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makamkkumar/NMT | 2021-01-18T09:38:10.000Z | []
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makanan/ae | 2021-04-21T17:20:16.000Z | []
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makanan/sw522 | 2021-04-21T17:03:44.000Z | []
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makanan/test | 2021-04-21T16:44:55.000Z | []
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makanan/umich | 2021-04-21T00:58:15.000Z | []
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maksteel/autonlp-atis | 2021-04-01T01:45:59.000Z | []
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malteos/arqmath-bert-base-cased | 2021-05-19T22:47:01.000Z | [
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| malteos | 30 | transformers | |
mami/malingkundonagn | 2021-04-02T13:24:01.000Z | []
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||
manandey/wav2vec2-large-xlsr-_irish | 2021-04-09T02:46:58.000Z | [
"pytorch",
"wav2vec2",
"ga",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 11 | transformers | ---
language: ga
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Irish by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ga-IE
type: common_voice
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 42.34
---
# Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Irish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-_irish")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-_irish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ga-IE", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-_irish")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-_irish")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 42.34%
## Training
The Common Voice `train`, `validation` datasets were used for training. |
manandey/wav2vec2-large-xlsr-assamese | 2021-03-28T17:55:02.000Z | [
"pytorch",
"wav2vec2",
"as",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 10 | transformers | ---
language: as
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Assamese by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice as
type: common_voice
args: as
metrics:
- name: Test WER
type: wer
value: 74.25
---
# Wav2Vec2-Large-XLSR-53-Assamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Assamese using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "as", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-assamese")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-assamese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "as", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-assamese")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-assamese")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\।]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 74.25%
## Training
The Common Voice `train`, `validation` datasets were used for training. |
manandey/wav2vec2-large-xlsr-breton | 2021-03-26T04:08:58.000Z | [
"pytorch",
"wav2vec2",
"br",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 11 | transformers | ---
language: br
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Breton by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice br
type: common_voice
args: br
metrics:
- name: Test WER
type: wer
value: 54.04
---
# Wav2Vec2-Large-XLSR-53-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "br", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "br", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)\/\«\»\½\…]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 54.04%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
manandey/wav2vec2-large-xlsr-estonian | 2021-03-26T04:04:34.000Z | [
"pytorch",
"wav2vec2",
"et",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 10 | transformers | ---
language: et
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Estonian by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice et
type: common_voice
args: et
metrics:
- name: Test WER
type: wer
value: 37.36
---
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "et", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-estonian")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-estonian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "et", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-estonian")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-estonian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\|\।\–\’\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 37.36%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
manandey/wav2vec2-large-xlsr-mongolian | 2021-03-26T04:06:01.000Z | [
"pytorch",
"wav2vec2",
"mn",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 12 | transformers | ---
language: mn
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Mongolian by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice mn
type: common_voice
args: mn
metrics:
- name: Test WER
type: wer
value: 43.08
---
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "mn", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-mongolian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "mn", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-mongolian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 43.08%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
manandey/wav2vec2-large-xlsr-punjabi | 2021-03-27T12:59:44.000Z | [
"pytorch",
"wav2vec2",
"pa-IN",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 14 | transformers | ---
language: pa-IN
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Punjabi by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pa-IN
type: common_voice
args: pa-IN
metrics:
- name: Test WER
type: wer
value: 57.31
---
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-punjabi")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-punjabi")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "pa-IN", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-punjabi")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-punjabi")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 57.31%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
manandey/wav2vec2-large-xlsr-tamil | 2021-03-27T05:10:15.000Z | [
"pytorch",
"wav2vec2",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manandey | 1,156 | transformers | ---
language: ta
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Tamil by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ta
type: common_voice
args: ta
metrics:
- name: Test WER
type: wer
value: 56.44
---
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ta", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-tamil")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-tamil")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ta", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-tamil")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-tamil")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 56.44%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
manav/causal_qa | 2021-05-19T22:48:49.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manav | 17 | transformers | This a BERT-based QA model finetuned to answer causal questions. The original model this is based on can be found [here](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2). Analysis of this model is associated with the work found at the following [repo](https://github.com/kstats/CausalQG). |
manav/dialogpt-large-kanye-reddit | 2021-05-23T08:48:22.000Z | [
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"conversational",
"text-generation"
]
| conversational | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manav | 332 | transformers | ---
tags:
- conversational
---
## Model description
Finetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed. |
manav/dialogpt-medium-berkeley-reddit | 2021-05-23T08:52:16.000Z | [
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"conversational",
"text-generation"
]
| conversational | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| manav | 137 | transformers | ---
tags:
- conversational
---
## Model description
Finetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed. |
manishiitg/bart-recruit-qa | 2020-11-01T14:16:30.000Z | [
"pytorch",
"bart",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 29 | transformers | |
manishiitg/distilbart-xsum-12-6-recruit-qa | 2020-11-02T11:30:29.000Z | [
"pytorch",
"bart",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 12 | transformers | |
manishiitg/distilbert-resume-parts-classify | 2020-12-09T13:59:30.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"eval_results_None.txt",
"label_list.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 3,492 | transformers | |
manishiitg/distilbert-squad-256seq-8batch-test | 2020-06-13T15:50:48.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 9 | transformers | |
manishiitg/distilrobert-base-squadv2-328seq-128stride-test | 2021-05-20T17:43:42.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 24 | transformers | |
manishiitg/longformer-recruit-qa-large | 2020-10-30T05:17:51.000Z | [
"pytorch",
"longformer",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 30 | transformers | |
manishiitg/longformer-recruit-qa-v2 | 2020-11-11T12:52:27.000Z | [
"pytorch",
"longformer",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 32 | transformers | |
manishiitg/longformer-recruit-qa-v3 | 2020-11-11T10:30:24.000Z | []
| [
".gitattributes"
]
| manishiitg | 0 | |||
manishiitg/longformer-recruit-qa-v6 | 2020-11-11T12:16:05.000Z | []
| [
".gitattributes"
]
| manishiitg | 0 | |||
manishiitg/longformer-recruit-qa | 2020-11-22T06:49:37.000Z | [
"pytorch",
"longformer",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tmpova8rth0",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| manishiitg | 2,438 | transformers | |
manishiitg/mobilebert-recruit-qa | 2020-11-02T10:38:40.000Z | [
"pytorch",
"mobilebert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 30 | transformers | |
manishiitg/output | 2021-05-20T17:44:39.000Z | [
"pytorch",
"jax",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"eval_results_lm.txt",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json",
"checkpoint-10000/optimizer.pt",
"checkpoint-10000/scheduler.pt",
"checkpoint-10000/training_args.bin",
"distilbert/distilbert-base-uncased/eval_results.txt",
"distilbert/distilbert-base-uncased/pytorch_model.bin",
"distilbert/distilbert-base-uncased/test_results.txt",
"distilbert/distilbert-base-uncased/tokenizer_config.json",
"distilbert/distilbert-base-uncased/training_args.bin"
]
| manishiitg | 30 | transformers | |
manishiitg/resume-ner | 2020-07-21T11:52:03.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers"
]
| token-classification | [
".gitattributes",
"config.json",
"eval_results.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 115 | transformers | |
manishiitg/spanbert-large-recruit-qa | 2021-05-19T22:51:08.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 20 | transformers | |
manishiitg/spanbert-recruit-qa | 2021-05-19T22:54:34.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manishiitg | 63 | transformers | |
manoilok/gpt-neo-2.7B | 2021-04-24T09:37:12.000Z | []
| [
".gitattributes"
]
| manoilok | 0 | |||
manudotc/transformers_distilbert-base-uncased_finetuneQA_squad | 2021-04-12T07:45:36.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| manudotc | 11 | transformers | |
manueldeprada/t5-cord19-paraphrase-paws-msrp-opinosis | 2021-04-16T21:27:04.000Z | [
"pytorch",
"t5",
"seq2seq",
"transformers",
"text2text-generation"
]
| text2text-generation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
]
| manueldeprada | 13 | transformers | # T5-Paraphrase pretrained using the CORD-19 dataset.
The base model is manueldeprada/t5-cord19, which has been pretrained with the text and abstracts from the CORD-19 dataset.
It has been finetuned in paraphrasing text like ceshine/t5-paraphrase-paws-msrp-opinosis, using the scripts from [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase).
It does the same paraphrasing but the CORD-19 pretraining allows this model to perform well in COVID-19 related text. |
manueldeprada/t5-cord19 | 2021-04-25T23:12:15.000Z | [
"pytorch",
"t5",
"seq2seq",
"transformers",
"text2text-generation"
]
| text2text-generation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
]
| manueldeprada | 7 | transformers | # T5-base pretrained on CORD-19 dataset
The model has been pretrained on text and abstracts from the CORD-19 dataset, using a manually implemented denoising objetive similar to the original T5 denoising objective.
Model needs to be finetuned on downstream tasks.
Code avaliable in github: [https://github.com/manueldeprada/Pretraining-T5-PyTorch-Lightning](https://github.com/manueldeprada/Pretraining-T5-PyTorch-Lightning). |
manueltonneau/bert-base-cased-conversational-nli | 2021-05-19T22:58:04.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
]
| [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| manueltonneau | 17 | transformers | ||
manueltonneau/biocovid-bert-large-cased | 2020-06-03T07:40:46.000Z | [
"pytorch",
"transformers"
]
| [
".gitattributes",
"biocovid_bert_large_cased.ckpt.zip",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| manueltonneau | 32 | transformers | ||
manueltonneau/clinicalcovid-bert-base-cased | 2020-06-02T11:52:31.000Z | [
"pytorch",
"transformers"
]
| [
".gitattributes",
"clinicalcovid_bert_base_cased.ckpt.zip",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| manueltonneau | 244 | transformers | ||
manueltonneau/clinicalcovid-bert-nli | 2021-05-19T22:59:04.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
]
| [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| manueltonneau | 13 | transformers | ||
manueltonneau/twibert-lowercase-50272 | 2021-06-14T19:37:36.000Z | [
"pytorch",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"dict.txt",
"merges.txt",
"pytorch_model.bin",
"vocab.json"
]
| manueltonneau | 39 | transformers | |
marbogusz/bert-multi-cased-squad_sv | 2021-05-19T23:00:13.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| marbogusz | 15 | transformers | Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset
|
marcel/wav2vec2-large-xlsr-53-german | 2021-03-29T03:55:44.000Z | [
"pytorch",
"wav2vec2",
"de",
"dataset:common_voice",
"dataset:wer",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"optimizer.pt",
"preprocessor_config.json",
"pytorch_model.bin",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin",
"vocab.json"
]
| marcel | 38 | transformers | ---
language: de
datasets:
- common_voice
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 15.80
---
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "de", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "de", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]'
substitutions = {
'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]',
'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]',
'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]',
'c' : '[\č\ć\ç\с]',
'l' : '[\ł]',
'u' : '[\ú\ū\ứ\ů]',
'und' : '[\&]',
'r' : '[\ř]',
'y' : '[\ý]',
's' : '[\ś\š\ș\ş]',
'i' : '[\ī\ǐ\í\ï\î\ï]',
'z' : '[\ź\ž\ź\ż]',
'n' : '[\ñ\ń\ņ]',
'g' : '[\ğ]',
'ss' : '[\ß]',
't' : '[\ț\ť]',
'd' : '[\ď\đ]',
"'": '[\ʿ\་\’\`\´\ʻ\`\‘]',
'p': '\р'
}
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
for x in substitutions:
batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
The model can also be evaluated with in 10% chunks which needs less ressources (to be tested).
```
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import jiwer
lang_id = "de"
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]'
substitutions = {
'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]',
'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]',
'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]',
'c' : '[\č\ć\ç\с]',
'l' : '[\ł]',
'u' : '[\ú\ū\ứ\ů]',
'und' : '[\&]',
'r' : '[\ř]',
'y' : '[\ý]',
's' : '[\ś\š\ș\ş]',
'i' : '[\ī\ǐ\í\ï\î\ï]',
'z' : '[\ź\ž\ź\ż]',
'n' : '[\ñ\ń\ņ]',
'g' : '[\ğ]',
'ss' : '[\ß]',
't' : '[\ț\ť]',
'd' : '[\ď\đ]',
"'": '[\ʿ\་\’\`\´\ʻ\`\‘]',
'p': '\р'
}
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
for x in substitutions:
batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
H, S, D, I = 0, 0, 0, 0
for i in range(10):
print("test["+str(10*i)+"%:"+str(10*(i+1))+"%]")
test_dataset = load_dataset("common_voice", "de", split="test["+str(10*i)+"%:"+str(10*(i+1))+"%]")
test_dataset = test_dataset.map(speech_file_to_array_fn)
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = result["pred_strings"]
targets = result["sentence"]
chunk_metrics = jiwer.compute_measures(targets, predictions)
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
WER = float(S + D + I) / float(H + S + D)
print("WER: {:2f}".format(WER*100))
```
**Test Result**: 15.80 %
## Training
The first 50% of the Common Voice `train`, and 12% of the `validation` datasets were used for training (30 epochs on first 12% and 3 epochs on the remainder).
|
marcel/wav2vec2-large-xlsr-german-demo | 2021-03-25T18:45:25.000Z | [
"pytorch",
"wav2vec2",
"de",
"dataset:common_voice",
"dataset:wer",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"optimizer.pt",
"preprocessor_config.json",
"pytorch_model.bin",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin",
"vocab.json",
"checkpoint-6800/config.json",
"checkpoint-6800/optimizer.pt",
"checkpoint-6800/preprocessor_config.json",
"checkpoint-6800/pytorch_model.bin",
"checkpoint-6800/scheduler.pt",
"checkpoint-6800/trainer_state.json",
"checkpoint-6800/training_args.bin"
]
| marcel | 45 | transformers | ---
language: de
datasets:
- common_voice
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 29.35
---
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using 3% of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "de", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "de", split="test[:10%]")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]'
substitutions = {
'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]',
'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]',
'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]',
'c' : '[\č\ć\ç\с]',
'l' : '[\ł]',
'u' : '[\ú\ū\ứ\ů]',
'und' : '[\&]',
'r' : '[\ř]',
'y' : '[\ý]',
's' : '[\ś\š\ș\ş]',
'i' : '[\ī\ǐ\í\ï\î\ï]',
'z' : '[\ź\ž\ź\ż]',
'n' : '[\ñ\ń\ņ]',
'g' : '[\ğ]',
'ss' : '[\ß]',
't' : '[\ț\ť]',
'd' : '[\ď\đ]',
"'": '[\ʿ\་\’\`\´\ʻ\`\‘]',
'p': '\р'
}
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
for x in substitutions:
batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 29.35 %
## Training
The first 3% of the Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found TODO
|
marefa-nlp/marefa-mt-en-ar | 2021-02-28T14:39:53.000Z | [
"pytorch",
"marian",
"seq2seq",
"en",
"ar",
"dataset:marefa-mt",
"transformers",
"translation",
"Arabic Abjad Characters",
"Arabic",
"license:apache 2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
".gitattributes (1)",
".gitattributes (2)",
".gitattributes (3)",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"train_results.txt",
"trainer_state.json",
"training_args.bin",
"vocab.json"
]
| marefa-nlp | 368 | transformers | ---
language:
- en
- ar
tags:
- translation
- Arabic Abjad Characters
- Arabic
license: Apache 2.0
datasets:
- marefa-mt
---
# Marefa-Mt-En-Ar
# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية
## Model description
This is a model for translating English to Arabic. The special about this model that is take into considration the
using of additional Arabic characters like `پ` or `گ`.
## عن النموذج
هذا النموذج للترجمة الآلية من اللغة الإنجليزية إلى اللغة العربية, هو أول نماذج الترجمة الآلية التي تصدر تحت رعاية
[موسوعة المعرفة](https://www.marefa.org)
يتميز هذا النموذج عن غيره من النماذج بدعمه لحروف الأبجدية العربية الإضافية لتمييز الصوتيات الخاصة في اللغة الإنجليزية مثل `پ` , `گ`.
يمكنك زيارة
[هذه الصفحة](https://www.marefa.org/%D8%A7%D9%84%D9%85%D8%B9%D8%B1%D9%81%D8%A9:%D8%AF%D9%84%D9%8A%D9%84_%D8%A7%D9%84%D8%A3%D8%B3%D9%84%D9%88%D8%A8#.D8.AD.D8.B1.D9.88.D9.81_.D8.A5.D8.B6.D8.A7.D9.81.D9.8A.D8.A9_.D9.84.D9.84.D9.86.D8.B7.D9.82_.D8.A7.D9.84.D8.B3.D9.84.D9.8A.D9.85)
لمعرفة أكثر عن أسلوب إستخدام هذه الحروف الأبجدية العربية
### How to use كيفية الإستخدام
Install transformers and sentencepiece (python >= 3.6)
`$ pip3 install transformers==4.3.0 sentencepiece==0.1.95 nltk==3.5 protobuf==3.15.3 torch==1.7.1`
> If you are using `Google Colab`, please restart your runtime after installing the packages.
-----------
```python
from transformers import MarianTokenizer, MarianMTModel
mname = "marefa-nlp/marefa-mt-en-ar"
tokenizer = MarianTokenizer.from_pretrained(mname)
model = MarianMTModel.from_pretrained(mname)
# English Sample Text
input = "President Putin went to the presidential palace in the capital, Kiev"
translated_tokens = model.generate(**tokenizer.prepare_seq2seq_batch([input], return_tensors="pt"))
translated_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated_tokens]
# translated Arabic Text
print(translated_text)
# ذهب الرئيس پوتن إلى القصر الرئاسي في العاصمة كييڤ
``` |
marefa-nlp/marefa-ner | 2021-05-27T08:57:40.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"ar",
"dataset:Marefa-NER",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"model_args.json",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
]
| marefa-nlp | 912 | transformers | ---
language: ar
datasets:
- Marefa-NER
---
# Tebyan تبيـان
## Marefa Arabic Named Entity Recognition Model
## نموذج المعرفة لتصنيف أجزاء النص
---------
**Version**: 1.2
**Last Update:** 22-05-2021
## Model description
**Marefa-NER** is a Large Arabic Named Entity Recognition (NER) model built on a completely new dataset and targets to extract up to 9 different types of entities
```
Person, Location, Organization, Nationality, Job, Product, Event, Time, Art-Work
```
نموذج المعرفة لتصنيف أجزاء النص. نموذج جديد كليا من حيث البيانات المستخدمة في تدريب النموذج.
كذلك يستهدف النموذج تصنيف حتى 9 أنواع مختلفة من أجزاء النص
```
شخص - مكان - منظمة - جنسية - وظيفة - منتج - حدث - توقيت - عمل إبداعي
```
## How to use كيف تستخدم النموذج
*You can test the model quickly by checking this [Colab notebook](https://colab.research.google.com/drive/1OGp9Wgm-oBM5BBhTLx6Qow4dNRSJZ-F5?usp=sharing)*
-----
Install the following Python packages
`$ pip3 install simpletransformers==0.61.5 nltk==3.5 protobuf==3.15.3 torch==1.7.1`
> If you are using `Google Colab`, please restart your runtime after installing the packages.
-----------
```python
from simpletransformers.ner import NERModel, NERArgs
import logging
import re
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
# disable INFO Logs
transformers_logger = logging.getLogger("transformers")
simpletransformers_logger = logging.getLogger("simpletransformers")
simpletransformers_ner_logger = logging.getLogger("simpletransformers.ner")
transformers_logger.setLevel(logging.WARNING)
simpletransformers_logger.setLevel(logging.WARNING)
simpletransformers_ner_logger.setLevel(logging.WARNING)
# Load the Model
custom_labels = ["O", "B-job", "I-job", "B-nationality", "B-person", "I-person", "B-location",
"B-time", "I-time", "B-event", "I-event", "B-organization", "I-organization",
"I-location", "I-nationality", "B-product", "I-product", "B-artwork", "I-artwork"]
model_args = NERArgs()
model_args.labels_list=custom_labels
ner_model = NERModel(
"xlmroberta", "marefa-nlp/marefa-ner",
args=model_args,
use_cuda=True # set to False to use CPU
)
# Model Inference
samples = [
"تلقى تعليمه في الكتاب ثم انضم الى الأزهر عام 1873م. تعلم على يد السيد جمال الدين الأفغاني والشيخ محمد عبده",
"بعد عودته إلى القاهرة، التحق نجيب الريحاني فرقة جورج أبيض، الذي كان قد ضمَّ - قُبيل ذلك - فرقته إلى فرقة سلامة حجازي . و منها ذاع صيته",
"امبارح اتفرجت على مباراة مانشستر يونايتد مع ريال مدريد في غياب الدون كرستيانو رونالدو",
"Government extends flight ban from India, Pakistan until June 21"
]
# Preprocess
samples = [ " ".join(word_tokenize(sample.strip())) for sample in samples if sample.strip() != "" ]
# Predict
predictions, raw_outputs = ner_model.predict(samples)
# Group the Predicted Entities
entities = []
for pred in predictions:
grouped_entities = []
for rec in pred:
token = list(rec.keys())[0]
label = rec[token]
if label == "O":
continue
if "B-" in label:
grouped_entities.append({"token": token, "label": label.replace("B-","")})
elif "I-" in label and len(grouped_entities) > 0:
grouped_entities[-1]["token"] += f" {token}"
entities.append(grouped_entities)
# Print the model outputs
for sample, results in zip(samples, entities):
print(sample)
for res in results:
print("\t", res["token"], "=>", res["label"])
print("==================")
###
# تلقى تعليمه في الكتاب ثم انضم الى الأزهر عام 1873م . تعلم على يد السيد جمال الدين الأفغاني والشيخ محمد عبده
# الأزهر => organization
# عام 1873م => time
# جمال الدين الأفغاني => person
# محمد عبده => person
# ==================
# بعد عودته إلى القاهرة، التحق نجيب الريحاني فرقة جورج أبيض، الذي كان قد ضمَّ - قُبيل ذلك - فرقته إلى فرقة سلامة حجازي . و منها ذاع صيته
# القاهرة، => location
# نجيب الريحاني => person
# فرقة جورج أبيض، => organization
# فرقة سلامة حجازي => organization
# ==================
# امبارح اتفرجت على مباراة مانشستر يونايتد مع ريال مدريد في غياب الدون كرستيانو رونالدو
# مانشستر يونايتد => organization
# ريال مدريد => organization
# كرستيانو رونالدو => person
# ==================
# Government extends flight ban from India , Pakistan until June 21
# India => location
# Pakistan => location
# June 21 => time
# ==================
###
```
## Fine-Tuning
Check this [notebook](https://colab.research.google.com/drive/1WUYrnmDFFEItqGMvbyjqZEJJqwU7xQR-?usp=sharing) to fine-tune the NER model
## Acknowledgment شكر و تقدير
قام بإعداد البيانات التي تم تدريب النموذج عليها, مجموعة من المتطوعين الذين قضوا ساعات يقومون بتنقيح البيانات و مراجعتها
- على سيد عبد الحفيظ - إشراف
- نرمين محمد عطيه
- صلاح خيرالله
- احمد علي عبدربه
- عمر بن عبد العزيز سليمان
- محمد ابراهيم الجمال
- عبدالرحمن سلامه خلف
- إبراهيم كمال محمد سليمان
- حسن مصطفى حسن
- أحمد فتحي سيد
- عثمان مندو
- عارف الشريف
- أميرة محمد محمود
- حسن سعيد حسن
- عبد العزيز علي البغدادي
- واثق عبدالملك الشويطر
- عمرو رمضان عقل الحفناوي
- حسام الدين أحمد على
- أسامه أحمد محمد محمد
- حاتم محمد المفتي
- عبد الله دردير
- أدهم البغدادي
- أحمد صبري
- عبدالوهاب محمد محمد
- أحمد محمد عوض |
marjoripomarole/teste | 2021-06-08T04:29:58.000Z | []
| [
".gitattributes",
"README.md"
]
| marjoripomarole | 0 | |||
markussagen/xlm-roberta-longformer-base-4096 | 2021-04-29T20:22:49.000Z | [
"pytorch",
"xlm-roberta",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin"
]
| markussagen | 611 | transformers | ## XLM-R Longformer Model
XLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) on the English WikiText-103 corpus.
The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at [Peltarion](https://peltarion.com/) and was fine-tuned on multilingual quesion-answering tasks, with code available [here](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer#xlm-r).
Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps.
## How to Use
The model can be used as expected to fine-tune on a downstream task.
For instance for QA.
```python
import torch
from transformers import AutoModel, AutoTokenizer
MAX_SEQUENCE_LENGTH = 4096
MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096"
tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
padding="max_length",
truncation=True,
)
model = AutoModelForQuestionAnswering.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
)
```
## Training Procedure
The model have been trained on the WikiText-103 corpus, using a **48GB** GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/main/scripts/finetune_qa_models.py) and [Github repo](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer) for more information
```sh
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
unzip wikitext-103-raw-v1.zip
export DATA_DIR=./wikitext-103-raw
scripts/run_long_lm.py \
--model_name_or_path xlm-roberta-base \
--model_name xlm-roberta-to-longformer \
--output_dir ./output \
--logging_dir ./logs \
--val_file_path $DATA_DIR/wiki.valid.raw \
--train_file_path $DATA_DIR/wiki.train.raw \
--seed 42 \
--max_pos 4096 \
--adam_epsilon 1e-8 \
--warmup_steps 500 \
--learning_rate 3e-5 \
--weight_decay 0.01 \
--max_steps 6000 \
--evaluate_during_training \
--logging_steps 50 \
--eval_steps 50 \
--save_steps 6000 \
--max_grad_norm 1.0 \
--per_device_eval_batch_size 2 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 64 \
--overwrite_output_dir \
--fp16 \
--do_train \
--do_eval
```
|
marma/bert-base-swedish-cased-sentiment | 2021-05-19T23:02:02.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| marma | 3,794 | transformers | Experimental sentiment analysis based on ~20k of App Store reviews in Swedish.
### Usage
```python
from transformers import pipeline
>>> sa = pipeline('sentiment-analysis', model='marma/bert-base-swedish-cased-sentiment')
>>> sa('Det här är ju fantastiskt!')
[{'label': 'POSITIVE', 'score': 0.9974609613418579}]
>>> sa('Den här appen suger!')
[{'label': 'NEGATIVE', 'score': 0.998340368270874}]
>>> sa('Det är fruktansvärt.')
[{'label': 'NEGATIVE', 'score': 0.998340368270874}]
>>> sa('Det är fruktansvärt bra.')
[{'label': 'POSITIVE', 'score': 0.998340368270874}]
``` |
marma/test | 2021-03-17T12:23:23.000Z | [
"pytorch",
"wav2vec2",
"sv",
"transformers",
"speech",
"audio",
"automatic-speech-recognition"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| marma | 7 | transformers | ---
language: sv
tags:
- speech
- audio
- automatic-speech-recognition
---
## Test |
marma/wav2vec2-large-xlsr-swedish | 2021-03-22T15:24:50.000Z | [
"pytorch",
"wav2vec2",
"sv",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| marma | 8 | transformers | ---
language: sv
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Swedish by Marma
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice sv-SE
type: common_voice
args: sv
metrics:
- name: Test WER
type: wer
value: 23.33
---
# Wav2Vec2-Large-XLSR-53-Swedish
This model has moved [here](https://huggingface.co/KBLab/wav2vec2-large-xlsr-53-swedish) |
maroo93/kd_squad1.1 | 2021-05-19T23:04:36.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maroo93 | 16 | transformers | |
maroo93/practice00 | 2021-05-19T23:05:30.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maroo93 | 23 | transformers | |
maroo93/practice01 | 2021-05-19T23:06:33.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maroo93 | 28 | transformers | |
maroo93/squad1.1 | 2021-05-19T23:07:37.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maroo93 | 52 | transformers | |
maroo93/squad1.1_1 | 2021-05-19T23:08:41.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"nbest_predictions_.json",
"optimizer.pt",
"predictions_.json",
"pytorch_model.bin",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maroo93 | 33 | transformers | |
maroo93/squad2.0 | 2021-05-19T23:09:45.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| maroo93 | 21 | transformers | |
marrrcin/PolBERTa-base-polish-cased-v1 | 2021-05-20T17:45:35.000Z | [
"pytorch",
"jax",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| marrrcin | 156 | transformers | |
mary2005/Kira | 2021-01-24T13:56:10.000Z | []
| [
".gitattributes",
"IDcard"
]
| mary2005 | 0 | |||
matt-gm/gremon | 2021-04-22T04:10:02.000Z | []
| [
".gitattributes"
]
| matt-gm | 0 | |||
matthartman/matt_test_model | 2021-03-09T16:58:11.000Z | []
| [
".gitattributes"
]
| matthartman | 0 | |||
maurice/PolitBERT | 2021-05-19T23:10:43.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"LR_BERT.png",
"README.md",
"config.json",
"evalloss_BERT.png",
"flax_model.msgpack",
"loss_BERT.png",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| maurice | 15 | transformers | # PolitBERT
## Background
This model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.
## Training
The model was initialized using the pre-trained weights of BERT<sub>BASE</sub> and trained for 20 epochs on the standard MLM task with default parameters.
The used learning rate was 5e-5 with a linearly decreasing schedule and AdamW.
The used batch size is 8 per GPU while beeing trained on two Nvidia GTX TITAN X.
The rest of the used configuration is the same as in ```AutoConfig.from_pretrained('bert-base-uncased')```.
As a tokenizer the default tokenizer of BERT was used (```BertTokenizer.from_pretrained('bert-base-uncased')```)
## Dataset
PolitBERT was trained on the following dataset, which has been split up into single sentences:
<https://www.kaggle.com/mauricerupp/englishspeaking-politicians>
## Usage
To predict a missing word of a sentence, the following pipeline can be applied:
```
from transformers import pipeline, BertTokenizer, AutoModel
fill_mask = pipeline("fill-mask",
model=AutoModel.from_pretrained('maurice/PolitBERT'),
tokenizer=BertTokenizer.from_pretrained('bert-base-uncased'))
print(fill_mask('Donald Trump is a [MASK].'))
```
## Training Results
Evaluation Loss:
Training Loss:
Learning Rate Schedule:
|
maveriq/posbert | 2021-02-01T12:09:10.000Z | []
| [
".gitattributes"
]
| maveriq | 0 | |||
maxdavish/test-model | 2021-03-31T16:33:39.000Z | []
| [
".gitattributes"
]
| maxdavish | 0 | |||
maxidl/wav2vec2-large-xlsr-german | 2021-03-30T05:29:48.000Z | [
"pytorch",
"wav2vec2",
"de",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| maxidl | 405 | transformers | ---
language: de
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: {XLSR Wav2Vec2 Large 53 CV-de}
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 12.77
---
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "de", split="test[:8]") # use a batch of 8 for demo purposes
processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
"""
Preprocessing the dataset by:
- loading audio files
- resampling to 16kHz
- converting to array
- prepare input tensor using the processor
"""
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
# run forward
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"])
"""
Example Result:
Prediction: [
'zieh durch bittet draußen die schuhe aus',
'es kommt zugvorgebauten fo',
'ihre vorterstrecken erschienen it modemagazinen wie der voge karpes basar mariclair',
'fürliepert eine auch für manachen ungewöhnlich lange drittelliste',
'er wurde zu ehren des reichskanzlers otto von bismarck errichtet',
'was solls ich bin bereit',
'das internet besteht aus vielen computern die miteinander verbunden sind',
'der uranus ist der siebinteplanet in unserem sonnensystem s'
]
Reference: [
'Zieht euch bitte draußen die Schuhe aus.',
'Es kommt zum Showdown in Gstaad.',
'Ihre Fotostrecken erschienen in Modemagazinen wie der Vogue, Harper’s Bazaar und Marie Claire.',
'Felipe hat eine auch für Monarchen ungewöhnlich lange Titelliste.',
'Er wurde zu Ehren des Reichskanzlers Otto von Bismarck errichtet.',
'Was solls, ich bin bereit.',
'Das Internet besteht aus vielen Computern, die miteinander verbunden sind.',
'Der Uranus ist der siebente Planet in unserem Sonnensystem.'
]
"""
```
## Evaluation
The model can be evaluated as follows on the German test data of Common Voice:
```python
import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
"""
Evaluation on the full test set:
- takes ~20mins (RTX 3090).
- requires ~170GB RAM to compute the WER. Below, we use a chunked implementation of WER to avoid large RAM consumption.
"""
test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\tpred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8) # batch_size=8 -> requires ~14.5GB GPU memory
# non-chunked version:
# print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
# WER: 12.900291
# Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5:
import jiwer
def chunked_wer(targets, predictions, chunk_size=None):
if chunk_size is None: return jiwer.wer(targets, predictions)
start = 0
end = chunk_size
H, S, D, I = 0, 0, 0, 0
while start < len(targets):
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
start += chunk_size
end += chunk_size
return float(S + D + I) / float(H + S + D)
print("Total (chunk_size=1000), WER: {:2f}".format(100 * chunked_wer(result["pred_strings"], result["sentence"], chunk_size=1000)))
# Total (chunk=1000), WER: 12.768981
```
**Test Result**: WER: 12.77 %
## Training
The Common Voice German `train` and `validation` were used for training.
The script used for training can be found [here](https://github.com/maxidl/wav2vec2).
The model was trained for 50k steps, taking around 30 hours on a single A100.
The arguments used for training this model are:
```
python run_finetuning.py \\
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
--dataset_config_name="de" \\
--output_dir=./wav2vec2-large-xlsr-german \\
--preprocessing_num_workers="16" \\
--overwrite_output_dir \\
--num_train_epochs="20" \\
--per_device_train_batch_size="64" \\
--per_device_eval_batch_size="32" \\
--learning_rate="1e-4" \\
--warmup_steps="500" \\
--evaluation_strategy="steps" \\
--save_steps="5000" \\
--eval_steps="5000" \\
--logging_steps="1000" \\
--save_total_limit="3" \\
--freeze_feature_extractor \\
--activation_dropout="0.055" \\
--attention_dropout="0.094" \\
--feat_proj_dropout="0.04" \\
--layerdrop="0.04" \\
--mask_time_prob="0.08" \\
--gradient_checkpointing="1" \\
--fp16 \\
--do_train \\
--do_eval \\
--dataloader_num_workers="16" \\
--group_by_length
```
|
mayu0007/pegasus_large_covid | 2021-04-27T01:53:59.000Z | [
"pytorch",
"pegasus",
"seq2seq",
"en",
"dataset:CORD-19",
"arxiv:1912.08777",
"transformers",
"summarization",
"text2text-generation"
]
| summarization | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
]
| mayu0007 | 141 | transformers | ---
language: en
tags:
- pytorch
- pegasus
- summarization
datasets:
- CORD-19
widget:
- text: "Background:
On 31 December 2019, the World Health Organization was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The causative pathogen was suspected to be a virus, but it did not match any other known virus. The following day, Wuhan City officials closed the Huanan seafood market, suspected to be the source of the mystery pathogen, because it was reported that certain patients presenting with the symptoms were vendors at that public market. By January 4 2020, the Chinese Health Organization reported 44 active cases. On 7 January 2020, Chinese authorities confirmed that they had identified the causative agent as a novel Coronavirus (CoV). That family includes viruses of the common cold as well as viruses known to cause Middle-East Respiratory Syndrome (MERS); Severe Acute Respiratory Syndrome (SARS).
The new CoV was named Novel Coronavirus (emerged late) 2019 (2019-nCoV). Two days later, Chinese authorities reported the first fatality linked to 2019-nCoV: a 61-year-old male who had been admitted in the first cohort of patients. He had several other underlying medical conditions, which may have contributed to weakening his immune system. Apart from respiratory failure and severe pneumonia caused by 2019-nCoV, the patient suffered from abdominal tumors and chronic liver disease. On 12 January, Chinese scientists released the genetic sequence of 2019-nCoV, in part because nonofficial report of international spread of 2019-nCoV had commenced. The next day, Thailand officially reported its first imported case of 2019-nCoV: a 61-year-old woman from Wuhan -she, however, denied having visited the Huanan seafood market. On January 15 2020, Chinese authorities reported the 140 ©Biomedical Informatics (2020) second death attributed to 2019-nCoV: a 69-year-old male who also suffered of other unrelated severe pathologies, including myocarditis. Infection with 2019-nCov, nonetheless, were thought to be responsible for his abnormal renal function, and severely damaged to multiple organ functions. The following day, Japan reported its first case of 2019-nCoV: a Chinese man in his 30s, who also denied having visited the Huanan market. On January 17, Thailand confirmed the second imported case of 2019-nCoV. Chinese authorities noted a spike in 2019-nCoV infections between January 18 and 19, 2020. That observation arose the suspicion that 2019-nCoV was capable of direct human-to-human transmission.
The following day, 20 January 2020, South Korea confirmed its first case of 2019-nCoV infection: a male patient who denied having visited any public markets in Wuhan. On January 21 2020, the World Health Organization confirmed human-to-human transmission of 2019-nCov. As of that date, the total official number of cases has risen to 222, although it was suspected to be in reality much higher. Infection had spread to health-care workers, and it was suspected that one mode of transmission may be via the eye mucosa. Chinese authorities have also reported a fourth death. The situation was fast becoming alarming: suspected cases appeared in France, Italy and other countries of Europe. Australia seems to be affected as well. Other countries in Asia also reported suspected cases, including the Philippines and Singapore. Suspected cases of 2019-nCoV were reported in North America. The following day, 22 January 2020, World Health Organization Director-General Tedros Adhanom Ghebreyesus convened an emergency meeting to decide whether 2019-nCoV should be declared to constitute a worldwide public health emergency of international concern. Despite a significant rise in confirmed cases of individuals infected with 2019-nCoV -in China alone, at 580 infected individuals, with a death toll now at 17 in the Hubei Province alone -the emergency committee deferred its decision on whether to advise Director-General Ghebreyesus to declare the 2019-nCoV outbreak a public health emergency pandemic of international concern. On January 23, Chinese authorities shut down the city of Wuhan: no public transportation, closed airport and railway station for 11 million people. Later that same day, the city of Ezhou is also in complete lockdown. Festivities for the upcoming Chinese New Year were cancelled throughout China to minimize human contact in crowds.
The following day, the city of Huanggang was declared under lockdown. Singapore confirmed its first imported case, and Vietnam confirmed two cases. Director-General Ghebreyesus declared that, indeed, the 2019-nCoV outbreaks is a public health emergency of international concern. On January 24 2020, the official number of confirmed cases of patients infected with 2019-nCoV had risen to 830 in China alone, with 177 (21%) among them in severe and critical condition. The number of fatalities caused by 2019-nCoV in China was now 25. Japan confirmed its second 2019-nCoV case. Nepal confirmed its first case. The following day, Australia confirmed its first case of 2019-nCoV, as did France. Two suspected cases in Italy were being closely monitored. In China, the official number of new infections -that is, over the previous 24 h -was 444, and the number of new deaths was 16 above and beyond the number reported the previous day. The official number of individuals confirmed to be infected with 2019-nCoV in China became 1,287, including 237 (20.7%) in severe and critical condition. There is no first-, second-or third-generation vaccine available for any members of the Cov family, nor is there practically the time to develop, raise, test and evaluate the effectiveness of a vaccine for 2019-nCov. Moreover, the World Health Organization stated in its 12 January 2020 recommendations entitled'Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected -Interim guidance; WHO/nCoV/Clinical/2020.1' that '…there is no current evidence from RCTs to recommend any specific anti-nCoV treatment for patients with suspected or confirmed nCoV…'. In brief, the international medical community is totally devoid of tools to combat the unfolding 2019-nCov thereat to global public healthnot in terms of preventive medicine to protect subjects at-risk, and not in terms of clinical interventions for infected patients.
What is known, however, is that 2019-nCov, like all corona viruses belong to the Coronaviruses (Coronaviridae) family of RNA viruses that cause diseases in mammals and birds that include diarrhea in cows and pigs, and upper respiratory disease in chickens. In humans, the virus causes respiratory infections, which are generally often mild, rarely lethal. The trends we begin to observe with 2019-nCov suggest that it can be directly transmitted humanto-human, and that it causes serious infections in roughly one in five patients that can lead to death: staggering preliminary statistics. Previous research with other CoV members indicates that proteins of Coronaviruses that could be used in the generation of vaccines include the spike, the envelope, the membrane and the ©Biomedical Informatics (2020) nucleocapsid proteins. The spike protein is of particular interest because it is responsible for the penetration of the virus into the cell, which leads to the initiation of viral replication and proliferation. The spike protein binds to the angiotensin-converting enzyme 2 (ACE2) transmembrane -receptor on the eukaryotic host cell. Case in point, SARS-CoV binds to ACE2, as does MERS-CoV [2] . Indeed, ACE2 is the obligate cellular receptor for CoV entry process via the spike protein [3] .
While the development of a vaccine of the 1 st , 2 nd or 3 rd generation against the spike protein is possible but time consuming, it is therefore timely ad critical to propose new possible and practical approaches for preventing infection of subjects at-risk and for treatment intervention of patients infected with 2019-nCov, or any other CoV for that matter. One such alternative protocol is proposed below.
Methodology:
Short of 1 st , 2 nd or 3 rd generation vaccine measures for preventive CoV, and short of clinical treatment interventions for patients infected with CoV, and specifically, 2019-nCov, it is timely and critical to evaluate new alternatives. Here, we propose that one putative 4 th generation vaccine to control 2019-nCoV explosion might simply involve the genetic engineering a soluble binary molecule (i.e., ACE2R-ACE2R; [ACE2R] 2) or its quaternary form (i.e. two intertwined ACE2R-ACE2R; [ACE2R] 4). This process is fast, reliable and precise by today's standard, and doable in any modern biochemistry laboratory. The obtained sterile molecule could be injected in individuals at high risk as a preventive 4 th vaccination measure, or as a treatment intervention in confirmed cases of 2019-nCoV infection. The soluble molecule is expected to bind the spike protein of circulating CoV with higher affinity than the transmembrane ACE2R, and to render the CoV particles, therefore, incapable of binding to the cell receptor, of penetration into the cells, and of replicating inside the cell. The proposed 4 th generation vaccine would, besides protecting the cells from CoV infection, also preserve ACE2 intracellular functional activity, and guard against the rise of serum angiotensin II levels, which can be pathogenic to lung cell integrity. In brief, the 4 th generation vaccine proposed here would prevent at-risk individuals from becoming sick from any incipient infection: that is, in the true meaning of the term, it would 'vaccinate' them against CoV in general, and in the present case of high emergency provide substantial protection against2019-nCoV. Moreover, should the molecule be genetically engineered to incorporate a neutral protein, such as human serum albumin, the soluble albumin-[ACE2R] 2 or albumin-[ACE2R] 4 complex injected in 2019-nCoV-infected patients would bind the circulating CoV. Patients could then undergo a treatment intervention of 'cleaning' their blood from albumin-[ACE2R] n-CoV complexes by a clinical protocol akin to dialysis. The patient's blood would be passed through a sterile column constructed with high affinity anti-human albumin antibodies. The anti-albumin antibody-albumin-[ACE2R] n-CoV moieties would be retained on the column, and the 'CoV-cleaned' blood returned to the patient to dampen the infection. It is possible that the binding of CoV spike protein to ACE2 is a down regulation of its expression, resulting in increased serum angiotensin II levels, and lung injury. Indeed, administration of recombinant human ACE2 in experimental models of CoV infection ameliorates lung injury in animal models [4] . Therefore, we propose that the 'CoV-cleaned' blood returned to the patient would also be enriched with recombinant human ACE2 to ameliorate lung injury.
Discussion:
Vaccines that are raised from whole pathogens -attenuated or inactivated -are called 1 st generation vaccines. Protocols that involve utilizing specific protein components extracted from the pathogens to reduce risks and side -effects in the host produce 2 nd generation vaccines. By contrast 3 rd generation vaccines are vaccines derived from administration of genetically engineered DNA or mRNA to induce the host cells to produce an antigen in vivo, which in turn is expected to be recognized as non-self, and generate protective antibodies [5] . Here, we propose a new avenue in vaccinology: the generation of a molecule with the purpose of preventing infectious disease -that is, a vaccine -, but not based on the traditional norms of antigen-idiotype binding. The 4 th generation vaccine we theorize here depends upon the specificity of receptor-ligand binding, but is a biochemical molecule constructed TRN-rewired CoV are neither, properly speaking, 1 st or 2 nd generation vaccine, and neither are they 3 rd generation vaccines: they are efficacious hybrid measures that prevent or slow down SARS-CoV, and possibly MERS-CoV epidemic. However, the urgency of the present moment precludes the somewhat lengthy experimentation time that would be required for the development and testing of a 3 rd generation vaccine of the sort. Since scientists have had several issues up to this point in the process of producing a 3 rd generation vaccine for SARS or MERS, whose epidemics were several years ago, it implausible that they could now develop such a 3 rd generation vaccine for 2019-nCov in the emergency the world is experiencing today.
Conclusion:
Taken together, the important points brought forth above emphasize the fact that the field of vaccinology cannot and must not be limited strictly to 1 st , 2 nd or 3 rd generation vaccines. A 4 th generation of vaccines is now emerging that may seem unconventional, but converge toward the same goal of preventing the spread of infectious disease. These 4 th generation vaccines may be particularly relevant in the case of flaming epidemics, when the time to generate, test, evaluate and distribute 1 st , 2 nd or 3 rd generation vaccines is prohibitive, such as is precisely the case now with 2019-nCoV. In certain circumstances, public health urgency demands immediate intervention, and precludes the time required to generate and test new vaccine species. Case in point, the threat now posed by the new member of the Coronavirus family (2019-nConV), whose discovery was announced by the Chinese health authorities on Chinese authorities reported having isolated a new type of coronavirus on 7 January 2020. Whereas 2019-nCoV is reported to a beta coronavirus closely related to SARS and other coronaviruses that originate from bats, it is unclear -and at this point almost irrelevant -to date if 2019-nConV originated from bats or from snake or other animals and subsequently transferred to bats. What is clear is that 2019-nConV is capable of direct humanto-human transmission, and its infection patterns grows alarmingly fast across all continents. To be clear, three weeks into its original reporting, 2019-nCoV has infected children, men, women and elderly in all continents. In China alone, the number of confirmed cases are over thirty-seven thousand infected individuals (n=37,593 as of day 21), and the number of fatalities from the disease has risen over eight hundred (n=813). Whereas both the percent confirmed cases and the percent death rate seem to have steadily decreased in parallel over the past 21 days, the case-fatality percent rate has remained steady above 2% (mean ± SD: 2.34% ± 0.39) (Figure 1) . As a reference point, the case-fatality percent rate of the Spanish influenza following World War I worldwide was at, or slightly above 2.5%; that same statistic for measles with no preventive vaccination measures is close 15%.
In brief, 2019-nCoV seems to be less lethal than the Spanish flu, and may be abating somewhat at its original epicenter; it has generated heightened fear for a global pandemic as other epicenters have emerged, including Singapore and Thailand. In this hypothesis report, we have proposed here a new avenue into 4 th generation vaccines. Thus, vaccine protocols that do not involve the generation of antibodies against whole pathogens uses protein extracts obtained from pathogens, or nucleic acids related to pathogens. Rather, the preventive and protecting ability of the intervention we propose, which still relies on the specific binding of the pathogen to a substrate generated specifically against it, is a biochemical construct, which could actually best be generated by artificial intelligence of immune surveillance [8] algorithms in the not so distant future. The construct we propose here, specific to CoV, and applicable to 2019-nCoV in the context of the immediate urgency that is upon us, can be generated and expanded quickly, simply and reliably in any biochemistry laboratory. We also describe how it can be effectively utilized in treatment protocols of patients already infected with 2019-nCoV, in a slight modification of the common clinical protocol for renal dialysis."
---
# PEGASUS for COVID Literature Summarization
## Model Description
Pegasus-large fine-tuned for COVID literature summarization
## Training data
The data is the [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset, containing over 400,000 scholarly articles, including over 150,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.
A subset of 1,000 articles and their abstracts were used.
The baseline was from the PEGASUS model: [google/pegasus-large](https://huggingface.co/google/pegasus-large). PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf).
## Evaluation Results
The results before and after the fine-tuning on our dataset are shown below:
| Fine-tuning | R-1 | R-2 | R-L |
|:-----------:|:-----:|:-----:|:------:|
| Yes | 36.64 | 12.97 | 20.73 |
| No | 25.51 | 8.07 | 15.21 |
### How to use
We provide a simple snippet of how to use this model for the task of text summarization in PyTorch.
```Python
from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
# Let's load the model and the tokenizer
model_name = "mayu0007/pegasus_large_covid"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name)
# Some text to summarize here
text_to_summarize = "Background:
On 31 December 2019, the World Health Organization was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The causative pathogen was suspected to be a virus, but it did not match any other known virus. The following day, Wuhan City officials closed the Huanan seafood market, suspected to be the source of the mystery pathogen, because it was reported that certain patients presenting with the symptoms were vendors at that public market. By January 4 2020, the Chinese Health Organization reported 44 active cases. On 7 January 2020, Chinese authorities confirmed that they had identified the causative agent as a novel Coronavirus (CoV). That family includes viruses of the common cold as well as viruses known to cause Middle-East Respiratory Syndrome (MERS); Severe Acute Respiratory Syndrome (SARS).\\\\
The new CoV was named Novel Coronavirus (emerged late) 2019 (2019-nCoV). Two days later, Chinese authorities reported the first fatality linked to 2019-nCoV: a 61-year-old male who had been admitted in the first cohort of patients. He had several other underlying medical conditions, which may have contributed to weakening his immune system. Apart from respiratory failure and severe pneumonia caused by 2019-nCoV, the patient suffered from abdominal tumors and chronic liver disease. On 12 January, Chinese scientists released the genetic sequence of 2019-nCoV, in part because nonofficial report of international spread of 2019-nCoV had commenced. The next day, Thailand officially reported its first imported case of 2019-nCoV: a 61-year-old woman from Wuhan -she, however, denied having visited the Huanan seafood market. On January 15 2020, Chinese authorities reported the 140 ©Biomedical Informatics (2020) second death attributed to 2019-nCoV: a 69-year-old male who also suffered of other unrelated severe pathologies, including myocarditis. Infection with 2019-nCov, nonetheless, were thought to be responsible for his abnormal renal function, and severely damaged to multiple organ functions. The following day, Japan reported its first case of 2019-nCoV: a Chinese man in his 30s, who also denied having visited the Huanan market. On January 17, Thailand confirmed the second imported case of 2019-nCoV. Chinese authorities noted a spike in 2019-nCoV infections between January 18 and 19, 2020. That observation arose the suspicion that 2019-nCoV was capable of direct human-to-human transmission.
The following day, 20 January 2020, South Korea confirmed its first case of 2019-nCoV infection: a male patient who denied having visited any public markets in Wuhan. On January 21 2020, the World Health Organization confirmed human-to-human transmission of 2019-nCov. As of that date, the total official number of cases has risen to 222, although it was suspected to be in reality much higher. Infection had spread to health-care workers, and it was suspected that one mode of transmission may be via the eye mucosa. Chinese authorities have also reported a fourth death. The situation was fast becoming alarming: suspected cases appeared in France, Italy and other countries of Europe. Australia seems to be affected as well. Other countries in Asia also reported suspected cases, including the Philippines and Singapore. Suspected cases of 2019-nCoV were reported in North America. The following day, 22 January 2020, World Health Organization Director-General Tedros Adhanom Ghebreyesus convened an emergency meeting to decide whether 2019-nCoV should be declared to constitute a worldwide public health emergency of international concern. Despite a significant rise in confirmed cases of individuals infected with 2019-nCoV -in China alone, at 580 infected individuals, with a death toll now at 17 in the Hubei Province alone -the emergency committee deferred its decision on whether to advise Director-General Ghebreyesus to declare the 2019-nCoV outbreak a public health emergency pandemic of international concern. On January 23, Chinese authorities shut down the city of Wuhan: no public transportation, closed airport and railway station for 11 million people. Later that same day, the city of Ezhou is also in complete lockdown. Festivities for the upcoming Chinese New Year were cancelled throughout China to minimize human contact in crowds.\\\\
The following day, the city of Huanggang was declared under lockdown. Singapore confirmed its first imported case, and Vietnam confirmed two cases. Director-General Ghebreyesus declared that, indeed, the 2019-nCoV outbreaks is a public health emergency of international concern. On January 24 2020, the official number of confirmed cases of patients infected with 2019-nCoV had risen to 830 in China alone, with 177 (21%) among them in severe and critical condition. The number of fatalities caused by 2019-nCoV in China was now 25. Japan confirmed its second 2019-nCoV case. Nepal confirmed its first case. The following day, Australia confirmed its first case of 2019-nCoV, as did France. Two suspected cases in Italy were being closely monitored. In China, the official number of new infections -that is, over the previous 24 h -was 444, and the number of new deaths was 16 above and beyond the number reported the previous day. The official number of individuals confirmed to be infected with 2019-nCoV in China became 1,287, including 237 (20.7%) in severe and critical condition. There is no first-, second-or third-generation vaccine available for any members of the Cov family, nor is there practically the time to develop, raise, test and evaluate the effectiveness of a vaccine for 2019-nCov. Moreover, the World Health Organization stated in its 12 January 2020 recommendations entitled \\\\\\\\'Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected -Interim guidance; WHO/nCoV/Clinical/2020.1\\\\\\\\' that "…there is no current evidence from RCTs to recommend any specific anti-nCoV treatment for patients with suspected or confirmed nCoV…". In brief, the international medical community is totally devoid of tools to combat the unfolding 2019-nCov thereat to global public healthnot in terms of preventive medicine to protect subjects at-risk, and not in terms of clinical interventions for infected patients.
What is known, however, is that 2019-nCov, like all corona viruses belong to the Coronaviruses (Coronaviridae) family of RNA viruses that cause diseases in mammals and birds that include diarrhea in cows and pigs, and upper respiratory disease in chickens. In humans, the virus causes respiratory infections, which are generally often mild, rarely lethal. The trends we begin to observe with 2019-nCov suggest that it can be directly transmitted humanto-human, and that it causes serious infections in roughly one in five patients that can lead to death: staggering preliminary statistics. Previous research with other CoV members indicates that proteins of Coronaviruses that could be used in the generation of vaccines include the spike, the envelope, the membrane and the ©Biomedical Informatics (2020) nucleocapsid proteins. The spike protein is of particular interest because it is responsible for the penetration of the virus into the cell, which leads to the initiation of viral replication and proliferation. The spike protein binds to the angiotensin-converting enzyme 2 (ACE2) transmembrane -receptor on the eukaryotic host cell. Case in point, SARS-CoV binds to ACE2, as does MERS-CoV [2] . Indeed, ACE2 is the obligate cellular receptor for CoV entry process via the spike protein [3] .
While the development of a vaccine of the 1 st , 2 nd or 3 rd generation against the spike protein is possible but time consuming, it is therefore timely ad critical to propose new possible and practical approaches for preventing infection of subjects at-risk and for treatment intervention of patients infected with 2019-nCov, or any other CoV for that matter. One such alternative protocol is proposed below.
Methodology:
Short of 1 st , 2 nd or 3 rd generation vaccine measures for preventive CoV, and short of clinical treatment interventions for patients infected with CoV, and specifically, 2019-nCov, it is timely and critical to evaluate new alternatives. Here, we propose that one putative 4 th generation vaccine to control 2019-nCoV explosion might simply involve the genetic engineering a soluble binary molecule (i.e., ACE2R-ACE2R; [ACE2R] 2) or its quaternary form (i.e. two intertwined ACE2R-ACE2R; [ACE2R] 4). This process is fast, reliable and precise by today's standard, and doable in any modern biochemistry laboratory. The obtained sterile molecule could be injected in individuals at high risk as a preventive 4 th vaccination measure, or as a treatment intervention in confirmed cases of 2019-nCoV infection. The soluble molecule is expected to bind the spike protein of circulating CoV with higher affinity than the transmembrane ACE2R, and to render the CoV particles, therefore, incapable of binding to the cell receptor, of penetration into the cells, and of replicating inside the cell. The proposed 4 th generation vaccine would, besides protecting the cells from CoV infection, also preserve ACE2 intracellular functional activity, and guard against the rise of serum angiotensin II levels, which can be pathogenic to lung cell integrity. In brief, the 4 th generation vaccine proposed here would prevent at-risk individuals from becoming sick from any incipient infection: that is, in the true meaning of the term, it would 'vaccinate' them against CoV in general, and in the present case of high emergency provide substantial protection against2019-nCoV. Moreover, should the molecule be genetically engineered to incorporate a neutral protein, such as human serum albumin, the soluble albumin-[ACE2R] 2 or albumin-[ACE2R] 4 complex injected in 2019-nCoV-infected patients would bind the circulating CoV. Patients could then undergo a treatment intervention of 'cleaning' their blood from albumin-[ACE2R] n-CoV complexes by a clinical protocol akin to dialysis. The patient's blood would be passed through a sterile column constructed with high affinity anti-human albumin antibodies. The anti-albumin antibody-albumin-[ACE2R] n-CoV moieties would be retained on the column, and the 'CoV-cleaned' blood returned to the patient to dampen the infection. It is possible that the binding of CoV spike protein to ACE2 is a down regulation of its expression, resulting in increased serum angiotensin II levels, and lung injury. Indeed, administration of recombinant human ACE2 in experimental models of CoV infection ameliorates lung injury in animal models [4] . Therefore, we propose that the 'CoV-cleaned' blood returned to the patient would also be enriched with recombinant human ACE2 to ameliorate lung injury.
Discussion:
Vaccines that are raised from whole pathogens -attenuated or inactivated -are called 1 st generation vaccines. Protocols that involve utilizing specific protein components extracted from the pathogens to reduce risks and side -effects in the host produce 2 nd generation vaccines. By contrast 3 rd generation vaccines are vaccines derived from administration of genetically engineered DNA or mRNA to induce the host cells to produce an antigen in vivo, which in turn is expected to be recognized as non-self, and generate protective antibodies [5] . Here, we propose a new avenue in vaccinology: the generation of a molecule with the purpose of preventing infectious disease -that is, a vaccine -, but not based on the traditional norms of antigen-idiotype binding. The 4 th generation vaccine we theorize here depends upon the specificity of receptor-ligand binding, but is a biochemical molecule constructed TRN-rewired CoV are neither, properly speaking, 1 st or 2 nd generation vaccine, and neither are they 3 rd generation vaccines: they are efficacious hybrid measures that prevent or slow down SARS-CoV, and possibly MERS-CoV epidemic. However, the urgency of the present moment precludes the somewhat lengthy experimentation time that would be required for the development and testing of a 3 rd generation vaccine of the sort. Since scientists have had several issues up to this point in the process of producing a 3 rd generation vaccine for SARS or MERS, whose epidemics were several years ago, it implausible that they could now develop such a 3 rd generation vaccine for 2019-nCov in the emergency the world is experiencing today.
Conclusion:
Taken together, the important points brought forth above emphasize the fact that the field of vaccinology cannot and must not be limited strictly to 1 st , 2 nd or 3 rd generation vaccines. A 4 th generation of vaccines is now emerging that may seem unconventional, but converge toward the same goal of preventing the spread of infectious disease. These 4 th generation vaccines may be particularly relevant in the case of flaming epidemics, when the time to generate, test, evaluate and distribute 1 st , 2 nd or 3 rd generation vaccines is prohibitive, such as is precisely the case now with 2019-nCoV. In certain circumstances, public health urgency demands immediate intervention, and precludes the time required to generate and test new vaccine species. Case in point, the threat now posed by the new member of the Coronavirus family (2019-nConV), whose discovery was announced by the Chinese health authorities on Chinese authorities reported having isolated a new type of coronavirus on 7 January 2020. Whereas 2019-nCoV is reported to a beta coronavirus closely related to SARS and other coronaviruses that originate from bats, it is unclear -and at this point almost irrelevant -to date if 2019-nConV originated from bats or from snake or other animals and subsequently transferred to bats. What is clear is that 2019-nConV is capable of direct humanto-human transmission, and its infection patterns grows alarmingly fast across all continents. To be clear, three weeks into its original reporting, 2019-nCoV has infected children, men, women and elderly in all continents. In China alone, the number of confirmed cases are over thirty-seven thousand infected individuals (n=37,593 as of day 21), and the number of fatalities from the disease has risen over eight hundred (n=813). Whereas both the percent confirmed cases and the percent death rate seem to have steadily decreased in parallel over the past 21 days, the case-fatality percent rate has remained steady above 2% (mean ± SD: 2.34% ± 0.39) (Figure 1) . As a reference point, the case-fatality percent rate of the Spanish influenza following World War I worldwide was at, or slightly above 2.5%; that same statistic for measles with no preventive vaccination measures is close 15%.
In brief, 2019-nCoV seems to be less lethal than the Spanish flu, and may be abating somewhat at its original epicenter; it has generated heightened fear for a global pandemic as other epicenters have emerged, including Singapore and Thailand. In this hypothesis report, we have proposed here a new avenue into 4 th generation vaccines. Thus, vaccine protocols that do not involve the generation of antibodies against whole pathogens uses protein extracts obtained from pathogens, or nucleic acids related to pathogens. Rather, the preventive and protecting ability of the intervention we propose, which still relies on the specific binding of the pathogen to a substrate generated specifically against it, is a biochemical construct, which could actually best be generated by artificial intelligence of immune surveillance [8] algorithms in the not so distant future. The construct we propose here, specific to CoV, and applicable to 2019-nCoV in the context of the immediate urgency that is upon us, can be generated and expanded quickly, simply and reliably in any biochemistry laboratory. We also describe how it can be effectively utilized in treatment protocols of patients already infected with 2019-nCoV, in a slight modification of the common clinical protocol for renal dialysis."
# Tokenize our text
batch = tokenizer(text_to_summarize, truncation=True, padding='longest', return_tensors="pt")
# Generate the output
output = model.generate(**batch)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
# Finally, we can print the generated summary
print(output_text)
``` |
mazula/test | 2021-02-15T10:02:34.000Z | []
| [
".gitattributes",
"README.md"
]
| mazula | 0 | readme test |
||
mbeck/roberta-base-squad2 | 2021-05-20T17:46:33.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
]
| mbeck | 25 | transformers | |
mbertoni/signaturizer | 2021-05-19T16:21:09.000Z | []
| [
".gitattributes"
]
| mbertoni | 0 | |||
mbien/fdh-wikibio | 2021-05-23T08:55:05.000Z | [
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
]
| text-generation | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| mbien | 22 | transformers | # fdh-wikibio
Model used to prepare Biography Generator for EPFL Foundations of Digital Humanities course.
## Project description
Please read our report on FDH page: http://fdh.epfl.ch/index.php/WikiBio
## Project result
You're invited to read through our generated biographies!
https://wikibio.mbien.pl/ |
mbien/fma2vec | 2021-05-09T21:00:30.000Z | [
"pytorch",
"wav2vec2",
"transformers"
]
| [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin"
]
| mbien | 7 | transformers | # Predicting music popularity using DNNs
This is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa ([email protected])
* Michał ([email protected])
* Noé ([email protected])
## Milestone 3
Main notebook presenting out results is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3.ipynb)
Notebook describing the details of Wav2Vec2.0 pre-training and fine-tuning for the task is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3-wav2vec2.ipynb)
## Milestone 2
Exploratory data analysis notebook is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone2.ipynb)
## Milestone 1
Refined project proposal is available [here](https://github.com/Glorf/DH-401/blob/main/milestone0.md)
## Milestone 0
Original project proposal is available in git history [here](https://github.com/Glorf/DH-401/blob/bb14813ff2bbbd9cdc6b6eecf34c9e3c160598eb/milestone0.md) |
|
mbien/fma2vec2popularity | 2021-05-09T20:48:57.000Z | [
"pytorch",
"wav2vec2",
"transformers"
]
| [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin"
]
| mbien | 10 | transformers | # Predicting music popularity using DNNs
This is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa ([email protected])
* Michał ([email protected])
* Noé ([email protected])
## Milestone 3
Main notebook presenting out results is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3.ipynb)
Notebook describing the details of Wav2Vec2.0 pre-training and fine-tuning for the task is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3-wav2vec2.ipynb)
## Milestone 2
Exploratory data analysis notebook is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone2.ipynb)
## Milestone 1
Refined project proposal is available [here](https://github.com/Glorf/DH-401/blob/main/milestone0.md)
## Milestone 0
Original project proposal is available in git history [here](https://github.com/Glorf/DH-401/blob/bb14813ff2bbbd9cdc6b6eecf34c9e3c160598eb/milestone0.md) |
|
mbien/recipenlg | 2021-05-23T08:56:58.000Z | [
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
]
| text-generation | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| mbien | 232 | transformers | # RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
Model accompanying our INLG 2020 paper: [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://www.aclweb.org/anthology/2020.inlg-1.4.pdf)
## Where is the dataset?
Please visit the website of our project: [recipenlg.cs.put.poznan.pl](https://recipenlg.cs.put.poznan.pl/) to download it.
## How to use the model? Could you explain X andy Y?
Yes, sure! If you feel some information is missing in our paper, please check first in our [thesis](https://www.researchgate.net/publication/345308878_Cooking_recipes_generator_utilizing_a_deep_learning-based_language_model), which is much more detailed. In case of further questions, you're invited to send us a github issue, we will respond as fast as we can!
|
mbien/wav2vec2-large-xlsr-polish | 2021-03-20T20:40:58.000Z | [
"pytorch",
"wav2vec2",
"pl",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| mbien | 22 | transformers | ---
language: pl
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: mbien/wav2vec2-large-xlsr-polish
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pl
type: common_voice
args: pl
metrics:
- name: Test WER
type: wer
value: 23.01
---
# Wav2Vec2-Large-XLSR-53-Polish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "pl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("mbien/wav2vec2-large-xlsr-polish")
model = Wav2Vec2ForCTC.from_pretrained("mbien/wav2vec2-large-xlsr-polish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Polish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "pl", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mbien/wav2vec2-large-xlsr-polish")
model = Wav2Vec2ForCTC.from_pretrained("mbien/wav2vec2-large-xlsr-polish")
model.to("cuda")
chars_to_ignore_regex = '[\—\…\,\?\.\!\-\;\:\"\“\„\%\‘\”\�\«\»\'\’]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 23.01 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/drive/1DvrFMoKp9h3zk_eXrJF2s4_TGDHh0tMc?usp=sharing) |
mbsouksu/wav2vec2-large-xlsr-turkish-large | 2021-03-29T06:40:53.000Z | [
"pytorch",
"wav2vec2",
"tr",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| mbsouksu | 14 | transformers | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Mehmet Berk Souksu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 29.80
---
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
model = Wav2Vec2ForCTC.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
model = Wav2Vec2ForCTC.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\`\…\\]\\[\\&\\’\»«]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 29.80 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://github.com/mbsouksu/wav2vec2-turkish) |
mcanet/gpt2_asimov | 2021-03-19T21:50:04.000Z | []
| [
".gitattributes"
]
| mcanet | 0 | |||
md/alamin | 2021-04-04T16:01:50.000Z | []
| [
".gitattributes",
"README.md"
]
| md | 0 | https://trainraceinspire.com/advert/free-tv-watch-miami-open-2021-live-stream-free/ |
||
mdraw/german-news-sentiment-bert | 2021-05-19T23:11:49.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| mdraw | 14,609 | transformers | # German sentiment BERT finetuned on news data
Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sentiment development in German news articles about migration between 2007 and 2019.
Code for inference (predicting sentiment polarity) on raw text can be found at https://github.com/text-analytics-20/news-sentiment-development/blob/main/sentiment_analysis/bert.py
If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's `germansentiment` package as follows:
First install the package from PyPI:
```bash
pip install germansentiment
```
Then you can use the model in Python:
```python
from germansentiment import SentimentModel
model = SentimentModel('mdraw/german-news-sentiment-bert')
# Examples from our validation dataset
texts = [
'[...], schwärmt der parteilose Vizebürgermeister und Historiker Christian Matzka von der "tollen Helferszene".',
'Flüchtlingsheim 11.05 Uhr: Massenschlägerei',
'Rotterdam habe einen Migrantenanteil von mehr als 50 Prozent.',
]
result = model.predict_sentiment(texts)
print(result)
```
The code above will print:
```python
['positive', 'negative', 'neutral']
```
|
medzaf/test | 2021-04-07T08:58:06.000Z | []
| [
".gitattributes"
]
| medzaf | 0 | |||
meedan/indian-sbert | 2021-02-22T22:37:11.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
]
| [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
]
| meedan | 15 | transformers | ||
meedan/indian-xlm-r | 2021-02-22T22:37:11.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
]
| [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
]
| meedan | 14 | transformers | ||
meghanabhange/Hinglish-Bert-Class | 2021-05-19T23:12:59.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".DS_Store",
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| meghanabhange | 21 | transformers | |
meghanabhange/Hinglish-Bert | 2021-05-19T23:14:48.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| meghanabhange | 20 | transformers | |
meghanabhange/Hinglish-DistilBert-Class | 2021-05-19T23:15:31.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".DS_Store",
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| meghanabhange | 24 | transformers | |
meghanabhange/Hinglish-DistilBert | 2020-10-21T12:46:32.000Z | [
"pytorch",
"distilbert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"distilBertOutput.csv",
"eval_results.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| meghanabhange | 14 | transformers | |
meghanabhange/hinglish-indic-bert | 2020-10-22T18:31:30.000Z | [
"pytorch"
]
| [
".gitattributes",
"config.json",
"eval_results_lm.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"training_args.bin"
]
| meghanabhange | 15 | |||
meghanabhange/hinglish-sbert | 2021-05-19T23:16:15.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"eval_results_lm.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| meghanabhange | 15 | transformers | |
meghanabhange/hinglish-sentence-bert | 2021-05-19T23:17:18.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"eval_results_lm.txt",
"flax_model.msgpack",
"log_history.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| meghanabhange | 30 | transformers | |
mertcanalici/test | 2021-04-01T18:50:39.000Z | []
| [
".gitattributes",
"README.md"
]
| mertcanalici | 0 | |||
mfeb/albert-xxlarge-v2-squad2 | 2020-04-24T16:10:08.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers"
]
| question-answering | [
".gitattributes",
"config.json",
"nbest_predictions_.json",
"null_odds_.json",
"predictions_.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"training_args.bin"
]
| mfeb | 25,917 | transformers | |
mfuntowicz/bert-base-cased-finetuned-sst2 | 2021-05-19T23:19:10.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"eval_results_sst2.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"train_results.txt",
"trainer_state.json",
"training_args.bin",
"vocab.txt"
]
| mfuntowicz | 71 | transformers | |
mfuntowicz/test-model | 2020-11-10T16:10:45.000Z | []
| [
".gitattributes",
"README.md"
]
| mfuntowicz | 0 | Hello World
Test from Colab
|
||
mhmd-azeez/test | 2020-12-02T07:54:00.000Z | []
| [
".gitattributes"
]
| mhmd-azeez | 0 | |||
mhu-coder/ConvTasNet_Libri1Mix_enhsingle | 2021-01-12T20:18:16.000Z | [
"pytorch",
"dataset:libri1mix",
"dataset:enh_single",
"asteroid",
"audio",
"ConvTasNet",
"audio-source-separation",
"license:cc-by-sa-3.0"
]
| audio-source-separation | [
".gitattributes",
"README.md",
"pytorch_model.bin"
]
| mhu-coder | 0 | asteroid | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-source-separation
datasets:
- libri1mix
- enh_single
license: cc-by-sa-3.0
---
## Asteroid model `mhu-coder/ConvTasNet_Libri1Mix_enhsingle`
Imported from [Zenodo](https://zenodo.org/record/4301955#.X9cj98Jw0bY)
### Description:
This model was trained by Mathieu Hu using the librimix/ConvTasNet recipe in
[Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
### Training config:
```yaml
data:
n_src: 1
sample_rate: 16000
segment: 3
task: enh_single
train_dir: data/wav16k/min/train-100
valid_dir: data/wav16k/min/dev
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/train_convtasnet_f34664b9
help: None
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 1
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 2
early_stop: True
epochs: 200
half_lr: True
num_workers: 4
```
### Results:
```yaml
si_sdr: 13.938355526049932
si_sdr_imp: 10.488574220190232
sdr: 14.567380104207393
sdr_imp: 11.064717304994337
sir: inf
sir_imp: nan
sar: 14.567380104207393
sar_imp: 11.064717304994337
stoi: 0.9201010933251715
stoi_imp: 0.1241812697846321
```
### License notice:
This work "ConvTasNet_Libri1Mx_enhsingle" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_Libri1Mix_enhsingle" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Mathieu Hu.
|
miale0711/swedish-sentencetransformers | 2021-01-22T10:53:33.000Z | []
| [
".gitattributes"
]
| miale0711 | 0 | |||
miaomiaomiao/macbert_miao | 2021-05-19T23:20:21.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"trainer_state.json",
"training_args.bin"
]
| miaomiaomiao | 15 | transformers | |
miaomiaomiao/macbert_ngram_miao | 2021-05-19T23:22:00.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"trainer_state.json",
"training_args.bin"
]
| miaomiaomiao | 10 | transformers | for contest
|
miaomiaomiao/nezha_miao | 2021-04-15T04:29:14.000Z | [
"pytorch",
"transformers"
]
| [
".gitattributes",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| miaomiaomiao | 11 | transformers |
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