten/wav2vec2-base-random](https://huggingface.co/patrickvonp... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "wav2vec2-random", "results": []}]} | patrickvonplaten/wav2vec2-random | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| wav2vec2-random
===============
This model is a fine-tuned version of patrickvonplaten/wav2vec2-base-random on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1593
* Wer: 0.8364
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
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-xlarge-...-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-xlarge-xlsr-...](https://huggi... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xlarge-...-common_voice-tr-demo", "results": []}]} | patrickvonplaten/wav2vec2-xlarge-dotdotdot-common_voice-tr-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-xlarge-...-common\_voice-tr-demo
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xlarge-xlsr-... on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2701
* Wer: 0.2309
* Cer: 0.0527
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00005\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00005\n* tr... |
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-xls-r-100m-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-100m](https://huggingface.... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "xls_r_repro_common_voice_tr"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-100m-common_voice-tr-ft", "results": []}]} | patrickvonplaten/wav2vec2-xls-r-100m-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"xls_r_repro_common_voice_tr",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-100m-common\_voice-tr-ft
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-100m on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4113
* Wer: 1.0
* Cer: 1.0
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
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-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingfac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "xls_r_repro_common_voice_tr"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-common_voice-tr-ft", "results": []}]} | patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"xls_r_repro_common_voice_tr",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-1b-common_voice-tr-ft
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503
## Model description
More information needed
## Intended uses & limi... | [
"# wav2vec2-large-xls-r-1b-common_voice-tr-ft\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n\n- Loss: 0.3015\n- Wer: 0.2149\n- Cer: 0.0503",
"## Model description\n\nMore information needed",
"## I... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-1b-common_voice-tr-ft\n\nThis model is a fine-tuned vers... |
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-xls-r-phoneme-300m-sv
**Note**: The tokenizer was created from the official Swedish phoneme vocabulary as defined here:... | {"language": ["sv"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-phoneme-300m-sv", "results": []}]} | patrickvonplaten/wav2vec2-xls-r-phoneme-300m-sv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"sv",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #sv #dataset-common_voice #endpoints_compatible #region-us
|
# Wav2vec2-xls-r-phoneme-300m-sv
Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL
One can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/URL
This model is a fine-tuned version of wav2vec2-xls-r... | [
"# Wav2vec2-xls-r-phoneme-300m-sv\n\nNote: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL\n\nOne can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained(\"./directory/with/URL\n\nThis model is a fine-tuned version of wav2v... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #sv #dataset-common_voice #endpoints_compatible #region-us \n",
"# Wav2vec2-xls-r-phoneme-300m-sv\n\nNote: The tokenizer was created from the official Swedish phoneme vocabulary as defined here... |
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-xls-r-phoneme-300m-tr
This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-phoneme-300m-tr", "results": []}]} | patrickvonplaten/wav2vec2-xls-r-phoneme-300m-tr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
| Wav2vec2-xls-r-phoneme-300m-tr
==============================
This model is a fine-tuned version of wav2vec2-xls-r-300m on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6380
* PER: 0.1664
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* tra... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-53-300m-mls-german-ft
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-xlsr-53-300m-mls-german-ft", "results": []}]} | patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"multilingual_librispeech",
"generated_from_trainer",
"dataset:multilingual_librispeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-53-300m-mls-german-ft
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MULTILINGUAL\_LIBRISPEECH - GERMAN 10h dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2219
* Wer: 0.1288
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Spanish-With-LM
This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish)
that has language model support.
This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) int... | {"language": "es", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | patrickvonplaten/wav2vec2-xlsr-53-es-kenlm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Large-XLSR-53-Spanish-With-LM
======================================
This is a model copy of Wav2Vec2-Large-XLSR-53-Spanish
that has language model support.
This model card can be seen as a demo for the pyctcdecode integration
with Transformers led by this PR. The PR explains in-detail how the
integration ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ## Test model
To test this model run the following code:
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC
import torchaudio
import torch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplate... | {} | patrickvonplaten/wav2vec2_tiny_random | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us
| ## Test model
To test this model run the following code:
| [
"## Test model\n\nTo test this model run the following code:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #endpoints_compatible #region-us \n",
"## Test model\n\nTo test this model run the following code:"
] |
automatic-speech-recognition | transformers |
## Test model
To test this model run the following code:
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC
import torchaudio
import torch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplat... | {"language": "en", "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | patrickvonplaten/wav2vec2_tiny_random_robust | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
|
## Test model
To test this model run the following code:
| [
"## Test model\n\nTo test this model run the following code:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Test model\n\nTo test this model run the following code:"
] |
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. -->
# wavlm-libri-clean-100h-base-plus
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/micros... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune"], "model-index": [{"name": "wavlm-libri-clean-100h-base-plus", "results": []}]} | patrickvonplaten/wavlm-libri-clean-100h-base-plus | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us
| wavlm-libri-clean-100h-base-plus
================================
This model is a fine-tuned version of microsoft/wavlm-base-plus on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0819
* Wer: 0.0683
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 32\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\... |
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. -->
# wavlm-libri-clean-100h-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune"], "model-index": [{"name": "wavlm-libri-clean-100h-base", "results": []}]} | patrickvonplaten/wavlm-libri-clean-100h-base | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us
| wavlm-libri-clean-100h-base
===========================
This model is a fine-tuned version of microsoft/wavlm-base on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0829
* Wer: 0.0675
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 32\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\... |
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. -->
# wavlm-libri-clean-100h-large
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavl... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune"], "model-index": [{"name": "wavlm-librispeech-clean-100h-dist", "results": []}]} | patrickvonplaten/wavlm-libri-clean-100h-large | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us
| wavlm-libri-clean-100h-large
============================
This model is a fine-tuned version of microsoft/wavlm-large on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0601
* Wer: 0.0491
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\... |
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... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "sv"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - Swedish - CV7 - v2", ... | patrickvonplaten/xls-r-300-sv-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2604
- Wer: 0.2334
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# \n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.\nIt achieves the following results on the evaluation set:\n\n- Loss: 0.2604\n- Wer: 0.2334",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# \n\nTh... |
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. -->
# xls-r-300m-it-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_3_0", "generated_from_trainer"], "model-index": [{"name": "xls-r-300m-it-phoneme", "results": []}]} | patrickvonplaten/xls-r-300m-it-phoneme | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_3_0",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us
|
# xls-r-300m-it-phoneme
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - IT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3899
- Wer: 0.0770
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# xls-r-300m-it-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - IT dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3899\n- Wer: 0.0770",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us \n",
"# xls-r-300m-it-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voic... |
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... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sv", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Swedish - CV8 - v2", ... | patrickvonplaten/xls-r-300m-sv-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sv",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sv #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2779
* Wer: 0.2525
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sv #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Trai... |
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. -->
# xls-r-300m-sv-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_3_0", "generated_from_trainer"], "model-index": [{"name": "xls-r-300m-sv-phoneme", "results": []}]} | patrickvonplaten/xls-r-300m-sv-phoneme | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_3_0",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us
|
# xls-r-300m-sv-phoneme
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4879
- Wer: 0.0997
## Model description
More information needed
## Intended uses & limitations... | [
"# xls-r-300m-sv-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - SV-SE dataset.\nIt achieves the following results on the evaluation set:\n\n- Loss: 0.4879\n- Wer: 0.0997",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us \n",
"# xls-r-300m-sv-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voic... |
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. -->
# xls-r-300m-tr-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_3_0", "generated_from_trainer"], "model-index": [{"name": "xls-r-300m-tr-phoneme", "results": []}]} | patrickvonplaten/xls-r-300m-tr-phoneme | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_3_0",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us
|
# xls-r-300m-tr-phoneme
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4378
- Wer: 0.09936
## Model description
More information needed
## Intended uses & limitations
M... | [
"# xls-r-300m-tr-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voice_3_0 - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4378\n- Wer: 0.09936",
"## Model description\n\nMore information needed",
"## Intended uses... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_3_0 #generated_from_trainer #endpoints_compatible #region-us \n",
"# xls-r-300m-tr-phoneme\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the mozilla-foundation/common_voic... |
text-classification | transformers |
# bert-tiny-bahasa-cased-sentiment
Proof of concept of creating a sentiment analysis model with using
https://huggingface.co/malay-huggingface/bert-base-bahasa-cased as the base model.
Tokenizer is copied directly from https://huggingface.co/malay-huggingface/bert-base-bahasa-cased.
Sentiment analysis fine tuning w... | {"language": ["ms", "en", "multilingual"], "license": "apache-2.0", "tags": ["text-classification", "sentiment-analysis"], "widget": [{"text": "Saya sangat gembira hari ini!"}]} | patrickxchong/bert-tiny-bahasa-cased-sentiment | null | [
"transformers",
"tf",
"bert",
"text-classification",
"sentiment-analysis",
"ms",
"en",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms",
"en",
"multilingual"
] | TAGS
#transformers #tf #bert #text-classification #sentiment-analysis #ms #en #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-tiny-bahasa-cased-sentiment
Proof of concept of creating a sentiment analysis model with using
URL as the base model.
Tokenizer is copied directly from URL
Sentiment analysis fine tuning was done with data compiled by huseinzol05 at URL
| [
"# bert-tiny-bahasa-cased-sentiment\n\nProof of concept of creating a sentiment analysis model with using\nURL as the base model.\n\nTokenizer is copied directly from URL\n\nSentiment analysis fine tuning was done with data compiled by huseinzol05 at URL"
] | [
"TAGS\n#transformers #tf #bert #text-classification #sentiment-analysis #ms #en #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-tiny-bahasa-cased-sentiment\n\nProof of concept of creating a sentiment analysis model with using\nURL as the base model.\n\nTokeniz... |
text2text-generation | transformers |
# T5 for Automatic Podcast Summarisation
This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270).
It is based on [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) which was pretrained ... | {"language": ["en"], "tags": ["t5", "summarisation", "pytorch", "lm-head"], "datasets": ["Spotify Podcasts Dataset"], "metrics": ["ROUGE"], "pipeline": ["summarisation"]} | paulowoicho/t5-podcast-summarisation | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04270",
"1910.10683"
] | [
"en"
] | TAGS
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|
# T5 for Automatic Podcast Summarisation
This model is the result of fine-tuning t5-base on the Spotify Podcast Dataset.
It is based on Google's T5 which was pretrained on the C4 dataset.
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: Colin Raffel, Noam Shazeer, ... | [
"# T5 for Automatic Podcast Summarisation\n\nThis model is the result of fine-tuning t5-base on the Spotify Podcast Dataset.\n\nIt is based on Google's T5 which was pretrained on the C4 dataset.\n\n\nPaper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer\n\nAuthors: Colin Raffel, N... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarisation #lm-head #en #arxiv-2004.04270 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5 for Automatic Podcast Summarisation\n\nThis model is the result of fine-tunin... |
fill-mask | transformers | model pretrained on 10m smiles from pubchem.
| {} | pchanda/pretrained-smiles-pubchem10m | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| model pretrained on 10m smiles from pubchem.
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Spanish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset{s}.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Th... | {"language": "es", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Spanish by pcuenq", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | pcuenq/wav2vec2-large-xlsr-53-es | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-Spanish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the Common Voice dataset{s}.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be ev... | [
"# Wav2Vec2-Large-XLSR-53-Spanish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the Common Voice dataset{s}.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe... | [
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"# Wav2Vec2-Large-XLSR-53-Spanish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the Common V... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-EU
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Basque using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model c... | {"language": "eu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Basque by pcuenq", "results": [{"task": {"type": "automatic-speech-recognition", "name": "S... | pcuenq/wav2vec2-large-xlsr-53-eu | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-EU
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Basque using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated a... | [
"# Wav2Vec2-Large-XLSR-53-EU\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Basque using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ca... | [
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"# Wav2Vec2-Large-XLSR-53-EU\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Basque using the Common Voice d... |
fill-mask | transformers |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model.... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "BERT"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERT, een <mask> taalmodel va... | pdelobelle/robbert-v2-dutch-base | null | [
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"a... | null | 2022-03-02T23:29:05+00:00 | [
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"2101.05716",
"1907.11692",
"2001.02943",
"1909.11942"
] | [
"nl"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #BERT #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2001.06286 #arxiv-2004.02814 #arxiv-2010.13652 #arxiv-2101.05716 #arxiv-1907.11692 #arxiv-2001.02943 #arxiv-19... |
RobBERT: Dutch RoBERTa-based Language Model.
============================================
RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging... | [
"### Our Performance Evaluation Results\n\n\nAll experiments are described in more detail in our paper, with the code in our GitHub repository.",
"### Sentiment analysis\n\n\nPredicting whether a review is positive or negative using the Dutch Book Reviews Dataset.",
"### Die/Dat (coreference resolution)\n\n\nWe... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #BERT #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2001.06286 #arxiv-2004.02814 #arxiv-2010.13652 #arxiv-2101.05716 #arxiv-1907.11692 #arxiv-2001.02943 #ar... |
token-classification | transformers |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model.... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Mijn naam is RobBERT en ik ben een taalm... | pdelobelle/robbert-v2-dutch-ner | null | [
"transformers",
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"jax",
"roberta",
"token-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #Dutch #Flemish #RoBERTa #RobBERT #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
<p align="center">
<img src="URL alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any... | [
"# RobBERT: Dutch RoBERTa-based Language Model.\n\nRobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many r... | [
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"# RobBERT: Dutch RoBERTa-based Language Model.\n\nRobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Du... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | pdroberts/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
text-generation | transformers |
# Morty DialoGPT Model | {"tags": ["conversational"]} | peamjo/DialoGPT-small-morty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Morty DialoGPT Model | [
"# Morty DialoGPT Model"
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"# Morty DialoGPT Model"
] |
text-generation | transformers | # Exo-Machina
A deep language model, GPT-2, is trained on scientific manuscripts from NASA's Astrophysical Data System pertaining to extrasolar planets and the references therein. This pilot study uses the abstracts of each article as training data in order to explore correlations in scientific literature from a langu... | {} | pearsonkyle/gpt2-exomachina | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Exo-Machina
A deep language model, GPT-2, is trained on scientific manuscripts from NASA's Astrophysical Data System pertaining to extrasolar planets and the references therein. This pilot study uses the abstracts of each article as training data in order to explore correlations in scientific literature from a langu... | [
"# Exo-Machina\r\nA deep language model, GPT-2, is trained on scientific manuscripts from NASA's Astrophysical Data System pertaining to extrasolar planets and the references therein. This pilot study uses the abstracts of each article as training data in order to explore correlations in scientific literature from ... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Exo-Machina\r\nA deep language model, GPT-2, is trained on scientific manuscripts from NASA's Astrophysical Data System pertaining to extrasolar planets and the refer... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 504313966
- CO2 Emissions (in grams): 12.994518654810642
## Validation Metrics
- Loss: 0.19673296809196472
- Accuracy: 0.9398032027783138
- Precision: 0.9133115705476967
- Recall: 0.9718255499807025
- AUC: 0.985316873222122
- F1: 0.9416... | {"language": "unk", "tags": "autonlp", "datasets": ["pediberto/autonlp-data-testing"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 12.994518654810642} | pediberto/autonlp-testing-504313966 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-pediberto/autonlp-data-testing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 504313966
- CO2 Emissions (in grams): 12.994518654810642
## Validation Metrics
- Loss: 0.19673296809196472
- Accuracy: 0.9398032027783138
- Precision: 0.9133115705476967
- Recall: 0.9718255499807025
- AUC: 0.985316873222122
- F1: 0.9416... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 504313966\n- CO2 Emissions (in grams): 12.994518654810642",
"## Validation Metrics\n\n- Loss: 0.19673296809196472\n- Accuracy: 0.9398032027783138\n- Precision: 0.9133115705476967\n- Recall: 0.9718255499807025\n- AUC: 0.9853168732... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-pediberto/autonlp-data-testing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 504313966\n- CO2 Emissions (in grams)... |
translation | transformers |
# DeUnCaser
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "widget": [{"text": "moscow says deployments in eastern europe increase tensions nato says russia has moved troops to belarus"}, {"text": "dette er en liten test som er laget av per egil kummervold han er en forsker som tidligere jobbet ved nasjonalbi... | pere/DeUnCaser | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DeUnCaser
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this... | [
"# DeUnCaser\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text. \r\n\r\nThe DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process. It adds punctation, and capitalises the correct words. In some langu... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DeUnCaser\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This doe... |
summarization | transformers | # Demo model
Currently this is just a demo page but there will come a real model here soon. | {"language": [false, "en"], "tags": ["summarization"]} | pere/summary-v1 | null | [
"transformers",
"pytorch",
"summarization",
"no",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no",
"en"
] | TAGS
#transformers #pytorch #summarization #no #en #endpoints_compatible #region-us
| # Demo model
Currently this is just a demo page but there will come a real model here soon. | [
"# Demo model\nCurrently this is just a demo page but there will come a real model here soon."
] | [
"TAGS\n#transformers #pytorch #summarization #no #en #endpoints_compatible #region-us \n",
"# Demo model\nCurrently this is just a demo page but there will come a real model here soon."
] |
text2text-generation | transformers | # RotoBART
## Running the script
### Script arguemnts
Available model config arguments from script:
```
encoder_layers
encoder_ffn_dim
decoder_layers
decoder_ffn_dim
d_model
vocab_size
max_position_embeddings
encoder_layerdrop
decoder_layerdrop
```
Training Arguments:
`testing` : only uses 1 batch, for testing the... | {} | pere/flax-bart-nb-nn | null | [
"transformers",
"jax",
"tensorboard",
"RotoBART",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #RotoBART #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # RotoBART
## Running the script
### Script arguemnts
Available model config arguments from script:
Training Arguments:
'testing' : only uses 1 batch, for testing the script
'adafactor': will enable adafactor, removing the command will revert to Adam
'grad_accum': what value for gradient accumulation to use, de... | [
"# RotoBART",
"## Running the script",
"### Script arguemnts\n\nAvailable model config arguments from script:\n\n\nTraining Arguments:\n\n'testing' : only uses 1 batch, for testing the script\n\n'adafactor': will enable adafactor, removing the command will revert to Adam\n\n'grad_accum': what value for gradient... | [
"TAGS\n#transformers #jax #tensorboard #RotoBART #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# RotoBART",
"## Running the script",
"### Script arguemnts\n\nAvailable model config arguments from script:\n\n\nTraining Arguments:\n\n'testing' : only uses 1 batch, for testin... |
null | null | # Multi-Lingual DeUnCaser - Base byT5 Version
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct wor... | {"license": "cc"} | pere/multi-sentencefix-byt5 | null | [
"license:cc",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-cc #region-us
| # Multi-Lingual DeUnCaser - Base byT5 Version
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct wor... | [
"# Multi-Lingual DeUnCaser - Base byT5 Version\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.\r\n\r\nThe DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the ... | [
"TAGS\n#license-cc #region-us \n",
"# Multi-Lingual DeUnCaser - Base byT5 Version\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.\r\n\r\nThe DeUnCaser is a sequence-to-sequence model that is reversing this process. It ... |
null | null | # Multi-Lingual DeUnCaser - Base mT5 Version
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct word... | {"license": "cc"} | pere/multi-sentencefix-mt5 | null | [
"license:cc",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-cc #region-us
| # Multi-Lingual DeUnCaser - Base mT5 Version
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct word... | [
"# Multi-Lingual DeUnCaser - Base mT5 Version\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.\r\n\r\nThe DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the c... | [
"TAGS\n#license-cc #region-us \n",
"# Multi-Lingual DeUnCaser - Base mT5 Version\r\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.\r\n\r\nThe DeUnCaser is a sequence-to-sequence model that is reversing this process. It a... |
translation | transformers | # Norwegian mT5 - Translation Bokmål Nynorsk - Development
## Description
This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead.
Here is an example of how to use the model from Pyth... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "datasets": ["oscar"], "widget": [{"text": "Skriv inn en tekst som du \u00f8nsker \u00e5 oversette til en annen m\u00e5lform."}]} | pere/nb-nn-dev | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"translation",
"no",
"dataset:oscar",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #region-us
| # Norwegian mT5 - Translation Bokmål Nynorsk - Development
## Description
This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run this version instead.
Here is an example of how to use the model from Python
Or if you like to use the pipeline instead
| [
"# Norwegian mT5 - Translation Bokmål Nynorsk - Development",
"## Description\n\nThis is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run this version instead.\n\n\nHere is an example of how to use the model from Python\n\n\nOr if you like to use the pi... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Norwegian mT5 - Translation Bokmål Nynorsk - Development",
"## Description\n\nThis is the development version of the Bokmål-Nynorsk translator. If you want something that i... |
translation | transformers | # Norwegian T5 - Translation Bokmål Nynorsk - Development
## Description
This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead.
Here is an example of how to use the model from Pytho... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "datasets": ["oscar"], "widget": [{"text": "Skriv inn en tekst som du \u00f8nsker \u00e5 oversette til en annen m\u00e5lform."}]} | pere/nb-nn-dev2 | null | [
"transformers",
"pytorch",
"jax",
"translation",
"no",
"dataset:oscar",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #region-us
| # Norwegian T5 - Translation Bokmål Nynorsk - Development
## Description
This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run this version instead.
Here is an example of how to use the model from Python
Or if you like to use the pipeline instead
| [
"# Norwegian T5 - Translation Bokmål Nynorsk - Development",
"## Description\n\nThis is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run this version instead.\n\n\nHere is an example of how to use the model from Python\n\n\nOr if you like to use the pip... | [
"TAGS\n#transformers #pytorch #jax #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Norwegian T5 - Translation Bokmål Nynorsk - Development",
"## Description\n\nThis is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Plea... |
translation | transformers |
# 🇳🇴 Bokmål ⇔ Nynorsk 🇳🇴
Norwegian has two relatively similar written languages; Bokmål and Nynorsk. Historically Nynorsk is a written norm based on dialects curated by the linguist Ivar Aasen in the mid-to-late 1800s, whereas Bokmål is a gradual 'Norwegization' of written Danish.
The two written languages are ... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "datasets": ["oscar"], "widget": [{"text": "Skriv inn en tekst som du \u00f8nsker \u00e5 oversette til en annen m\u00e5lform."}]} | pere/nb-nn-translation | null | [
"transformers",
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"jax",
"translation",
"no",
"dataset:oscar",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
| 🇳🇴 Bokmål ⇔ Nynorsk 🇳🇴
======================
Norwegian has two relatively similar written languages; Bokmål and Nynorsk. Historically Nynorsk is a written norm based on dialects curated by the linguist Ivar Aasen in the mid-to-late 1800s, whereas Bokmål is a gradual 'Norwegization' of written Danish.
The two wri... | [] | [
"TAGS\n#transformers #pytorch #jax #translation #no #dataset-oscar #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | # This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[https://huggingface.co/datasets/NbAiLab/scandinavian].
## Train for 180k steps for 128 sequences:
```bash
./run_mlm_flax_stream.py \
--o... | {} | pere/nb-roberta-base-scandinavian-long | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL
## Train for 180k steps for 128 sequences:
## Train for 20k steps for 512 sequences:
Approximate additional training time: 1 week.
... | [
"# This is just a Test Model. Do NOT use for anything! \n\nContinued pretrained from the nb-roberta-base.\n\nThe domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL",
"## Train for 180k steps for 128 sequences:",
"## Train for 20k steps for 512 sequences:\n\n\n\n\nApproximate additional t... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is just a Test Model. Do NOT use for anything! \n\nContinued pretrained from the nb-roberta-base.\n\nThe domain specific pretraining is done on the 102GB (Scandinavian corpus)[UR... |
text-generation | transformers |
# Norwegian GPT-2 - Social
## Description
Experimental Norwegian GPT-2-model trained on a 37GB mainly social corpus.
The following sub-corpora are used:
```bash
wikipedia_download_nb.jsonl
wikipedia_download_nn.jsonl
newspapers_online_nb.jsonl
newspapers_online_nn.jsonl
twitter_2016_2018_no.jsonl
twitter_news_2016_2... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "GPT2", "casual language modeling"]} | pere/norwegian-gpt2-social | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"norwegian",
"GPT2",
"casual language modeling",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #norwegian #GPT2 #casual language modeling #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Norwegian GPT-2 - Social
## Description
Experimental Norwegian GPT-2-model trained on a 37GB mainly social corpus.
The following sub-corpora are used:
| [
"# Norwegian GPT-2 - Social",
"## Description\nExperimental Norwegian GPT-2-model trained on a 37GB mainly social corpus.\n\nThe following sub-corpora are used:"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #norwegian #GPT2 #casual language modeling #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Norwegian GPT-2 - Social",
"## Description\nExperimental Norwegian GPT-2-m... |
text-generation | transformers |
# Norwegian GPT-2 - Social
## Description
Private test of gpt fine-tuning based on vgd.
The following sub-corpora are used for the base model:
```bash
wikipedia_download_nb.jsonl
wikipedia_download_nn.jsonl
newspapers_online_nb.jsonl
newspapers_online_nn.jsonl
twitter_2016_2018_no.jsonl
twitter_news_2016_2018_no.jso... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "GPT2", "casual language modeling"]} | pere/norwegian-gpt2-vgd | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
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"text-generation",
"norwegian",
"GPT2",
"casual language modeling",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #norwegian #GPT2 #casual language modeling #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Norwegian GPT-2 - Social
## Description
Private test of gpt fine-tuning based on vgd.
The following sub-corpora are used for the base model:
Finetuned on the private dataset located at NbAiLab/vgd.
| [
"# Norwegian GPT-2 - Social",
"## Description\nPrivate test of gpt fine-tuning based on vgd.\n\nThe following sub-corpora are used for the base model:\n\n\nFinetuned on the private dataset located at NbAiLab/vgd."
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #norwegian #GPT2 #casual language modeling #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Norwegian GPT-2 - Social",
"## Description\nPrivate test of gpt fine-tuning based on ... |
text-generation | transformers |
# Norwegian GPT-2 - Oscar
## Description
This is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Pretrained model on Norwegian language using a causal language modeling (CLM) objective. | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "GPT2", "casual language modeling"], "datasets": ["oscar"]} | pere/norwegian-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
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"text-generation",
"norwegian",
"GPT2",
"casual language modeling",
"no",
"dataset:oscar",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #norwegian #GPT2 #casual language modeling #no #dataset-oscar #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Norwegian GPT-2 - Oscar
## Description
This is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Pretrained model on Norwegian language using a causal language modeling (CLM) objective. | [
"# Norwegian GPT-2 - Oscar",
"## Description\n\nThis is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Pretrained model on Norwegian language using a causal language modeling (CLM) objective."
] | [
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"# Norwegian GPT-2 - Oscar",
"## Description\n\nThis is a sample referenc... |
null | transformers | # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | {} | pere/norwegian-gptneo-blue-highlr | null | [
"transformers",
"jax",
"tensorboard",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #endpoints_compatible #region-us
| # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | [
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] | [
"TAGS\n#transformers #jax #tensorboard #endpoints_compatible #region-us \n",
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] |
text-generation | transformers | # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | {} | pere/norwegian-gptneo-blue | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | [
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] |
text-generation | transformers | # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | {} | pere/norwegian-gptneo-red-highlr | null | [
"transformers",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # Norwegian GTPNeo Blue.
The first Norwegian GPTNeo model. This one is trained only on a administrative corpus. | [
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] | [
"TAGS\n#transformers #jax #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Norwegian GTPNeo Blue.\nThe first Norwegian GPTNeo model. This one is trained only on a administrative corpus."
] |
text2text-generation | transformers | # 🇳🇴 Norwegian mT5 Base model 🇳🇴
This mT5-base model is trained from the mT5 checkpoint on a 19GB Balanced Bokmål-Nynorsk Corpus.
Parameters used in training:
```bash
python3 ./run_t5_mlm_flax_streaming.py
--model_name_or_path="./norwegian-t5-base"
--output_dir="./norwegian-t5-base"
--config_name=".... | {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00c3\u00a5l"]} | pere/norwegian-mt5 | null | [
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"seq2seq",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # 🇳🇴 Norwegian mT5 Base model 🇳🇴
This mT5-base model is trained from the mT5 checkpoint on a 19GB Balanced Bokmål-Nynorsk Corpus.
Parameters used in training:
| [
"# 🇳🇴 Norwegian mT5 Base model 🇳🇴\nThis mT5-base model is trained from the mT5 checkpoint on a 19GB Balanced Bokmål-Nynorsk Corpus.\n\nParameters used in training:"
] | [
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"# 🇳🇴 Norwegian mT5 Base model 🇳🇴\nThis mT5-base model is trained from the mT5 checkpoint on a 19GB Balanced Bokmål-Nynorsk C... |
fill-mask | transformers | Same as norwegian-roberta-base but with higher learning rate and batch size | {} | pere/norwegian-roberta-base-highlr-512 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Same as norwegian-roberta-base but with higher learning rate and batch size | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Same as norwegian-roberta-base but with higher learning rate and batch size | {} | pere/norwegian-roberta-base-highlr | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Same as norwegian-roberta-base but with higher learning rate and batch size | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
The following setting were used in training:
```bash
./run_t5_mlm_flax_streaming.py \
... | {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | pere/norwegian-t5-base-NCC-fast | null | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
The following setting were used in training:
| [
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.\n\n\n The following setting were used in training:"
] | [
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"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Co... |
null | transformers | # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
The following setting were used in training:
```bash
./run_t5_mlm_flax_streaming.py \
... | {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | pere/norwegian-t5-base-NCC-nb-nn | null | [
"transformers",
"jax",
"tensorboard",
"seq2seq",
"no",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #seq2seq #no #license-cc-by-4.0 #endpoints_compatible #region-us
| # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
The following setting were used in training:
| [
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.\n\n\n The following setting were used in training:"
] | [
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"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task befor... |
text2text-generation | transformers | # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
Currently the model is training. It is expected that it should be finished by the end of A... | {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | pere/norwegian-t5-base-NCC | null | [
"transformers",
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"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.
Currently the model is training. It is expected that it should be finished by the end of A... | [
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything.\n\nCurrently the model is training. It is expected that it should be finished by the... | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Co... |
text2text-generation | transformers | # 🇳🇴 Norwegian T5 Base model 🇳🇴
This T5-base model is trained from scratch on a 19GB Balanced Bokmål-Nynorsk Corpus.
Update: Due to disk space errors, the model had to be restarted July 20. It is currently still running.
Parameters used in training:
```bash
python3 ./run_t5_mlm_flax_streaming.py
--model_na... | {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | pere/norwegian-t5-base | null | [
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"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # 🇳🇴 Norwegian T5 Base model 🇳🇴
This T5-base model is trained from scratch on a 19GB Balanced Bokmål-Nynorsk Corpus.
Update: Due to disk space errors, the model had to be restarted July 20. It is currently still running.
Parameters used in training:
| [
"# 🇳🇴 Norwegian T5 Base model 🇳🇴 \nThis T5-base model is trained from scratch on a 19GB Balanced Bokmål-Nynorsk Corpus.\n\nUpdate: Due to disk space errors, the model had to be restarted July 20. It is currently still running.\n\nParameters used in training:"
] | [
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"# 🇳🇴 Norwegian T5 Base model 🇳🇴 \nThis T5-base model is trained from scratch on a 19GB Balanced Bokmål-Nynorsk Corpus.\n\nUp... |
text2text-generation | transformers |
# Norwegian T5 - small - Oscar
## Description
This is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Do not use this model as anything other than a simple reference point. | {"language": false, "license": "cc-by-4.0", "tags": ["summary"], "datasets": ["oscar"], "widget": [{"text": "translate Bokm\u00e5l to Nynorsk: Dette er en test!"}]} | pere/norwegian-t5 | null | [
"transformers",
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"tensorboard",
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"summary",
"no",
"dataset:oscar",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #summary #no #dataset-oscar #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Norwegian T5 - small - Oscar
## Description
This is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Do not use this model as anything other than a simple reference point. | [
"# Norwegian T5 - small - Oscar",
"## Description\n\nThis is a sample reference model trained only on the Oscar Corpus for a day on a TPU v3-8. Do not use this model as anything other than a simple reference point."
] | [
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"# Norwegian T5 - small - Oscar",
"## Description\n\nThis is a sample reference model trained only on th... |
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 [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | pere/xls-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8789
- Wer: 1.3456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8789\n- Wer: 1.3456",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB datase... |
text-classification | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
labels = ["entails", "contradicts", "neutra... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "parsbert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mbert-base-parsinlu-entailment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"entailment",
"parsbert",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #entailment #parsbert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #entailment #parsbert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for text... |
text-classification | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mbert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from typing import List
import torch
from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokeni... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mbert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg", "pipeline_tag": "text-classification"} | persiannlp/mbert-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"bert",
"multiple-choice",
"mbert",
"persian",
"farsi",
"text-classification",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #multiple-choice #mbert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mbert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mbert-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #multiple-choice #mbert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mbert-based model for mul... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "base"
model_name = f"persiannlp/... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "commonsenseqa", "arc", "openbookqa"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-arc-comqa-obqa-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:commonsenseqa",
"dataset:arc",
"dataset:openbookqa",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compat... | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "base"
model_name = f"persiannlp/... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "base"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-op... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-opus-translation_fa_en | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
which should give the following:
which should give the following:
Which should produce the following:
For more details, visit this page: URL ... | [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (Persian -> English). \nHere is an example of how you can run this model: \n\n\n\nwhich should give the following: \n\n\nwhich should give the following: \n\n\nWhich should produce the following: \n\n\n\nFor more details, ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-bas... |
text2text-generation | transformers |
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
```python
from transformers import MT5Config, MT5ForConditionalGeneration, MT5Tokenizer
model_name = "persiannlp/mt5-base-parsinlu-qqp-query-paraphras... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["query-paraphrasing", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "qqp"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-qqp-query-paraphrasing | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"query-paraphrasing",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:qqp",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:... | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)\n\nThis is a model for detection of paraphrased queries. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Detection of Paraphrased Queries (تشخصیص سوالات ... |
text2text-generation | transformers |
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
```python
import torch
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
import numpy as np
model_name_or_path = "persiannlp/mt5-base-parsinlu-sentiment-analysis"
to... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["sentiment", "sentiment-analysis", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-sentiment-analysis | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"sentiment",
"sentiment-analysis",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model for sentiment analysis.\nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model ... |
text2text-generation | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size="base"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-snli-entailme... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "snli"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-snli-entailment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"entailment",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:snli",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis ... |
text2text-generation | transformers | # Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "base"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-squad-reading... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["reading-comprehension", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "squad"], "metrics": ["f1"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-squad-reading-comprehension | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"reading-comprehension",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:squad",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
For more details, visit this page: URL | [
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\nThis is a mT5-based model for reading comprehension. \nHere is an example of how you can run this model: \n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\nT... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "base"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-tr... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-base-parsinlu-translation_en_fa | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
which should output:
For more details, visit this page: URL
| [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (English -> Persian). \nHere is an example of how you can run this model: \n\n\n\nwhich should output:\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model fo... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "large"
model_name = f"persiannlp... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "commonsenseqa", "arc", "openbookqa"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-arc-comqa-obqa-multiple-choice | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:commonsenseqa",
"dataset:arc",
"dataset:openbookqa",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple-Choi... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "large"
model_name = f"persiannlp... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "large"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-o... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-opus-translation_fa_en | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (Persian -> English). \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model fo... |
text2text-generation | transformers |
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
```python
from transformers import MT5Config, MT5ForConditionalGeneration, MT5Tokenizer
model_name = "persiannlp/mt5-large-parsinlu-qqp-query-paraphra... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["query-paraphrasing", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "qqp"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-qqp-query-paraphrasing | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"query-paraphrasing",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:qqp",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)\n\nThis is a model for detection of paraphrased queries. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Detection of Paraphrased Queries (تشخصیص سوالات هممع... |
text2text-generation | transformers |
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
```python
import torch
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
import numpy as np
model_name_or_path = "persiannlp/mt5-large-parsinlu-sentiment-analysis"
t... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["sentiment", "sentiment-analysis", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-sentiment-analysis | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"sentiment",
"sentiment-analysis",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model for sentiment analysis.\nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model ... |
text2text-generation | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size="large"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-snli-entailm... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "snli"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-snli-entailment | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"entailment",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:snli",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\n... |
text2text-generation | transformers | # Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "large"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-squad-readin... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["reading-comprehension", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "squad"], "metrics": ["f1"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-squad-reading-comprehension | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"reading-comprehension",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:squad",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
For more details, visit this page: URL | [
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\nThis is a mT5-based model for reading comprehension. \nHere is an example of how you can run this model: \n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\nT... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "large"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-t... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-large-parsinlu-translation_en_fa | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
which should output:
For more details, visit this page: URL
| [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (English -> Persian). \nHere is an example of how you can run this model: \n\n\nwhich should output: \n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-bas... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "small"
model_name = f"persiannlp... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "commonsenseqa", "arc", "openbookqa"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-arc-comqa-obqa-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:commonsenseqa",
"dataset:arc",
"dataset:openbookqa",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compat... | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple... |
multiple-choice | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "small"
model_name = f"persiannlp... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"multiple-choice",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a mT5-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a mT5-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جواب... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "small"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-o... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-opus-translation_fa_en | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (Persian -> English).
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (Persian -> English). \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-bas... |
text2text-generation | transformers |
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
```python
from transformers import MT5Config, MT5ForConditionalGeneration, MT5Tokenizer
model_name = "persiannlp/mt5-small-parsinlu-qqp-query-paraphra... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["query-paraphrasing", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "qqp"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-qqp-query-paraphrasing | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"query-paraphrasing",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:qqp",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detection of paraphrased queries.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)\n\nThis is a model for detection of paraphrased queries. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #query-paraphrasing #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-qqp #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Detection of Paraphrased Queries (تشخصیص سوالات هممع... |
text2text-generation | transformers |
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
```python
import torch
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
import numpy as np
model_name_or_path = "persiannlp/mt5-small-parsinlu-sentiment-analysis"
t... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["sentiment", "sentiment-analysis", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-sentiment-analysis | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"sentiment",
"sentiment-analysis",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analysis.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model for sentiment analysis.\nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #sentiment #sentiment-analysis #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sentiment Analysis (آنالیز احساسات)\n\nThis is a mT5 model ... |
text2text-generation | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size="small"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-snli-entailm... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "snli"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-snli-entailment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"entailment",
"mt5",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:snli",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #entailment #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-snli #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis ... |
text2text-generation | transformers |
# Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "small"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-squad-re... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["reading-comprehension", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "squad"], "metrics": ["f1"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-squad-reading-comprehension | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"reading-comprehension",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:squad",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Reading Comprehension (مدل برای پاسخ به درک مطلب)
This is a mT5-based model for reading comprehension.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\n\nThis is a mT5-based model for reading comprehension. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #reading-comprehension #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-squad #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Reading Comprehension (مدل برای پاسخ به درک مطلب)\n\... |
text2text-generation | transformers |
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
```python
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_size = "small"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-t... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["machine-translation", "mt5", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["sacrebleu"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/mt5-small-parsinlu-translation_en_fa | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"machine-translation",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Machine Translation (ترجمهی ماشینی)
This is an mT5-based model for machine translation (English -> Persian).
Here is an example of how you can run this model:
which should output:
For more details, visit this page: URL
| [
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model for machine translation (English -> Persian). \nHere is an example of how you can run this model: \n\n\nwhich should output: \n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #machine-translation #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Machine Translation (ترجمهی ماشینی)\n\nThis is an mT5-based model fo... |
text-classification | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
labels = ["entails", "contradicts", "neutra... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "parsbert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/parsbert-base-parsinlu-entailment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"entailment",
"parsbert",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #entailment #parsbert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #entailment #parsbert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailm... |
text-classification | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a parsbert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from typing import List
import torch
from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTok... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "parsbert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg", "pipeline_tag": "text-classification"} | persiannlp/parsbert-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"bert",
"multiple-choice",
"parsbert",
"persian",
"farsi",
"text-classification",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #multiple-choice #parsbert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a parsbert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL | [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a parsbert-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #multiple-choice #parsbert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a parsbert-based model f... |
text-classification | transformers |
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
labels = ["entails", "contradicts", "neutra... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["entailment", "wikibert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"} | persiannlp/wikibert-base-parsinlu-entailment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"entailment",
"wikibert",
"persian",
"farsi",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #entailment #wikibert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
For more details, visit this page: URL
| [
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailment problems. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #entailment #wikibert #persian #farsi #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Textual Entailment (مدل برای پاسخ به استلزام منطقی)\n\nThis is a model for textual entailm... |
text-classification | transformers |
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a wikibert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
```python
from typing import List
import torch
from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTok... | {"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "wikibert", "persian", "farsi"], "datasets": ["parsinlu"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg", "pipeline_tag": "text-classification"} | persiannlp/wikibert-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"jax",
"bert",
"multiple-choice",
"wikibert",
"persian",
"farsi",
"text-classification",
"fa",
"multilingual",
"dataset:parsinlu",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #multiple-choice #wikibert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)
This is a wikibert-based model for multiple-choice question answering.
Here is an example of how you can run this model:
For more details, visit this page: URL | [
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a wikibert-based model for multiple-choice question answering. \nHere is an example of how you can run this model: \n\n\n\n\nFor more details, visit this page: URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #multiple-choice #wikibert #persian #farsi #text-classification #fa #multilingual #dataset-parsinlu #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)\n\nThis is a wikibert-based model f... |
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