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automatic-speech-recognition | transformers |
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](http... | {"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-300m-finnish-lm", "results... | aapot/wav2vec2-xlsr-300m-finnish-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [
"2111.09296"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
T... | [
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. Howe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the U... |
automatic-speech-recognition | transformers |
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://... | {"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-300m-finnish", "results": ... | aapot/wav2vec2-xlsr-300m-finnish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-02T23:29:05+00:00 | [
"2111.09296"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
N... | [
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. Howe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the U... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | aaraki/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8559\n- Bleu: 52.9456",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
automatic-speech-recognition | transformers | ---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
na... | {} | aasem/wav2vec2-xls-r-300m-Urdu | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| ---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
na... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | aashutosh2102/DialoGPT-smalll-harrypotter | 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
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
multiple-choice | 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. -->
# c4-aristo-roberta-large
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evalu... | {"metrics": ["accuracy"]} | abarbosa/c4-aristo-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #multiple-choice #endpoints_compatible #region-us
| c4-aristo-roberta-large
=======================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0332
* Accuracy: 0.7370
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam wi... | [
"TAGS\n#transformers #pytorch #roberta #multiple-choice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\... |
audio-classification | transformers |
**Context**
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
**What is the Shower Ideas concept?**
This is an app that detects when someone is taki... | {"tags": ["audio", "audio-classificaiton", "shower detection"], "datasets": ["SHD-2"], "metrics": ["Accuracy"]} | abdelhalim/Shower_Sound_Recognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"audio-classificaiton",
"shower detection",
"dataset:SHD-2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #audio #audio-classificaiton #shower detection #dataset-SHD-2 #endpoints_compatible #region-us
|
Context
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
What is the Shower Ideas concept?
This is an app that detects when someone is taking a sho... | [
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #audio #audio-classificaiton #shower detection #dataset-SHD-2 #endpoints_compatible #region-us \n",
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | abdelkader/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3038
* Accuracy: 0.9465
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
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",... | abdelkader/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: 0.7713
* Accuracy: 0.9174
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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | abdelkader/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2162
* Accuracy: 0.9215
* F1: 0.9216
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | abdelkader/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4844
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
fill-mask | transformers |
# Soraberta: Unsupervised Language Model Pre-training for Wolof
**bert-base-wolof** is pretrained bert-base model on wolof language .
## Soraberta models
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------: | :---: | :---: | :---: | :---: |
| `bert-base` |... | {"language": "wo", "tags": ["bert", "language-model", "wo", "wolof"]} | abdouaziiz/bert-base-wolof | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"language-model",
"wo",
"wolof",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"wo"
] | TAGS
#transformers #pytorch #bert #fill-mask #language-model #wo #wolof #autotrain_compatible #endpoints_compatible #region-us
| Soraberta: Unsupervised Language Model Pre-training for Wolof
=============================================================
bert-base-wolof is pretrained bert-base model on wolof language .
Soraberta models
----------------
Using Soraberta with Hugging Face's Transformers
----------------------------------------... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #language-model #wo #wolof #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Soraberta: Unsupervised Language Model Pre-training for Wolof
**Soraberta** is pretrained roberta-base model on wolof language . Roberta was introduced in [this paper](https://arxiv.org/abs/1907.11692)
## Soraberta models
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters ... | {"language": "wo", "tags": ["roberta", "language-model", "wo", "wolof"]} | abdouaziiz/soraberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"language-model",
"wo",
"wolof",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"wo"
] | TAGS
#transformers #pytorch #roberta #fill-mask #language-model #wo #wolof #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| Soraberta: Unsupervised Language Model Pre-training for Wolof
=============================================================
Soraberta is pretrained roberta-base model on wolof language . Roberta was introduced in this paper
Soraberta models
----------------
Using Soraberta with Hugging Face's Transformers
------... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #language-model #wo #wolof #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-wolof-lm
Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well repr... | {"license": "mit", "tags": ["automatic-speech-recognition", "asr", "pytorch", "wav2vec2", "wolof", "wo"]} | abdouaziiz/wav2vec2-xls-r-300m-wolof-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"asr",
"wolof",
"wo",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #asr #wolof #wo #license-mit #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-wolof-lm
============================
Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well represented, there are few resources in the field of Text en speech
In this sense we aim to bring our contribution to this, it is in this sense that enters this rep... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-4\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size : 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_sc... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #asr #wolof #wo #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-4\n* train\\_batch\\_size: 3\n* ev... |
token-classification | transformers |
# Arabic NER | {"language": "ar", "tags": ["ner", "ar", "classification"], "datasets": ["wikiann"], "pipeline_tag": "token-classification", "task_ids": ["named-entity-recognition"], "widget": [{"text": "\u0643\u0631\u064a\u0633\u062a\u064a\u0627\u0646\u0648 \u0631\u0648\u0646\u0627\u0644\u062f\u0648 \u064a\u0644\u0639\u0628 \u0645\u0... | abdusah/arabert-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ner",
"ar",
"classification",
"dataset:wikiann",
"doi:10.57967/hf/0271",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #token-classification #ner #ar #classification #dataset-wikiann #doi-10.57967/hf/0271 #autotrain_compatible #endpoints_compatible #region-us
|
# Arabic NER | [
"# Arabic NER"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #ner #ar #classification #dataset-wikiann #doi-10.57967/hf/0271 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Arabic NER"
] |
fill-mask | transformers |
## Dataset
English Bible Translation Dataset (https://www.kaggle.com/oswinrh/bible)
*NOTE:* It is `roberta-base` fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 `.csv` files mentioned above, which consist of around 5.5M words.
## Citation
If you use this model in your work, please add th... | {"language": "en", "tags": ["English", "Bible"], "dataset": ["English Bible Translation Dataset", {"Link": "https://www.kaggle.com/oswinrh/bible"}], "inference": false} | abhi1nandy2/Bible-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"English",
"Bible",
"en",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #English #Bible #en #autotrain_compatible #region-us
|
## Dataset
English Bible Translation Dataset (URL
*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.
If you use this model in your work, please add the following citation -
| [
"## Dataset \n\nEnglish Bible Translation Dataset (URL\n\n*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.\n\nIf you use this model in your work, please add the following citation -"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #English #Bible #en #autotrain_compatible #region-us \n",
"## Dataset \n\nEnglish Bible Translation Dataset (URL\n\n*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which cons... |
fill-mask | transformers |
Refer to https://aclanthology.org/2021.semeval-1.87/
## Citation
If you use this model in your work, please add the following citation -
```
@inproceedings{nandy-etal-2021-cs60075,
title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-tra... | {"language": ["English"], "tags": ["CRAFT", "roberta"], "datasets": ["CRAFT BioNLP Corpus"]} | abhi1nandy2/Craft-bionlp-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"CRAFT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #CRAFT #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL
If you use this model in your work, please add the following citation -
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #CRAFT #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website
## Citation
Please cite the work if you would like to use it.
```
@inproceedings{nandy-etal-2021-question-answering,
title = "Question Answering over Electronic Devic... | {"language": ["English"], "tags": ["EManuals", "customer support", "QA", "bert"]} | abhi1nandy2/EManuals_BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"EManuals",
"customer support",
"QA",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #bert #fill-mask #EManuals #customer support #QA #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL for the paper and URL for the project website
Please cite the work if you would like to use it.
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #EManuals #customer support #QA #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website
## Citation
Please cite the work if you would like to use it.
```
@inproceedings{nandy-etal-2021-question-answering,
title = "Question Answering over Electronic Devic... | {"language": ["English"], "tags": ["EManuals", "customer support", "QA", "roberta"]} | abhi1nandy2/EManuals_RoBERTa | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"EManuals",
"customer support",
"QA",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #EManuals #customer support #QA #endpoints_compatible #region-us
|
Refer to URL for the paper and URL for the project website
Please cite the work if you would like to use it.
| [] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #EManuals #customer support #QA #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
Refer to https://aclanthology.org/2021.semeval-1.87/
## Citation
If you use this model in your work, please add the following citation -
```
@inproceedings{nandy-etal-2021-cs60075,
title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-t... | {"language": ["English"], "tags": ["Europarl", "roberta"], "datasets": ["Europarl"]} | abhi1nandy2/Europarl-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"Europarl",
"dataset:Europarl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #Europarl #dataset-Europarl #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL
If you use this model in your work, please add the following citation -
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #Europarl #dataset-Europarl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
spanbert-large-cased fine-tuned for <b>"Adverse drug reaction"</b> and <b>"Drug"</b> span Extraction.
<b>Details of spanbert-large-cased:</b>
https://huggingface.co/SpanBERT/spanbert-large-cased
<b>Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset</b>
https://huggingface.co/dataset... | {"language": "en", "tags": ["spanbert"], "datasets": ["ade_corpus_v2"], "widget": [{"text": "Having fever after taking paracetamol.", "example_title": "NER"}, {"text": "Birth defects associated with thalidomide.", "example_title": "NER"}, {"text": "Deafness and kidney failure associated with gentamicin (an antibiotic).... | abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"spanbert",
"en",
"dataset:ade_corpus_v2",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #spanbert #en #dataset-ade_corpus_v2 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
spanbert-large-cased fine-tuned for <b>"Adverse drug reaction"</b> and <b>"Drug"</b> span Extraction.
<b>Details of spanbert-large-cased:</b>
URL
<b>Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset</b>
URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #spanbert #en #dataset-ade_corpus_v2 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers | # Dataset
---
---
datasets:
- covid_qa_deepset
---
---
Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset).
# Original Repository
Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabrah... | {} | abhijithneilabraham/longformer_covid_qa | null | [
"transformers",
"pytorch",
"longformer",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #longformer #question-answering #endpoints_compatible #region-us
| # Dataset
---
---
datasets:
- covid_qa_deepset
---
---
Covid 19 question answering data obtained from covid_qa_deepset.
# Original Repository
Repository for the fine tuning, inference and evaluation scripts can be found here.
# Model in action
| [
"# Dataset\n---\n---\n\ndatasets:\n- covid_qa_deepset\n---\n\n--- \nCovid 19 question answering data obtained from covid_qa_deepset.",
"# Original Repository\nRepository for the fine tuning, inference and evaluation scripts can be found here.",
"# Model in action"
] | [
"TAGS\n#transformers #pytorch #longformer #question-answering #endpoints_compatible #region-us \n",
"# Dataset\n---\n---\n\ndatasets:\n- covid_qa_deepset\n---\n\n--- \nCovid 19 question answering data obtained from covid_qa_deepset.",
"# Original Repository\nRepository for the fine tuning, inference and evaluat... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
fill-mask | transformers | ## German NER Albert Model For Token Classification
This is a trained Albert model for Token Classification in German ,[Germeval](https://sites.google.com/site/germeval2014ner/) and can be used for Inference.
## Model Specifications
- MAX_LENGTH=128
- MODEL='albert-base-v1'
- BATCH_SIZE=32
- NUM_EPOCHS=3
- SAVE_STE... | {} | abhilash1910/albert-german-ner | null | [
"transformers",
"tf",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## German NER Albert Model For Token Classification
This is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.
## Model Specifications
- MAX_LENGTH=128
- MODEL='albert-base-v1'
- BATCH_SIZE=32
- NUM_EPOCHS=3
- SAVE_STEPS=750
- SEED=1
- SAVE_STEPS = 100
- LOGGING_STEP... | [
"## German NER Albert Model For Token Classification\n\nThis is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.",
"## Model Specifications\n\n- MAX_LENGTH=128\n- MODEL='albert-base-v1'\n- BATCH_SIZE=32\n- NUM_EPOCHS=3\n- SAVE_STEPS=750\n- SEED=1\n- SAVE_STEPS = 1... | [
"TAGS\n#transformers #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## German NER Albert Model For Token Classification\n\nThis is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.",
"## Model Specifications\n\n- MAX_LENGTH=12... |
question-answering | transformers | # DistilBERT--SQuAD-v1
Training is done on the [SQuAD](https://huggingface.co/datasets/squad) dataset. The model can be accessed via [HuggingFace](https://huggingface.co/abhilash1910/distilbert-squadv1):
## Model Specifications
We have used the following parameters:
- Training Batch Size : 512
- Learning Rate : 3e... | {} | abhilash1910/distilbert-squadv1 | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
| # DistilBERT--SQuAD-v1
Training is done on the SQuAD dataset. The model can be accessed via HuggingFace:
## Model Specifications
We have used the following parameters:
- Training Batch Size : 512
- Learning Rate : 3e-5
- Training Epochs : 0.75
- Sequence Length : 384
- Stride : 128
## Usage Specifications
Th... | [
"# DistilBERT--SQuAD-v1\n\nTraining is done on the SQuAD dataset. The model can be accessed via HuggingFace:",
"## Model Specifications\n\nWe have used the following parameters:\n\n- Training Batch Size : 512\n- Learning Rate : 3e-5\n- Training Epochs : 0.75\n- Sequence Length : 384\n- Stride : 128",
"## Usage ... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n",
"# DistilBERT--SQuAD-v1\n\nTraining is done on the SQuAD dataset. The model can be accessed via HuggingFace:",
"## Model Specifications\n\nWe have used the following parameters:\n\n- Training Batch Size : 512\n- ... |
fill-mask | transformers | # Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found at :[Financial_Roberta]... | {"tags": ["finance"]} | abhilash1910/financial_roberta | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"fill-mask",
"finance",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #finance #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found at :Financial_Roberta
## Specifications
The corpus for training is taken from the... | [
"# Roberta Masked Language Model Trained On Financial Phrasebank Corpus \n\n\nThis is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.\nThe model is built using Huggingface transformers.\nThe model can be found at :Financial_Roberta",
"## Specifications\n\n\nThe corpus for training i... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #finance #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta Masked Language Model Trained On Financial Phrasebank Corpus \n\n\nThis is a Masked Language Model trained with Roberta on a Financial Phraseban... |
fill-mask | transformers | # Roberta Trained Model For Masked Language Model On French Corpus :robot:
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a small French News Corpus(Leipzig corpora).
The model is built using Huggingface transformers.
The model can be found at :[F... | {} | abhilash1910/french-roberta | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # Roberta Trained Model For Masked Language Model On French Corpus :robot:
This is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).
The model is built using Huggingface transformers.
The model can be found at :French-Roberta
## Specifications
The corpus for training is ... | [
"# Roberta Trained Model For Masked Language Model On French Corpus :robot:\n\n\nThis is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).\nThe model is built using Huggingface transformers.\nThe model can be found at :French-Roberta",
"## Specifications\n\n\nThe corpus ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta Trained Model For Masked Language Model On French Corpus :robot:\n\n\nThis is a Masked Language Model trained with Roberta on a small French News Corpus(Leip... |
text-generation | transformers | This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located [here](https://www.kaggle.com/danofer/sarcasm). | {"pipeline_tag": "conversational"} | abhiramtirumala/DialoGPT-sarcastic | 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
| This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located here. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validation Metrics
- Loss: 0.07081354409456253
- Accuracy: 0.9867109634551495
- Macro F1: 0.9859067529980614
- Micro F1: 0.9867109634551495
- Weighted F1: 0.9866417220968429... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-bbc-news-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.448567309047846} | abhishek/autonlp-bbc-news-classification-37229289 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-bbc-news-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-bbc-news-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validation Metrics
- Loss: 0.07081354409456253
- Accuracy: 0.9867109634551495
- Macro F1: 0.9859067529980614
- Micro F1: 0.9867109634551495
- Weighted F1: 0.9866417220968429... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37229289\n... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37249301
- CO2 Emissions (in grams): 1.9859980179658823
## Validation Metrics
- Loss: 0.06406362354755402
- Accuracy: 0.9833887043189369
- Macro F1: 0.9832763664701248
- Micro F1: 0.9833887043189369
- Weighted F1: 0.983328852882813... | {"language": "unk", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-bbc-roberta"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.9859980179658823} | abhishek/autonlp-bbc-roberta-37249301 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:abhishek/autonlp-data-bbc-roberta",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-bbc-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37249301
- CO2 Emissions (in grams): 1.9859980179658823
## Validation Metrics
- Loss: 0.06406362354755402
- Accuracy: 0.9833887043189369
- Macro F1: 0.9832763664701248
- Micro F1: 0.9833887043189369
- Weighted F1: 0.983328852882813... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37249301\n- CO2 Emissions (in grams): 1.9859980179658823",
"## Validation Metrics\n\n- Loss: 0.06406362354755402\n- Accuracy: 0.9833887043189369\n- Macro F1: 0.9832763664701248\n- Micro F1: 0.9833887043189369\n- Weighted F1:... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37249301\n- CO2 Emissions (in... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## Usage
You can use cURL to acces... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-ferd1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | abhishek/autonlp-ferd1-2652021 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-ferd1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-ferd1 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## Usage
You can use cURL to acces... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2652021",
"## Validation Metrics\n\n- Loss: 0.3934604227542877\n- Accuracy: 0.8411030860144452\n- Precision: 0.8201550387596899\n- Recall: 0.8076335877862595\n- AUC: 0.8946767157983608\n- F1: 0.8138461538461538",
"## Usage\n\nY... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-ferd1 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2652021",
"## Validation Metrics\n\n- Loss: 0.39346042... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## Usage
You can use cURL to access... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-fred2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | abhishek/autonlp-fred2-2682064 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-fred2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-abhishek/autonlp-data-fred2 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## Usage
You can use cURL to access... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2682064",
"## Validation Metrics\n\n- Loss: 0.4454168379306793\n- Accuracy: 0.8188976377952756\n- Precision: 0.8442028985507246\n- Recall: 0.7103658536585366\n- AUC: 0.8699702146791053\n- F1: 0.771523178807947",
"## Usage\n\nYo... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-abhishek/autonlp-data-fred2 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2682064",
"## Validation Metrics\n\n- Loss: 0.44541683793... |
automatic-speech-recognition | transformers |
# Model Trained Using AutoNLP
- Problem type: Speech Recognition
| {"language": {}, "tags": ["autonlp", "automatic-speech-recognition", "audio"]} | abhishek/autonlp-hindi-asr | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"autonlp",
"audio",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #autonlp #audio #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Speech Recognition
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Speech Recognition"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #autonlp #audio #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Speech Recognition"
] |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context":... | {"language": "hi", "tags": ["autonlp", "question-answering"], "datasets": ["abhishek/autonlp-data-hindi-question-answering"], "widget": [{"text": "\u00b4\u0938\u0924\u0940\u0936 \u0927\u0935\u0928 \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0915\u0947\u0902\u0926\u094d\u0930\u00b4 \u0915\u093f\u0938 \u0930\u093e... | abhishek/autonlp-hindi-question-answering-23865268 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"hi",
"dataset:abhishek/autonlp-data-hindi-question-answering",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- CO2 Emissions (in grams): 39.76330395590446",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- CO2 Emissions (in grams): 39.76330395590446... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics
- Loss: 0.20277436077594757
- Accuracy: 0.92604
- Precision: 0.9560674830864092
- Recall: 0.89312
- AUC: 0.9814625504000001
- F1: 0.9235223559581421
## Usage
... | {"language": "unk", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-imdb-roberta-base"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 25.894117734124272} | abhishek/autonlp-imdb-roberta-base-3662644 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:abhishek/autonlp-data-imdb-roberta-base",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-imdb-roberta-base #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics
- Loss: 0.20277436077594757
- Accuracy: 0.92604
- Precision: 0.9560674830864092
- Recall: 0.89312
- AUC: 0.9814625504000001
- F1: 0.9235223559581421
## Usage
... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 3662644\n- CO2 Emissions (in grams): 25.894117734124272",
"## Validation Metrics\n\n- Loss: 0.20277436077594757\n- Accuracy: 0.92604\n- Precision: 0.9560674830864092\n- Recall: 0.89312\n- AUC: 0.9814625504000001\n- F1: 0.92352235... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-imdb-roberta-base #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 3662644\n- CO2 Emissions (in... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- Recall: 0.8085539714867617
- AUC: 0.9088033420466026
- F1: 0.8194014447884417
## Usage
You can use cURL to access ... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-imdb_eval"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | abhishek/autonlp-imdb_eval-71421 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-imdb_eval",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-imdb_eval #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- Recall: 0.8085539714867617
- AUC: 0.9088033420466026
- F1: 0.8194014447884417
## Usage
You can use cURL to access ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 71421",
"## Validation Metrics\n\n- Loss: 0.4114699363708496\n- Accuracy: 0.8248248248248248\n- Precision: 0.8305439330543933\n- Recall: 0.8085539714867617\n- AUC: 0.9088033420466026\n- F1: 0.8194014447884417",
"## Usage\n\nYou... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-imdb_eval #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 71421",
"## Validation Metrics\n\n- Loss: 0.4114699... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219492302988
## Usage
You can use cURL to access this model:
```
$ curl... | {"language": "en", "tags": "autonlp", "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | abhishek/autonlp-imdb_sentiment_classification-31154 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autonlp",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autonlp #en #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219492302988
## Usage
You can use cURL to access this model:
Or Pytho... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 31154",
"## Validation Metrics\n\n- Loss: 0.19292379915714264\n- Accuracy: 0.9395\n- Precision: 0.9569557080474111\n- Recall: 0.9204\n- AUC: 0.9851040399999998\n- F1: 0.9383219492302988",
"## Usage\n\nYou can use cURL to access... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autonlp #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 31154",
"## Validation Metrics\n\n- Loss: 0.19292379915714264\n- Accuracy: 0.9395\n- Prec... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59362
## Validation Metrics
- Loss: 0.13092292845249176
- Accuracy: 0.9527127414314258
- Precision: 0.9634070704982427
- Recall: 0.9842171959602166
- AUC: 0.9667289746092403
- F1: 0.9737009564152002
## Usage
You can use cURL to access... | {"language": "ja", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-japanese-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | abhishek/autonlp-japanese-sentiment-59362 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:abhishek/autonlp-data-japanese-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59362
## Validation Metrics
- Loss: 0.13092292845249176
- Accuracy: 0.9527127414314258
- Precision: 0.9634070704982427
- Recall: 0.9842171959602166
- AUC: 0.9667289746092403
- F1: 0.9737009564152002
## Usage
You can use cURL to access... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59362",
"## Validation Metrics\n\n- Loss: 0.13092292845249176\n- Accuracy: 0.9527127414314258\n- Precision: 0.9634070704982427\n- Recall: 0.9842171959602166\n- AUC: 0.9667289746092403\n- F1: 0.9737009564152002",
"## Usage\n\nYo... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59362",
"## Validation Metrics\n\n- Loss: ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.9729688278823665
- Recall: 0.9744633462616643
- AUC: 0.9717333684823413
- F1: 0.9737155136027014
## Usage
You can use cURL to access... | {"language": "ja", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-japanese-sentiment"], "widget": [{"text": "\ud83e\udd17AutoNLP\u304c\u5927\u597d\u304d\u3067\u3059"}]} | abhishek/autonlp-japanese-sentiment-59363 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:abhishek/autonlp-data-japanese-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.9729688278823665
- Recall: 0.9744633462616643
- AUC: 0.9717333684823413
- F1: 0.9737155136027014
## Usage
You can use cURL to access... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59363",
"## Validation Metrics\n\n- Loss: 0.12651239335536957\n- Accuracy: 0.9532079853817648\n- Precision: 0.9729688278823665\n- Recall: 0.9744633462616643\n- AUC: 0.9717333684823413\n- F1: 0.9737155136027014",
"## Usage\n\nYo... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59363",
"## Validation Metrics\n\n- Loss: ... |
token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 3362554
- CO2 Emissions (in grams): 5.340540212393564
## Validation Metrics
- Loss: 0.14167872071266174
- Accuracy: 0.9587076867229332
- Precision: 0.7351351351351352
- Recall: 0.7923728813559322
- F1: 0.7626816212082591
## Usage
You can ... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-prodigy-10"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.340540212393564} | abhishek/autonlp-prodigy-10-3362554 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-prodigy-10",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autonlp #en #dataset-abhishek/autonlp-data-prodigy-10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 3362554
- CO2 Emissions (in grams): 5.340540212393564
## Validation Metrics
- Loss: 0.14167872071266174
- Accuracy: 0.9587076867229332
- Precision: 0.7351351351351352
- Recall: 0.7923728813559322
- F1: 0.7626816212082591
## Usage
You can ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 3362554\n- CO2 Emissions (in grams): 5.340540212393564",
"## Validation Metrics\n\n- Loss: 0.14167872071266174\n- Accuracy: 0.9587076867229332\n- Precision: 0.7351351351351352\n- Recall: 0.7923728813559322\n- F1: 0.7626816212082591",... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 3362554\n- CO2 Emissions (in grams): 5.340... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss: 0.08340361714363098
- Accuracy: 0.9688222161294113
- Precision: 0.9102096627164995
- Recall: 0.7692604006163328
- AUC: 0.9859340458715813
- F1: 0.8338... | {"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-toxic-new"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 30.684995819386277} | abhishek/autonlp-toxic-new-30516963 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-toxic-new",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-toxic-new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss: 0.08340361714363098
- Accuracy: 0.9688222161294113
- Precision: 0.9102096627164995
- Recall: 0.7692604006163328
- AUC: 0.9859340458715813
- F1: 0.8338... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 30516963\n- CO2 Emissions (in grams): 30.684995819386277",
"## Validation Metrics\n\n- Loss: 0.08340361714363098\n- Accuracy: 0.9688222161294113\n- Precision: 0.9102096627164995\n- Recall: 0.7692604006163328\n- AUC: 0.98593404587... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-toxic-new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 30516963\n- CO2 Emissions (in gram... |
question-answering | transformers |
# muril-large-chaii
This is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.
Our full solution can be found here:
| {"language": ["hi", "ta"], "tags": ["question-answering"], "widget": [{"text": "\u0905\u092d\u093f\u0937\u0947\u0915 \u0914\u0930 \u0909\u0926\u094d\u092d\u0935 \u0915\u094b \u0915\u094c\u0928 \u0938\u093e \u0938\u094d\u0925\u093e\u0928 \u092e\u093f\u0932\u093e?", "context": "kaggle \u0926\u094d\u0935\u093e\u0930\u093e... | abhishek/muril-large-chaii | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"hi",
"ta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"ta"
] | TAGS
#transformers #pytorch #bert #question-answering #hi #ta #endpoints_compatible #region-us
|
# muril-large-chaii
This is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.
Our full solution can be found here:
| [
"# muril-large-chaii\n\nThis is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.\nOur full solution can be found here:"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #hi #ta #endpoints_compatible #region-us \n",
"# muril-large-chaii\n\nThis is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.\nOur full solution can be found here:"
] |
text-generation | transformers |
# Emilybot DialoGPT Model | {"tags": ["conversational"]} | abhisht/DialoGPT-medium-Emilybot | 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
|
# Emilybot DialoGPT Model | [
"# Emilybot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Emilybot DialoGPT Model"
] |
text-generation | transformers | # GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
```python
pip install -r requirements.txt
```
## Model:
Pretr... | {"language": "ta", "datasets": ["oscar", "IndicNLP"], "widget": [{"text": "\u0b92\u0bb0\u0bc1 \u0b8a\u0bb0\u0bbf\u0bb2\u0bc7 \u0b92\u0bb0\u0bc1 \u0b95\u0bbe\u0b95\u0bcd\u0b95\u0bc8\u0b95\u0bcd\u0b95\u0bc1"}]} | abinayam/gpt-2-tamil | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ta",
"dataset:oscar",
"dataset:IndicNLP",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #ta #dataset-oscar #dataset-IndicNLP #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
## Model:
Pretrained model on Tamil language using a causal ... | [
"# GPT2-Tamil\n\nThis repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.",
"## Setup:\nTo setup the project, run the following command,",
"## Model:\nPretrained model on Tamil language ... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #ta #dataset-oscar #dataset-IndicNLP #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Tamil\n\nThis repository is created as part of the Flax/Jax community week by Huggingface. T... |
text-generation | transformers |
# Model v2 | {"tags": ["conversational"]} | abjbpi/DS_small | 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
|
# Model v2 | [
"# Model v2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model v2"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | abjbpi/Dwight_Schrute | 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
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
automatic-speech-recognition | transformers | Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
As capitalization is an important part of the German language (eg. Sie vs. sie).
I trained a model using ... | {} | abnerh/wav2vec2-xlsr-300m-german-truecase | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Fine-tuned facebook/wav2vec2-xls-r-300m on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
As capitalization is an important part of the German language (eg. Sie vs. sie).
I trained a model using a vocab that includes both lower case and upper case l... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
null | transformers | # Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya:
## Proposed Method:
<img src="data/proposed.png" height = "330" width ="760" >
The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by learning new token embeddings. All implementatio... | {} | abrhaleitela/TigXLNet | null | [
"transformers",
"pytorch",
"xlnet",
"arxiv:2006.07698",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.07698"
] | [] | TAGS
#transformers #pytorch #xlnet #arxiv-2006.07698 #endpoints_compatible #region-us
| Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya:
==============================================================================
Proposed Method:
----------------
The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by le... | [] | [
"TAGS\n#transformers #pytorch #xlnet #arxiv-2006.07698 #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS). | {} | accelotron/rugpt3-ficbook-bts | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS). | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#3PO | {"tags": ["conversational"]} | aced/DialoGPT-medium-3PO | 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
|
#3PO | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | https://www.guilded.gg/thisiscineplex/overview/news/XRz48Dr6
https://www.guilded.gg/FLIXmasGR/overview/news/7R0WorPy
https://www.guilded.gg/FLIXmasGR/overview/news/NyE5BPmy
https://www.guilded.gg/FLIXmasGR/overview/news/2l3Konal
https://www.guilded.gg/FLIXmasGR/overview/news/AykDjvVR
https://www.guilded.gg/FLIXmasGR/ov... | {} | activatepin/RC_News | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
| [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedi... | {} | activebus/BERT-DK_laptop | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedi... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Ele... |
fill-mask | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained ... | {} | activebus/BERT-DK_rest | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained ... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.\n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.\n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.",
"## Mo... |
fill-mask | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
`BERT-PT_*` addtionally uses SQuAD 1.1.
## Model Description
The original model is from ... | {} | activebus/BERT-PT_laptop | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.
'BERT-PT_*' addtionally uses SQuAD 1.1.
## Model Description
The original model is from ... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'. \n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original ... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Comp... |
fill-mask | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.
`BERT-PT_*` addtionally uses SQuAD 1.1.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipe... | {} | activebus/BERT-PT_rest | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.
'BERT-PT_*' addtionally uses SQuAD 1.1.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipe... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.\n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' train... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Y... |
null | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details.
`BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a sing... | {} | activebus/BERT-XD_Review | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
Please visit URL for details.
'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the same ratin... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \nPlease visit URL for details. \n\n'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the ... | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \nPlease visit URL for details. \n\n'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') langua... |
fill-mask | transformers | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews ... | {} | activebus/BERT_Review | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews ... | [
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') langua... |
feature-extraction | transformers |
This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)
## Usage
```python
from transformers import AutoTokenizer # Or BertTokenizer
from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads
from transformers import AutoMo... | {"language": ["pt"]} | adalbertojunior/distilbert-portuguese-cased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"pt",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #pt #endpoints_compatible #has_space #region-us
|
This model was distilled from BERTimbau
## Usage
You should fine tune it on your own data.
It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks. | [
"## Usage\n\n\nYou should fine tune it on your own data.\n\nIt can achieve accuracy up to 99% relative to the original BERTimbau in some tasks."
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #pt #endpoints_compatible #has_space #region-us \n",
"## Usage\n\n\nYou should fine tune it on your own data.\n\nIt can achieve accuracy up to 99% relative to the original BERTimbau in some tasks."
] |
null | transformers |
Image Captioning in Portuguese trained with ViT and GPT2
[DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese)
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | {"language": ["pt"]} | adalbertojunior/image_captioning_portuguese | null | [
"transformers",
"pytorch",
"jax",
"vision-encoder-decoder",
"pt",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #has_space #region-us
|
Image Captioning in Portuguese trained with ViT and GPT2
DEMO
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) | [] | [
"TAGS\n#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : adam.chellaoui@epfl.ch | {} | adam-chell/tweet-sentiment-analyzer | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : adam.chellaoui@URL | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, gr... | {"title": "Twitter Sentiments", "emoji": "\ud83d\ude0d", "colorFrom": "yellow", "colorTo": "blue", "sdk": "streamlit", "app_file": "app.py", "pinned": false} | adam3242/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, gr... | [
"# Configuration\r\n\r\n'title': _string_ \r\nDisplay title for the Space\r\n\r\n'emoji': _string_ \r\nSpace emoji (emoji-only character allowed)\r\n\r\n'colorFrom': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'colorTo': _string_ \r\nColor for Thumbnai... | [
"TAGS\n#region-us \n",
"# Configuration\r\n\r\n'title': _string_ \r\nDisplay title for the Space\r\n\r\n'emoji': _string_ \r\nSpace emoji (emoji-only character allowed)\r\n\r\n'colorFrom': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'colorTo': _string... |
null | null | dsfregrtgr | {} | adam3242/testing | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| dsfregrtgr | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("addy88/T5-23-emotions-detections")
tokenizer = T5Tokenizer.from_pretrained("addy88/T5-23-emotions-detections")
text_to_summarize="emo... | {} | addy88/T5-23-emotions-detections | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### How to use
Here is how to use this model in PyTorch:
| [
"### How to use\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
sentence-similarity | sentence-transformers |
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on [ELI5](https://huggingface.co/datasets/eli5)
<!--- Describe your model here -->
## Usage (Sentence-Transform... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | addy88/eli5-all-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us
|
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on ELI5
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can ... | [
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us \n",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like th... |
text-generation | transformers | This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [... | {} | addy88/gpt-j-8bit | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09685",
"2110.02861"
] | [] | TAGS
#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us
| This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: !... | [
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you wi... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to cons... |
text-generation | transformers | This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [... | {} | addy88/gptj8 | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09685",
"2110.02861"
] | [] | TAGS
#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us
| This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: !... | [
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you wi... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to cons... |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/hindi-wav2vec2-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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. -->
# hubert-base-timit-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-timit-demo-colab", "results": []}]} | addy88/hubert-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-base-timit-demo-colab
============================
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1092
* Wer: 0.1728
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\... |
image-classification | transformers | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("addy88/perceiver_image_classifier")
model = PerceiverForImage... | {} | addy88/perceiver_image_classifier | null | [
"transformers",
"pytorch",
"perceiver",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #perceiver #image-classification #autotrain_compatible #endpoints_compatible #region-us
| ### How to use
Here is how to use this model in PyTorch:
| [
"### How to use\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #perceiver #image-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
text-classification | transformers | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverTokenizer, PerceiverForMaskedLM
tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb")
model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb")
text = "This is an incomplete sentence where ... | {} | addy88/perceiver_imdb | null | [
"transformers",
"pytorch",
"perceiver",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #perceiver #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ### How to use
Here is how to use this model in PyTorch:
| [
"### How to use\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #perceiver #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
text-classification | transformers | This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/progr... | {} | addy88/programming-lang-identifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/progr... | [
"# index for the resulting label"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# index for the resulting label"
] |
text2text-generation | transformers | Pretraining Dataset: debatelab/aaac
| {} | addy88/t5-argument-anlyser | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Pretraining Dataset: debatelab/aaac
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #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-base-finetuned-sn-to-en
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["itihasa"], "model-index": [{"name": "t5-base-finetuned-sn-to-en", "results": []}]} | addy88/t5-base-finetuned-sn-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:itihasa",
"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-itihasa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-finetuned-sn-to-en
This model is a fine-tuned version of google/t5-v1_1-base on the itihasa dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# t5-base-finetuned-sn-to-en\n\nThis model is a fine-tuned version of google/t5-v1_1-base on the itihasa dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-itihasa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-finetuned-sn-to-en\n\nThis model is a fine-tuned version of google/t5-v1_1-base on the... |
text2text-generation | transformers | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("addy88/t5-grammar-correction")
model = AutoModelForSeq2SeqLM.from_pretrained("addy88/t5-grammar-correction")
input_ids = tokenizer('grammar: This... | {} | addy88/t5-grammar-correction | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### How to use
Here is how to use this model in PyTorch:
| [
"### How to use\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec-odia-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-assamese-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | addy88/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1339
* Accuracy: 0.9768
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-bengali-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-bhojpuri-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-dogri-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-english-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-gujarati-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-kannada-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | addy88/wav2vec2-large-xls-r-300m-hindi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-maithili-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-malayalam-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-marathi-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-nepali-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-punjabi-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-rajsthani-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("add... | {} | addy88/wav2vec2-sanskrit-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\n\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\n\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-tamil-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-telugu-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... | {} | addy88/wav2vec2-urdu-stt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
| [
"## Usage\nThe model can be used directly (without a language model) as follows:"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18833547
- CO2 Emissions (in grams): 64.58945483765274
## Validation Metrics
- Loss: 0.14247722923755646
- Accuracy: 0.9586074193404036
- Macro F1: 0.9468339778730883
- Micro F1: 0.9586074193404036
- Weighted F1: 0.9585551117678807... | {"language": "ar", "tags": "autonlp", "datasets": ["adelgasmi/autonlp-data-kpmg_nlp"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 64.58945483765274} | adelgasmi/autonlp-kpmg_nlp-18833547 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"ar",
"dataset:adelgasmi/autonlp-data-kpmg_nlp",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #ar #dataset-adelgasmi/autonlp-data-kpmg_nlp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18833547
- CO2 Emissions (in grams): 64.58945483765274
## Validation Metrics
- Loss: 0.14247722923755646
- Accuracy: 0.9586074193404036
- Macro F1: 0.9468339778730883
- Micro F1: 0.9586074193404036
- Weighted F1: 0.9585551117678807... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 18833547\n- CO2 Emissions (in grams): 64.58945483765274",
"## Validation Metrics\n\n- Loss: 0.14247722923755646\n- Accuracy: 0.9586074193404036\n- Macro F1: 0.9468339778730883\n- Micro F1: 0.9586074193404036\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #ar #dataset-adelgasmi/autonlp-data-kpmg_nlp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 18833547\n- CO2 Emissions (in grams... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Kazakh
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for Kazakh ASR using the [Kazakh Speech Corpus v1.1](https://issai.nu.edu.kz/kz-speech-corpus/?version=1.1)
When using this model, make sure that your speech input is sampled at 16kHz.... | {"language": "kk", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["kazakh_speech_corpus"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "Wav2Vec2-XLSR-53 Kazakh by adilism", "results": [{"task":... | aismlv/wav2vec2-large-xlsr-kazakh | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"kk",
"dataset:kazakh_speech_corpus",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"reg... | null | 2022-03-02T23:29:05+00:00 | [] | [
"kk"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #kk #dataset-kazakh_speech_corpus #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Kazakh
Fine-tuned facebook/wav2vec2-large-xlsr-53 for Kazakh ASR using the Kazakh Speech Corpus v1.1
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 ... | [
"# Wav2Vec2-Large-XLSR-53-Kazakh\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for Kazakh ASR using the Kazakh Speech Corpus v1.1\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 model) as follows:",
"## Evaluation\... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #kk #dataset-kazakh_speech_corpus #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Kazakh\n\nFin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kyrgyz 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 mo... | {"language": "ky", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": {}, "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name"... | aismlv/wav2vec2-large-xlsr-kyrgyz | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ky",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ky"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ky #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz 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 evalua... | [
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.\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 model) as follows:",
"## Evaluation\n\nThe mo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ky #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice datas... |
text-classification | transformers | {'junk': 0, 'relevant': 1}
| {} | adit94/relevancy_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {'junk': 0, 'relevant': 1}
| [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
| {} | Jackett/subject_classifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
| [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-bert-base-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-bert-base-cased | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-bert-base-cased
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-trans... | [
"# aditeyabaral/sentencetransformer-bert-base-cased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sent... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-bert-base-cased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be ... |
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