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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Ukrainian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian 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
T... | {"language": "uk", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Ukrainian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", ... | anton-l/wav2vec2-large-xlsr-53-ukrainian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Ukrainian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian 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 e... | [
"# Wav2Vec2-Large-XLSR-53-Ukrainian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian 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\nTh... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Ukrainian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian using the... |
null | null |
This is a standalone Turkish Wav2Vec2 tokenizer config intended for use with `run_speech_recognition_ctc_streaming.py` | {"license": "cc0-1.0"} | anton-l/wav2vec2-tokenizer-turkish | null | [
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-cc0-1.0 #region-us
|
This is a standalone Turkish Wav2Vec2 tokenizer config intended for use with 'run_speech_recognition_ctc_streaming.py' | [] | [
"TAGS\n#license-cc0-1.0 #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-xls-r-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-common_voice-tr-ft", "results": []}]} | anton-l/wav2vec2-xls-r-common_voice-tr-ft-100sh | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"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 #tr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-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: 0.5806
* Wer: 0.3998
* Cer: 0.1053
Model description
-----------------
More... | [
"### 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: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #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: 0.0005\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. -->
# wav2vec2-xls-r-common_voice-tr-ft-stream
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-common_voice-tr-ft-stream", "results": []}]} | anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"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 #tr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-common\_voice-tr-ft-stream
=========================================
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: 0.3519
* Wer: 0.2927
* Cer: 0.0694
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: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #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: 0.0005\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. -->
# wav2vec2-xls-r-common_voice-tr-ft-500sh
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-common_voice-tr-ft-500sh", "results": []}]} | anton-l/wav2vec2-xls-r-common_voice-tr-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"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 #tr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-common\_voice-tr-ft-500sh
========================================
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: 0.5794
* Wer: 0.4009
* Cer: 0.1032
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: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #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: 0.0005\n* train... |
question-answering | transformers |
# Italian Bert Base Uncased on Squad-it
## Model description
This model is the uncased base version of the italian BERT (which you may find at `dbmdz/bert-base-italian-uncased`) trained on the question answering task.
#### How to use
```python
from transformers import pipeline
nlp = pipeline('question-answering',... | {"language": "it", "widget": [{"text": "Quando nacque D'Annunzio?", "context": "D'Annunzio nacque nel 1863"}]} | antoniocappiello/bert-base-italian-uncased-squad-it | null | [
"transformers",
"pytorch",
"question-answering",
"it",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #question-answering #it #endpoints_compatible #has_space #region-us
| Italian Bert Base Uncased on Squad-it
=====================================
Model description
-----------------
This model is the uncased base version of the italian BERT (which you may find at 'dbmdz/bert-base-italian-uncased') trained on the question answering task.
#### How to use
Training data
-------------... | [
"#### How to use\n\n\nTraining data\n-------------\n\n\nIt has been trained on the question answering task using SQuAD-it, derived from the original SQuAD dataset and obtained through the semi-automatic translation of the SQuAD dataset in Italian.\n\n\nTraining procedure\n------------------\n\n\nEval Results\n-----... | [
"TAGS\n#transformers #pytorch #question-answering #it #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nTraining data\n-------------\n\n\nIt has been trained on the question answering task using SQuAD-it, derived from the original SQuAD dataset and obtained through the semi-automatic translat... |
question-answering | transformers |
# Question answering model for Estonian
This is a question answering model based on XLM-Roberta base model. It is fine-tuned subsequentially on:
1. English SQuAD v1.1
2. SQuAD v1.1 translated into Estonian
3. Small native Estonian dataset (800 samples)
The model has retained good multilingual properties and can be us... | {"tags": ["question-answering"], "datasets": ["squad", "anukaver/EstQA"]} | anukaver/xlm-roberta-est-qa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"dataset:squad",
"dataset:anukaver/EstQA",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #dataset-squad #dataset-anukaver/EstQA #endpoints_compatible #region-us
| Question answering model for Estonian
=====================================
This is a question answering model based on XLM-Roberta base model. It is fine-tuned subsequentially on:
1. English SQuAD v1.1
2. SQuAD v1.1 translated into Estonian
3. Small native Estonian dataset (800 samples)
The model has retained go... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #dataset-squad #dataset-anukaver/EstQA #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-as
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as", "results": [{"task": {"type": "automatic-speech-recogniti... | anuragshas/wav2vec2-large-xls-r-300m-as | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"as",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"as"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #as #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-as
============================
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: 1.9068
* Wer: 0.6679
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: 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 #hf-asr-leaderboard #robust-speech-event #as #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
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 - Bulgarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["bg"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Bulgarian", "results": [{"task... | anuragshas/wav2vec2-large-xls-r-300m-bg | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"bg",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #bg #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M - Bulgarian
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BG dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2473
* Wer: 0.3002
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #bg #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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 - Hausa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2... | {"language": ["ha"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "XLS-R-300M - Hausa", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | anuragshas/wav2vec2-large-xls-r-300m-ha-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ha"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #ha #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M - Hausa
==================
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: 0.6094
* Wer: 0.5234
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 13\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 #robust-speech-event #hf-asr-leaderboard #ha #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
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-hi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi", "results": []}]} | anuragshas/wav2vec2-large-xls-r-300m-hi | 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-hi
============================
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: 2.4156
* Wer: 0.7181
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: 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-xls-r-300m-mr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["mr"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-mr", "results": [{"task": {"type": "automatic-speech-recognition", "... | anuragshas/wav2vec2-large-xls-r-300m-mr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-mr
============================
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: 0.5479
* Wer: 0.5740
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: 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 #robust-speech-event #hf-asr-leaderboard #mr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
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-or
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["or"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-or", "results": [{"task": {"type": "automatic-speech-recogniti... | anuragshas/wav2vec2-large-xls-r-300m-or | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"or"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #or #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-or
============================
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: 1.6618
* Wer: 0.5166
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: 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 #robust-speech-event #hf-asr-leaderboard #or #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
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 - Punjabi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2ve... | {"language": ["pa"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "XLS-R-300M - Punjabi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | anuragshas/wav2vec2-large-xls-r-300m-pa-in | null | [
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"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #pa #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M - Punjabi
====================
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: 1.2548
* Wer: 0.5677
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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 #robust-speech-event #hf-asr-leaderboard #pa #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
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-ur-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"language": ["ur"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ur-cv8", "results": [{"task": {"type": "automatic-speech-recognition... | anuragshas/wav2vec2-large-xls-r-300m-ur-cv8 | null | [
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"license:apache-2.0",
"model-index",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-ur-cv8
================================
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: 1.1443
* Wer: 0.5677
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe followi... |
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-ur
This model is a fine-tuned version of [anuragshas/wav2vec2-large-xls-r-300m-ur](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ur", "results": []}]} | anuragshas/wav2vec2-large-xls-r-300m-ur | null | [
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"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-ur
============================
This model is a fine-tuned version of anuragshas/wav2vec2-large-xls-r-300m-ur on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0508
* Wer: 0.7328
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* ... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dhivehi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "dv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Dhivehi", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | anuragshas/wav2vec2-large-xlsr-53-dv | null | [
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"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.
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 as follows ... | [
"# Wav2Vec2-Large-XLSR-53-Dhivehi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be eva... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Dhivehi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Commo... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Sorbian, Upper
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sorbian, Upper using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The... | {"language": "hsb", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Sorbian, Upper", "results": [{"task": {"type": "automatic-speech-recognition"... | anuragshas/wav2vec2-large-xlsr-53-hsb | null | [
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| # Wav2Vec2-Large-XLSR-53-Sorbian, Upper
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Sorbian, Upper using the Common Voice.
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 evaluat... | [
"# Wav2Vec2-Large-XLSR-53-Sorbian, Upper\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sorbian, Upper using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe mo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Sorbian, Upper\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sorbian, Upper ... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Interlingua
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Interlingua using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "ia", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Interlingua", "results": [{"task": {"type": "automatic-speech-recognition", "n... | anuragshas/wav2vec2-large-xlsr-53-ia | null | [
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"license:apache-2.0",
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#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ia #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Interlingua
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using the Common Voice.
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 as ... | [
"# Wav2Vec2-Large-XLSR-53-Interlingua\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model ca... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ia #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Interlingua\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using t... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used d... | {"language": "or", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Odia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | anuragshas/wav2vec2-large-xlsr-53-odia | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"has_space",
"region:us"
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"or"
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| # Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the Common Voice.
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 as follows on the... | [
"# Wav2Vec2-Large-XLSR-53-Odia\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Odia\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the ... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Romansh Sursilv
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilv using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
T... | {"language": "rm-sursilv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Romansh Sursilv", "results": [{"task": {"type": "automatic-speech-reco... | anuragshas/wav2vec2-large-xlsr-53-rm-sursilv | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"rm-sursilv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Romansh Sursilv
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilv using the Common Voice.
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 evalu... | [
"# Wav2Vec2-Large-XLSR-53-Romansh Sursilv\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilv using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romansh Sursilv\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilv usi... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Romansh Vallader
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Vallader using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage... | {"language": "rm-vallader", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Romansh Vallader", "results": [{"task": {"type": "automatic-speech-re... | anuragshas/wav2vec2-large-xlsr-53-rm-vallader | null | [
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"xlsr-fine-tuning-week",
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"rm-vallader"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Romansh Vallader
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Vallader using the Common Voice.
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 eva... | [
"# Wav2Vec2-Large-XLSR-53-Romansh Vallader\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Vallader using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nTh... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romansh Vallader\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Vallader u... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used... | {"language": "sah", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Sakha", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | anuragshas/wav2vec2-large-xlsr-53-sah | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sah"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice.
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 as follows on t... | [
"# Wav2Vec2-Large-XLSR-53-Sakha\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluat... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Sakha\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common V... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (... | {"language": "te", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Telugu", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | anuragshas/wav2vec2-large-xlsr-53-telugu | null | [
"transformers",
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"automatic-speech-recognition",
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"te",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"te"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #te #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| # Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 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
Test Result: 44.98%
## Traini... | [
"# Wav2Vec2-Large-XLSR-53-Telugu\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\n\nTest Resu... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #te #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Telugu\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the O... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model c... | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Vietnamese", "results": [{"task": {"type": "automatic-speech-recognition", "na... | anuragshas/wav2vec2-large-xlsr-53-vietnamese | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice.
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 as fo... | [
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Assamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Assamese using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can b... | {"language": "as", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Assamese", "results": [{"task": {"type": "automatic-speech-recognition", "name... | anuragshas/wav2vec2-large-xlsr-as | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"as",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"as"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Assamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Assamese using the Common Voice.
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 as follow... | [
"# Wav2Vec2-Large-XLSR-53-Assamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Assamese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be e... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Assamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Assamese using the Com... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | anuragshas/wav2vec2-xls-r-1b-hi-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6780
* Wer: 0.3670
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\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 #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #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* ... |
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-1B - Hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "XLS-R-1B - Hindi", "res... | anuragshas/wav2vec2-xls-r-1b-hi-with-lm | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
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"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| XLS-R-1B - Hindi
================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6921
* Wer: 0.3547
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Traini... |
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-1b-hi-cv7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-1b-hi-cv... | anuragshas/wav2vec2-xls-r-1b-hi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
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"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-hi-cv7
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5878
* Wer: 0.3419
Model description
-----------------
More informatio... | [
"### 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: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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 - Latvian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2ve... | {"language": ["lv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Latvian", "results": [{"task":... | anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"lv",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"lv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #lv #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M - Latvian
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - LV dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1660
* Wer: 0.1705
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #lv #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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": ["mr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | anuragshas/wav2vec2-xls-r-300m-mr-cv8-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"mr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mr #dataset-common_voice #license-apache-2.0 #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 - MR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6693
* Wer: 0.5921
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: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mr #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* ... |
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 - Maltese
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2ve... | {"language": ["mt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "XLS-R-300M - Maltese", ... | anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mt",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mt #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M - Maltese
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1895
* Wer: 0.1984
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
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"### Training hyperpar... |
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": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | anuragshas/wav2vec2-xls-r-300m-pa-IN-cv8-with-lm | null | [
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"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
] | TAGS
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6864
* Wer: 0.6707
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: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
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 - Slovak
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec... | {"language": ["sk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Slovak", "results": [{"task": ... | anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm | null | [
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"license:apache-2.0",
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"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| XLS-R-300M - Slovak
===================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SK dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3067
* Wer: 0.2678
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
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"### Training hyperpar... |
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 - Slovenian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Slovenian", "results": [{"task... | anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm | null | [
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"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| XLS-R-300M - Slovenian
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SL dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2578
* Wer: 0.2273
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
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"### Training hyperpar... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "pa-IN", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Punjabi", "results": [{"task": {"type": "automatic-speech-recognition", "na... | anuragshas/wav2vec2-xlsr-53-pa-in | null | [
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"wav2vec2",
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"license:apache-2.0",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
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| # Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice.
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 as follows ... | [
"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be eva... | [
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"# Wav2Vec2-Large-XLSR-53-Punjabi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using 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-xlsr-53-rm-vallader-with-lm
This model is a fine-tuned version of [anuragshas/wav2vec2-large-xlsr-53-rm-vallader](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xlsr-53-rm-vallader-with-lm", "results": []}]} | anuragshas/wav2vec2-xlsr-53-rm-vallader-with-lm | null | [
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"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-xlsr-53-rm-vallader-with-lm
====================================
This model is a fine-tuned version of anuragshas/wav2vec2-large-xlsr-53-rm-vallader on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4552
* Wer: 0.3206
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used... | {"language": "ta", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Tamil", "results": [{"task": {"type": "automatic-speech-recognition", "name": ... | anuragshas/wav2vec2-xlsr-53-tamil | null | [
"transformers",
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"license:apache-2.0",
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"ta"
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| # Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tamil using the Common Voice.
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 as follows on t... | [
"# Wav2Vec2-Large-XLSR-53-Tamil\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tamil using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluat... | [
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"# Wav2Vec2-Large-XLSR-53-Tamil\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tamil using the Common Vo... |
text-generation | transformers |
# Chandler DialoGPT Model | {"tags": ["conversational"]} | anweasha/DialoGPT-small-Chandler | null | [
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"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
|
# Chandler DialoGPT Model | [
"# Chandler DialoGPT Model"
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] |
text-generation | transformers |
# Jake Peralta DialoGPT Model | {"tags": ["conversational"]} | anweasha/DialoGPT-small-Jake | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake Peralta DialoGPT Model | [
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"# Jake Peralta DialoGPT Model"
] |
translation | transformers |
## [google/t5-v1_1-small](google/t5-v1_1-small) model
### pretrained on [anzorq/kbd-ru-1.67M-temp](https://huggingface.co/datasets/anzorq/kbd-ru-1.67M-temp)
### fine-tuned on **17753** Russian-Kabardian word/sentence pairs
kbd text uses custom latin script for optimization reasons.
Translation input should start wit... | {"language": ["ru", "kbd"], "tags": ["translation"], "datasets": ["anzorq/kbd-ru-1.67M-temp", "17753 Russian-Kabardian pairs of text"], "widget": [{"text": "ru->kbd: \u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439.", "example_title": "\u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439."}, {"text": "ru->k... | anzorq/t5-v1_1-small-ru_kbd-cased | null | [
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"text2text-generation",
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"ru",
"kbd",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"kbd"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #ru #kbd #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## google/t5-v1_1-small model
### pretrained on anzorq/kbd-ru-1.67M-temp
### fine-tuned on 17753 Russian-Kabardian word/sentence pairs
kbd text uses custom latin script for optimization reasons.
Translation input should start with 'ru->kbd: '.
Tokenizer: T5 sentencepiece, char, cased. | [
"## google/t5-v1_1-small model",
"### pretrained on anzorq/kbd-ru-1.67M-temp",
"### fine-tuned on 17753 Russian-Kabardian word/sentence pairs\n\nkbd text uses custom latin script for optimization reasons.\n\nTranslation input should start with 'ru->kbd: '.\n\nTokenizer: T5 sentencepiece, char, cased."
] | [
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"### pretrained on anzorq/kbd-ru-1.67M-temp",
"### fine-tuned on 17753 Russian-Kabardian word/sentence pairs\n\n... |
fill-mask | transformers | # BERT L-10 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.0896... | {} | aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| # BERT L-10 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
## Training the model
''... | [
"# BERT L-10 H-512 fine-tuned on MLM (CORD-19 2020/06/16)\n\nBERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).",
"## Training th... | [
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question-answering | transformers |
# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0
BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1... | {"datasets": ["squad_v2"]} | aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"dataset:squad_v2",
"arxiv:1908.08962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #dataset-squad_v2 #arxiv-1908.08962 #endpoints_compatible #region-us
|
# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0
BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for ... | [
"# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0\n\nBERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tune... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #dataset-squad_v2 #arxiv-1908.08962 #endpoints_compatible #region-us \n",
"# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0\n\nBERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn ... |
fill-mask | transformers | # BERT L-2 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with [2 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-2_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962),... | {} | aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| # BERT L-2 H-512 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with 2 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
## Training the model
'''b... | [
"# BERT L-2 H-512 fine-tuned on MLM (CORD-19 2020/06/16)\n\nBERT model with 2 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).",
"## Training the ... | [
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"# BERT L-2 H-512 fine-tuned on MLM (CORD-19 2020/06/16)\n\nBERT model with 2 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the... |
fill-mask | transformers | # BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with [4 Transformer layers and hidden embedding of size 256](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962),... | {} | aodiniz/bert_uncased_L-4_H-256_A-4_cord19-200616 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| # BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)
BERT model with 4 Transformer layers and hidden embedding of size 256, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).
## Training the model
'''b... | [
"# BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)\n\nBERT model with 4 Transformer layers and hidden embedding of size 256, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).",
"## Training the ... | [
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"# BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)\n\nBERT model with 4 Transformer layers and hidden embedding of size 256, referenced in Well-Read Students Learn Better: On the... |
null | null | # Building a HuggingFace Transformer NLP Model
## Running this Repo
| {} | aogara/slai_transformer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Building a HuggingFace Transformer NLP Model
## Running this Repo
| [
"# Building a HuggingFace Transformer NLP Model",
"## Running this Repo"
] | [
"TAGS\n#region-us \n",
"# Building a HuggingFace Transformer NLP Model",
"## Running this Repo"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-new-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the xsum dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "my-new-model", "results": []}]} | aozorahime/my-new-model | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-xsum #license-apache-2.0 #endpoints_compatible #region-us
|
# my-new-model
This model is a fine-tuned version of bert-base-uncased on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The foll... | [
"# my-new-model\n\nThis model is a fine-tuned version of bert-base-uncased on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-xsum #license-apache-2.0 #endpoints_compatible #region-us \n",
"# my-new-model\n\nThis model is a fine-tuned version of bert-base-uncased on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Inten... |
text-generation | transformers |
# Aladdin Bot | {"tags": ["conversational"]} | aplnestrella/Aladdin-Bot | 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
|
# Aladdin Bot | [
"# Aladdin Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Aladdin Bot"
] |
text-to-image | transformers |
## DALL·E mini - Generate images from text
<img style="text-align:center; display:block;" src="https://raw.githubusercontent.com/borisdayma/dalle-mini/main/img/logo.png" width="200">
* [Technical Report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA)
* [Demo](https://huggingface.co/spac... | {"language": ["en"], "pipeline_tag": "text-to-image", "inference": false} | apol/dalle-mini | null | [
"transformers",
"jax",
"bart",
"text2text-generation",
"text-to-image",
"en",
"arxiv:1910.13461",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #jax #bart #text2text-generation #text-to-image #en #arxiv-1910.13461 #autotrain_compatible #region-us
|
## DALL·E mini - Generate images from text
<img style="text-align:center; display:block;" src="URL width="200">
* Technical Report
* Demo
### Model Description
This is an attempt to replicate OpenAI's DALL·E, a model capable of generating arbitrary images from a text prompt that describes the desired result.
!DA... | [
"## DALL·E mini - Generate images from text\n\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"200\">\n\n* Technical Report\n* Demo",
"### Model Description\n\nThis is an attempt to replicate OpenAI's DALL·E, a model capable of generating arbitrary images from a text prompt that describes the d... | [
"TAGS\n#transformers #jax #bart #text2text-generation #text-to-image #en #arxiv-1910.13461 #autotrain_compatible #region-us \n",
"## DALL·E mini - Generate images from text\n\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"200\">\n\n* Technical Report\n* Demo",
"### Model Description\n\nThis... |
null | null | hello
| {} | apoorvumang/kgt5-test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello
| [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | This is a t5-small model trained from scratch on WikiKG90Mv2 dataset. Please see https://github.com/apoorvumang/kgt5/ for more details on the method.
This model was trained on the tail entity prediction task ie. given subject entity and relation, predict the object entity. Input should be provided in the form of "\... | {"license": "mit", "widget": [{"text": "Apoorv Umang Saxena| family name", "example_title": "Family name prediction"}, {"text": "Apoorv Saxena| country", "example_title": "Country prediction"}, {"text": "World War 2| followed by", "example_title": "followed by"}]} | apoorvumang/kgt5-wikikg90mv2 | null | [
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"tf",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a t5-small model trained from scratch on WikiKG90Mv2 dataset. Please see URL for more details on the method.
This model was trained on the tail entity prediction task ie. given subject entity and relation, predict the object entity. Input should be provided in the form of "\<entity text\>| \<relation text\>... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | 1 | {} | app-test-user/test-tensorboard | null | [
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #region-us
| 1 | [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text-generation | transformers |
# DialoGPT-medium-simpsons
This is a version of [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) fine-tuned on The Simpsons scripts. | {"tags": ["conversational"]} | arampacha/DialoGPT-medium-simpsons | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DialoGPT-medium-simpsons
This is a version of DialoGPT-medium fine-tuned on The Simpsons scripts. | [
"# DialoGPT-medium-simpsons\n\nThis is a version of DialoGPT-medium fine-tuned on The Simpsons scripts."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT-medium-simpsons\n\nThis is a version of DialoGPT-medium fine-tuned on The Simpsons scripts."
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Chech
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech 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... | {"language": "cs", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "metrics": "wer", "dataset": "common_voice", "model-index": [{"name": "Czech XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognitio... | arampacha/wav2vec2-large-xlsr-czech | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"cs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cs #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Chech
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech 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... | [
"# Wav2Vec2-Large-XLSR-53-Chech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech 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 ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cs #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Chech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.\nWhen u... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Ukrainian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice) and sample of [M-AILABS Ukrainian Corpus](https://www.caito.de/2019/01/the-m-ailabs-speech-dataset... | {"language": "uk", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "metrics": "wer", "dataset": "common_voice", "model-index": [{"name": "Ukrainian XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogn... | arampacha/wav2vec2-large-xlsr-ukrainian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"uk",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Ukrainian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian using the Common Voice and sample of M-AILABS Ukrainian Corpus datasets.
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 foll... | [
"# Wav2Vec2-Large-XLSR-53-Ukrainian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian using the Common Voice and sample of M-AILABS Ukrainian Corpus datasets.\n\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 ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Ukrainian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Ukrainian using the Common Voice and samp... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the M... | {"language": ["hy"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hy", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy-cv", "results": ... | arampacha/wav2vec2-xls-r-1b-hy-cv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
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"robust-speech-event",
"hy",
"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 | [] | [
"hy"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hy #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-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HY-AM dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4521
* Wer: 0.5141
* Cer: 0.1100
* Wer+LM: 0.2756
* Cer+LM: 0.0866
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilon... | [
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"### 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /W... | {"language": ["hy"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hy", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy-cv", "results": [{"task": {"type": "aut... | arampacha/wav2vec2-xls-r-1b-hy | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
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"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hy"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hy #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/HY/NOIZY\_STUDENT\_4/ - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1693
* Wer: 0.2373
* Cer: 0.0429
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 842\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hy #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\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-xls-r-1b-ka
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"language": ["ka"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-1b-ka", "results": [{"task": {"type": "automatic-sp... | arampacha/wav2vec2-xls-r-1b-ka | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"ka",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ka"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-ka
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/KA/NOIZY\_STUDENT\_2/ - KA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1022
* Wer: 0.1527
* Cer: 0.0221
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy-cv", "results": [{"tas... | arampacha/wav2vec2-xls-r-1b-uk-cv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"uk",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UK dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1747
* Wer: 0.2107
* Cer: 0.0408
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /W... | {"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy", "results": [{"task": {"type": "automatic-sp... | arampacha/wav2vec2-xls-r-1b-uk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/UK/COMPOSED\_DATASET/ - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1092
* Wer: 0.1752
* Cer: 0.0323
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperpa... |
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": ["hy-AM"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hy"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | arampacha/wav2vec2-xls-r-300m-hy-cv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hy",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hy-AM"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hy #dataset-common_voice #license-apache-2.0 #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 - HY-AM dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5891
* Wer: 0.6569
Note: If you aim for best performance use this model. It is trained using noizy student procedure a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hy"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hy", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-hy", "results": [{"task": {"type": "auto... | arampacha/wav2vec2-xls-r-300m-hy | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hy",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hy"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hy #hf-asr-leaderboard #dataset-common_voice #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 /WORKSPACE/DATA/HY/NOIZY\_STUDENT\_3/ - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2293
* Wer: 0.3333
* Cer: 0.0602
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 842\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.98) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hy #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\... |
question-answering | transformers | ---
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English ... | {} | aravind-812/roberta-train-json | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us
| ---
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English ... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | arawat/pegasus-custom-xsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# results
This model is a fine-tuned version of google/pegasus-large on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The foll... | [
"# results\n\nThis model is a fine-tuned version of google/pegasus-large on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# results\n\nThis model is a fine-tuned version of google/pegasus-large on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended use... |
text-generation | transformers |
#HourAI bot based on DialoGPT | {"tags": ["conversational"]} | archmagos/HourAI | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#HourAI bot based on DialoGPT | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Mini-Me | {"tags": ["conversational"]} | ardatasc/miniMe-version1 | 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
|
#Mini-Me | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro-dataset_20-input_64
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-dataset_20-input_64", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "typ... | aretw0/t5-small-finetuned-en-to-ro-dataset_20-input_64 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-dataset\_20-input\_64
=================================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4335
* Bleu: 8.6652
* Gen Len: 18.2596
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro-dataset_20
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-dataset_20", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt1... | aretw0/t5-small-finetuned-en-to-ro-dataset_20 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-dataset\_20
=======================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4052
* Bleu: 7.3293
* Gen Len: 18.2556
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro-epoch.04375
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-epoch.04375", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt... | aretw0/t5-small-finetuned-en-to-ro-epoch.04375 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-epoch.04375
=======================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4137
* Bleu: 7.3292
* Gen Len: 18.2541
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.04375\n* mixed\... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
feature-extraction | transformers | hello
| {} | argv947059/example-based-ner-bert | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# citizenlab/distilbert-base-multilingual-cased-toxicity
This is multilingual Distil-Bert model sequence classifier trained based on [JIGSAW Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
## How to use it
```python
from transformers import pi... | {"language": ["en", "nl", "fr", "pt", "it", "es", "de", "da", "pl", "af"], "datasets": ["jigsaw_toxicity_pred"], "metrics": ["F1 Accuracy"], "pipeline_type": "text-classification", "widget": [{"text": "this is a lovely message", "example_title": "Example 1", "multi_class": false}, {"text": "you are an idiot and you and... | citizenlab/distilbert-base-multilingual-cased-toxicity | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"nl",
"fr",
"pt",
"it",
"es",
"de",
"da",
"pl",
"af",
"dataset:jigsaw_toxicity_pred",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"nl",
"fr",
"pt",
"it",
"es",
"de",
"da",
"pl",
"af"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# citizenlab/distilbert-base-multilingual-cased-toxicity
This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset.
## How to use it
## Evaluation
### Accuracy
| [
"# citizenlab/distilbert-base-multilingual-cased-toxicity\n\nThis is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset.",
"## How to use it",
"## Evaluation",
"### Accuracy"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# citizenlab/distilbert-base-multilingual-cased-toxicity\n\nThis is multilingual Distil-Bert model sequence c... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | arianpasquali/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7751
* Accuracy: 0.9113
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-classification | transformers |
# citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
This is multilingual XLM-Roberta model sequence classifier fine tunned and based on [Cardiff NLP Group](cardiffnlp/twitter-roberta-base-sentiment) sentiment classification model.
## How to use it
```python
from transformers import pipeline
model_path = "ci... | {"language": ["en", "nl", "fr", "pt", "it", "es", "de", "da", "pl", "af"], "datasets": ["jigsaw_toxicity_pred"], "metrics": ["F1 Accuracy"], "pipeline_type": "text-classification", "widget": [{"text": "this is a lovely message", "example_title": "Example 1", "multi_class": false}, {"text": "you are an idiot and you and... | citizenlab/twitter-xlm-roberta-base-sentiment-finetunned | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"nl",
"fr",
"pt",
"it",
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"da",
"pl",
"af",
"dataset:jigsaw_toxicity_pred",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"nl",
"fr",
"pt",
"it",
"es",
"de",
"da",
"pl",
"af"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
This is multilingual XLM-Roberta model sequence classifier fine tunned and based on Cardiff NLP Group sentiment classification model.
## How to use it
## Evaluation
| [
"# citizenlab/twitter-xlm-roberta-base-sentiment-finetunned\n\nThis is multilingual XLM-Roberta model sequence classifier fine tunned and based on Cardiff NLP Group sentiment classification model.",
"## How to use it",
"## Evaluation"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# citizenlab/twitter-xlm-roberta-base-sentiment-finetunned\n\nThis is multilingual XLM-Roberta model sequenc... |
text-generation | transformers | # Rick DialoGPT Model | {"tags": ["conversational"]} | arifbhrn/DialogGPT-small-Rickk | 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
| # Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training.
Whe... | {"language": "Bengali", "license": "cc-by-sa-4.0", "tags": ["bn", "audio", "automatic-speech-recognition", "speech"], "datasets": ["OpenSLR"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Bengali by Arijit", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dat... | arijitx/wav2vec2-large-xlsr-bengali | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"audio",
"speech",
"dataset:OpenSLR",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Bengali"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #endpoints_compatible #has_space #region-us
| # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances from Bengali ASR training data set containing ~196K utterances. Tested WER using ~4200 held out from training.
When using this model, make sure that your speech input is sampled at 16kHz.
Train Script ca... | [
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances from Bengali ASR training data set containing ~196K utterances. Tested WER using ~4200 held out from training.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\nTrain S... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using a subset of 40,000 utterances ... |
automatic-speech-recognition | transformers | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- WER: 0.21726385291857586
- CER: 0.04725010353701041
With 5 gram langua... | {"language": ["bn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "bn", "hf-asr-leaderboard", "openslr_SLR53", "robust-speech-event"], "datasets": ["openslr", "SLR53", "AI4Bharat/IndicCorp"], "metrics": ["wer", "cer"], "model-index": [{"name": "arijitx/wav2vec2-xls-r-300m-bengali", "results": [{"ta... | arijitx/wav2vec2-xls-r-300m-bengali | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"hf-asr-leaderboard",
"openslr_SLR53",
"robust-speech-event",
"dataset:openslr",
"dataset:SLR53",
"dataset:AI4Bharat/IndicCorp",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"regio... | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #hf-asr-leaderboard #openslr_SLR53 #robust-speech-event #dataset-openslr #dataset-SLR53 #dataset-AI4Bharat/IndicCorp #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- WER: 0.21726385291857586
- CER: 0.04725010353701041
With 5 gram language model trained on 30M sentences randomly chosen from ... | [
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n\n- dataset_name=\"openslr\" \t\n- model_name_or_path=\"facebook/wav2vec2-xls-r-300m\" \t\n- dataset_config_name=\"SLR53\" \t\n- output_dir=\"./wav2vec2-xls-r-300m-bengali\" \t\n- overwrite_output_dir \t\n- num_train_epo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #hf-asr-leaderboard #openslr_SLR53 #robust-speech-event #dataset-openslr #dataset-SLR53 #dataset-AI4Bharat/IndicCorp #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\nThe foll... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-xsum
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wsj_markets"], "metrics": ["rouge"], "model_index": [{"name": "bart-large-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wsj_markets", "type": "wsj_markets... | aristotletan/bart-large-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:wsj_markets",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wsj_markets #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-finetuned-xsum
=========================
This model is a fine-tuned version of facebook/bart-large on the wsj\_markets dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8497
* Rouge1: 15.3934
* Rouge2: 7.0378
* Rougel: 13.9522
* Rougelsum: 14.3541
* Gen Len: 20.0
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wsj_markets #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sst2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sci... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scim"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-finetuned-sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "scim", "type": "scim", "args": "eod"}, "metric": {"name"... | aristotletan/roberta-base-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:scim",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-scim #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-sst2
===========================
This model is a fine-tuned version of roberta-base on the scim dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4632
* Accuracy: 0.9111
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-scim #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wsj_markets dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wsj_markets"], "metrics": ["rouge"], "model_index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wsj_markets", "type": "wsj_ma... | aristotletan/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wsj_markets",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wsj_markets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the wsj\_markets dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1447
* Rouge1: 10.4492
* Rouge2: 3.9563
* Rougel: 9.3368
* Rougelsum: 9.9828
* Gen Len: 19.0
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wsj_markets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 15892673
## Validation Metrics
- Loss: 1.3661842346191406
- Rouge1: 50.8868
- Rouge2: 26.996
- RougeL: 42.9088
- RougeLsum: 46.6748
- Gen Len: 20.716
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Beare... | {"language": "unk", "tags": "autonlp", "datasets": ["arjun3816/autonlp-data-pegas_large_samsum"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | arjun3816/autonlp-pegas_large_samsum-15892673 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:arjun3816/autonlp-data-pegas_large_samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-arjun3816/autonlp-data-pegas_large_samsum #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 15892673
## Validation Metrics
- Loss: 1.3661842346191406
- Rouge1: 50.8868
- Rouge2: 26.996
- RougeL: 42.9088
- RougeLsum: 46.6748
- Gen Len: 20.716
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 15892673",
"## Validation Metrics\n\n- Loss: 1.3661842346191406\n- Rouge1: 50.8868\n- Rouge2: 26.996\n- RougeL: 42.9088\n- RougeLsum: 46.6748\n- Gen Len: 20.716",
"## Usage\n\nYou can use cURL to access this model:"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-arjun3816/autonlp-data-pegas_large_samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 15892673",
"## Validation Metrics\n\n- Loss: 1.36... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 15492651
## Validation Metrics
- Loss: 1.4060134887695312
- Rouge1: 50.9953
- Rouge2: 35.9204
- RougeL: 43.5673
- RougeLsum: 46.445
- Gen Len: 58.0193
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bear... | {"language": "unk", "tags": "autonlp", "datasets": ["arjun3816/autonlp-data-sam_summarization1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | arjun3816/autonlp-sam_summarization1-15492651 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:arjun3816/autonlp-data-sam_summarization1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-arjun3816/autonlp-data-sam_summarization1 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 15492651
## Validation Metrics
- Loss: 1.4060134887695312
- Rouge1: 50.9953
- Rouge2: 35.9204
- RougeL: 43.5673
- RougeLsum: 46.445
- Gen Len: 58.0193
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 15492651",
"## Validation Metrics\n\n- Loss: 1.4060134887695312\n- Rouge1: 50.9953\n- Rouge2: 35.9204\n- RougeL: 43.5673\n- RougeLsum: 46.445\n- Gen Len: 58.0193",
"## Usage\n\nYou can use cURL to access this model:"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-arjun3816/autonlp-data-sam_summarization1 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 15492651",
"## Validation Metrics\n\n- Loss: 1.40... |
null | null |
# Noise2Recon
> **Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising**\
> Arjun Desai, Batu Ozturkler, Christopher Sandino, Shreyas Vasanawala, Brian Hargreaves, Christopher Ré, John Pauly, Akshay Chaudhari\
> https://arxiv.org/abs/2110.00075
This repository contains the artif... | {"language": "en", "license": "apache-2.0", "tags": ["mri", "reconstruction", "denoising"]} | arjundd/noise2recon-release | null | [
"mri",
"reconstruction",
"denoising",
"en",
"arxiv:2110.00075",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.00075"
] | [
"en"
] | TAGS
#mri #reconstruction #denoising #en #arxiv-2110.00075 #license-apache-2.0 #region-us
|
# Noise2Recon
> Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising\
> Arjun Desai, Batu Ozturkler, Christopher Sandino, Shreyas Vasanawala, Brian Hargreaves, Christopher Ré, John Pauly, Akshay Chaudhari\
> URL
This repository contains the artifacts for the Noise2Recon paper. T... | [
"# Noise2Recon\r\n\r\n> Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising\\\r\n> Arjun Desai, Batu Ozturkler, Christopher Sandino, Shreyas Vasanawala, Brian Hargreaves, Christopher Ré, John Pauly, Akshay Chaudhari\\\r\n> URL\r\n\r\nThis repository contains the artifacts for the Noi... | [
"TAGS\n#mri #reconstruction #denoising #en #arxiv-2110.00075 #license-apache-2.0 #region-us \n",
"# Noise2Recon\r\n\r\n> Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising\\\r\n> Arjun Desai, Batu Ozturkler, Christopher Sandino, Shreyas Vasanawala, Brian Hargreaves, Christopher Ré... |
null | null |
# VORTEX
<div align="center">
<img src="https://drive.google.com/uc?export=view&id=1q0jAm6Kg5ZhRg3h0w0ZbtIgcRF3_-Vgb" alt="Vortex Schematic" width="700px" />
</div>
> **VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Accelerated MRI Reconstruction**\
> Arjun Desai, Beliz Gun... | {"language": "en", "license": "apache-2.0", "tags": ["mri", "reconstruction", "artifact correction"]} | arjundd/vortex-release | null | [
"mri",
"reconstruction",
"artifact correction",
"en",
"arxiv:2111.02549",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.02549"
] | [
"en"
] | TAGS
#mri #reconstruction #artifact correction #en #arxiv-2111.02549 #license-apache-2.0 #region-us
|
# VORTEX
<div align="center">
<img src="URL alt="Vortex Schematic" width="700px" />
</div>
> VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Accelerated MRI Reconstruction\
> Arjun Desai, Beliz Gunel, Batu Ozturkler, Harris Beg, Shreyas Vasanawala, Brian Hargreaves, Christop... | [
"# VORTEX\r\n\r\n<div align=\"center\">\r\n <img src=\"URL alt=\"Vortex Schematic\" width=\"700px\" />\r\n</div>\r\n\r\n> VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Accelerated MRI Reconstruction\\\r\n> Arjun Desai, Beliz Gunel, Batu Ozturkler, Harris Beg, Shreyas Vasanawala, B... | [
"TAGS\n#mri #reconstruction #artifact correction #en #arxiv-2111.02549 #license-apache-2.0 #region-us \n",
"# VORTEX\r\n\r\n<div align=\"center\">\r\n <img src=\"URL alt=\"Vortex Schematic\" width=\"700px\" />\r\n</div>\r\n\r\n> VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Acce... |
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-multilingual-cased-sentiment-2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-multilingual-cased-sentiment-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_r... | arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"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-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-multilingual-cased-sentiment-2
This model is a fine-tuned version of distilbert-base-multilingual-cased on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5882
- Accuracy: 0.7614
- F1: 0.7614
## Model description
More information needed
## In... | [
"# distilbert-base-multilingual-cased-sentiment-2\n\nThis model is a fine-tuned version of distilbert-base-multilingual-cased on the amazon_reviews_multi dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5882\n- Accuracy: 0.7614\n- F1: 0.7614",
"## Model description\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-multilingual-cased-sentiment-2\n\nThis model is a fine-tuned version of ... |
fill-mask | transformers |
BERTweet-FA: A pre-trained language model for Persian (a.k.a Farsi) Tweets
---
BERTweet-FA is a transformer-based model trained on 20665964 Persian tweets. The model has been trained on the data only for 1 epoch (322906 steps), and yet it has the ability to recognize the meaning of most of the conversational sentence... | {"language": "fa", "license": "apache-2.0", "tags": ["BERTweet"], "widget": [{"text": "\u0627\u06cc\u0646 \u0628\u0648\u062f [MASK] \u0647\u0627\u06cc \u0645\u0627\u061f"}, {"text": "\u062f\u0627\u062f\u0627\u0686 \u062f\u0627\u0631\u06cc [MASK] \u0645\u06cc\u0632\u0646\u06cc"}, {"text": "\u0628\u0647 \u0639\u0644\u06c... | arm-on/BERTweet-FA | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"BERTweet",
"fa",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"fa"
] | TAGS
#transformers #pytorch #bert #fill-mask #BERTweet #fa #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERTweet-FA: A pre-trained language model for Persian (a.k.a Farsi) Tweets
--------------------------------------------------------------------------
BERTweet-FA is a transformer-based model trained on 20665964 Persian tweets. The model has been trained on the data only for 1 epoch (322906 steps), and yet it has the ... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #BERTweet #fa #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-xxlarge-v2-squad2-covid-qa-deepset
This model is a fine-tuned version of [mfeb/albert-xxlarge-v2-squad2](https://huggingf... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "albert-xxlarge-v2-squad2-covid-qa-deepset", "results": []}]} | armageddon/albert-xxlarge-v2-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
|
# albert-xxlarge-v2-squad2-covid-qa-deepset
This model is a fine-tuned version of mfeb/albert-xxlarge-v2-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Train... | [
"# albert-xxlarge-v2-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of mfeb/albert-xxlarge-v2-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us \n",
"# albert-xxlarge-v2-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of mfeb/albert-xxlarge-v2-squad2 on the covid_qa_deepset dataset.",
"#... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid_qa_analysis_bert_base_uncased_squad2
This model is a fine-tuned version of [twmkn9/bert-base-uncased-squad2](https://huggi... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "covid_qa_analysis_bert_base_uncased_squad2", "results": []}]} | armageddon/bert-base-uncased-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
|
# covid_qa_analysis_bert_base_uncased_squad2
This model is a fine-tuned version of twmkn9/bert-base-uncased-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# covid_qa_analysis_bert_base_uncased_squad2\n\nThis model is a fine-tuned version of twmkn9/bert-base-uncased-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us \n",
"# covid_qa_analysis_bert_base_uncased_squad2\n\nThis model is a fine-tuned version of twmkn9/bert-base-uncased-squad2 on the covid_qa_deepset dataset.",
"... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-squad2-covid-qa-deepset
This model is a fine-tuned version of [phiyodr/bert-large-finetuned-squad2](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "bert-large-uncased-squad2-covid-qa-deepset", "results": []}]} | armageddon/bert-large-uncased-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
|
# bert-large-uncased-squad2-covid-qa-deepset
This model is a fine-tuned version of phiyodr/bert-large-finetuned-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# bert-large-uncased-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of phiyodr/bert-large-finetuned-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us \n",
"# bert-large-uncased-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of phiyodr/bert-large-finetuned-squad2 on the covid_qa_deepset dataset.",... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid_qa_analysis_albert_base_squad_v2
This model is a fine-tuned version of [abhilash1910/albert-squad-v2](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "covid_qa_analysis_albert_base_squad_v2", "results": []}]} | armageddon/albert-squad-v2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us
|
# covid_qa_analysis_albert_base_squad_v2
This model is a fine-tuned version of abhilash1910/albert-squad-v2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# covid_qa_analysis_albert_base_squad_v2\n\nThis model is a fine-tuned version of abhilash1910/albert-squad-v2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us \n",
"# covid_qa_analysis_albert_base_squad_v2\n\nThis model is a fine-tuned version of abhilash1910/albert-squad-v2 on the covid_qa_deepset... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid_qa_analysis_roberta-base-squad2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "covid_qa_analysis_roberta-base-squad2", "results": []}]} | armageddon/roberta-base-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-cc-by-4.0 #endpoints_compatible #region-us
|
# covid_qa_analysis_roberta-base-squad2
This model is a fine-tuned version of deepset/roberta-base-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# covid_qa_analysis_roberta-base-squad2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# covid_qa_analysis_roberta-base-squad2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the covid_qa_deepset d... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid_qa_analysis_roberta-large-squad2
This model is a fine-tuned version of [deepset/roberta-large-squad2](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "covid_qa_analysis_roberta-large-squad2", "results": []}]} | armageddon/roberta-large-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
|
# covid_qa_analysis_roberta-large-squad2
This model is a fine-tuned version of deepset/roberta-large-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# covid_qa_analysis_roberta-large-squad2\n\nThis model is a fine-tuned version of deepset/roberta-large-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us \n",
"# covid_qa_analysis_roberta-large-squad2\n\nThis model is a fine-tuned version of deepset/roberta-large-squad2 on the covid_qa_deepset dataset.",
"## M... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-covid-qa-deepset
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](ht... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "distilbert-base-uncased-squad2-covid-qa-deepset", "results": []}]} | armageddon/distilbert-base-uncased-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-covid-qa-deepset
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ne... | [
"# distilbert-base-uncased-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the covid_qa_deeps... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-squad2-covid-qa-deepset
This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "electra-base-squad2-covid-qa-deepset", "results": []}]} | armageddon/electra-base-squad2-covid-qa-deepset | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-cc-by-4.0 #endpoints_compatible #region-us
|
# electra-base-squad2-covid-qa-deepset
This model is a fine-tuned version of deepset/electra-base-squad2 on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# electra-base-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of deepset/electra-base-squad2 on the covid_qa_deepset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# electra-base-squad2-covid-qa-deepset\n\nThis model is a fine-tuned version of deepset/electra-base-squad2 on the covid_qa_deepset da... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | arman0320/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8596
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text-generation | transformers | **A casual chatbot**
This is a dialogpt medium fine tuned to talk like Tony Stark, Currently its only trained upon the script of Iron man 3 | {"language": ["en"], "license": "MIT", "tags": ["conversational"]} | arnav7633/DialoGPT-medium-tony_stark | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A casual chatbot
This is a dialogpt medium fine tuned to talk like Tony Stark, Currently its only trained upon the script of Iron man 3 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
# Model description
**bert-base-uncased-kin** is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named En... | {"language": ["kin"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n\u2019u Rwanda, bushingiye nanone ku bufatanye hagati y\u2019imigabane ya Afurika n\u2019u B... | arnolfokam/bert-base-uncased-kin | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"kin",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kin"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
bert-base-uncased-kin is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research ... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Kinyarwandan corpus (kin) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precisio... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**bert-base-uncased-pcm** is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named Ent... | {"language": ["pcm"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."}]} | arnolfokam/bert-base-uncased-pcm | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pcm"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
bert-base-uncased-pcm is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research ... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**bert-base-uncased-swa** is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named Ent... | {"language": ["swa"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."}]} | arnolfokam/bert-base-uncased-swa | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"swa",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"swa"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
bert-base-uncased-swa is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research ... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (swa) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**mbert-base-uncased-kin** is a model based on the fine-tuned multilingual BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concer... | {"language": ["kin"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n\u2019u Rwanda, bushingiye nanone ku bufatanye hagati y\u2019imigabane ya Afurika n\u2019u B... | arnolfokam/mbert-base-uncased-kin | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"kin",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kin"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-kin is a model based on the fine-tuned multilingual BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Kinyarwandan corpus (kin) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precisio... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**mbert-base-uncased-ner-kin** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizati... | {"language": ["kin"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n\u2019u Rwanda, bushingiye nanone ku bufatanye hagati y\u2019imigabane ya Afurika n\u2019u B... | arnolfokam/mbert-base-uncased-ner-kin | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"kin",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kin"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-ner-kin is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Kinyarwandan corpus (kin) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precisio... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
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