modelId
stringlengths 4
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stringclasses 21
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stringlengths 2
37
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| library
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values | modelCard
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gorave/gorave
|
2021-03-31T18:02:30.000Z
|
[] |
[
".gitattributes",
"README.md"
] |
gorave
| 0 |
https://www.geogebra.org/m/awcxgj4g
https://www.geogebra.org/m/tx9tme6s
https://www.geogebra.org/m/yx5yyjmx
|
||
gorkemgoknar/gpt2-small-turkish
|
2021-05-25T06:54:50.000Z
|
[
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"tr",
"dataset:wikipedia-turkish",
"transformers",
"turkish",
"license:apache 2.0",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] |
gorkemgoknar
| 285 |
transformers
|
---
language:
- tr
thumbnail:
tags:
- gpt2
- turkish
license: Apache 2.0
datasets:
- wikipedia-turkish
metrics:
- perplexity
- accuracy
widget:
- text: "Bu yazıyı bir bilgisayar yazdı. Yazarken"
context: ""
- text: "İnternete kolay erişim sayesinde dünya daha da küçüldü. Bunun sonucunda"
context: ""
---
# Turkish GPT2 Model Finetuned
# Türkçe GPT2 Modeli
## Model description
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : https://www.metayazar.com/
Fine tuned writer on this model: https://huggingface.co/gorkemgoknar/gpt2-turkish-writer
Work has been done on Pierre Guillou tutorial as on this page.
(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Additional tutorial and source will be in https://github.com/gorkemgoknar in later stage.
Current accuracy 33 % , Perplexity : 51.88
Models are available:
* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
## Intended uses & limitations
#### How to use
#### Install
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-small-turkish")
model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-small-turkish")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
#### Generate 1 word
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
# input text:
# predicted text:
```
#### Generate Full Sequence
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\\\\
\\\\
{}".format(i+1, tokenizer.decode(sample_output.tolist())))
# >> Generated text
#
```
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.
## Training data
Wikipedia Turkish article dump as of 28-10-2020
## Training procedure
## Eval results
| epoch\\\\t|train_loss\\\\t|valid_loss\\\\t|accuracy\\\\t|perplexity\\\\t|time |
| ----- | -------- |--------- | ---------- | --------- | ----- |
|0\\\\t|4.777015\\\\t|4.621834\\\\t|0.292547\\\\t|101.680367\\\\t|2:42:05|
|1\\\\t|4.509412\\\\t|4.403999\\\\t|0.305574\\\\t|81.777267\\\\t|1:09:38|
|2\\\\t|4.169529\\\\t|4.120755\\\\t|0.324908\\\\t|61.605747\\\\t|1:07:45|
|3\\\\t|4.293973\\\\t|4.177899\\\\t|0.317211\\\\t|65.228653\\\\t|1:07:02|
|4\\\\t|4.049848\\\\t|3.949103\\\\t|0.338347\\\\t|51.888783\\\\t|1:05:53|
#Epoch 0 on Tesla T4, others on V100
```
|
gorkemgoknar/gpt2-turkish-writer
|
2021-05-21T16:15:14.000Z
|
[
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"tr",
"dataset:wikipedia-turkish",
"dataset:custom-book-corpus",
"transformers",
"turkish",
"aiwriter",
"finetuned",
"license:apache 2.0",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] |
gorkemgoknar
| 272 |
transformers
|
---
language:
- tr
thumbnail:
tags:
- gpt2
- turkish
- aiwriter
- finetuned
license: Apache 2.0
datasets:
- wikipedia-turkish
- custom-book-corpus
metrics:
- perplexity
- accuracy
widget:
- text: "Bir zaman topu olan ama köpeği olmayan bir çocuk vardı. Parkta"
context: ""
- text: "Uzun uzun sahile doğru baktı. Düşündüklerinden "
context: ""
- text: "Çok uzun zaman önce galaksinin uzak bir köşesinde..."
context: ""
- text: "'Bugün kendimi çok hasta hissediyorum' dedi. Karşısında "
context: ""
---
# Turkish AI Writer based on GPT2-Small
# Türkçe Yapay Zeka Yazarı
## Model description
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski, Shaekspeare, Dumas)
Base work has been done on Pierre Guillou tutorial as on this page.
(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)
Note that Since Turkish language is not close to English as in Porteguese instead of training last 2 layers, last 3 layers are trained.
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Current accuracy 36.3 % , Perplexity : 44.75
Demo (using CPU inference) is available on: http://www.metayazar.com
Models are available:
* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
## Intended uses & limitations
#### How to use
#### Install
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
#### Generate 1 word
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
# input text:
# predicted text:
```
#### Generate Full Sequence
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
# >> Generated text
#
```
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.
## Training data
Wikipedia Turkish article dump as of 28-10-2020
Turkish book dataset of >400 classic novels
## Training procedure
## Eval results
| epoch |train_loss |valid_loss |accuracy |perplexity |time |
| ----- | -------- |--------- | ---------- | --------- | ----- |
|0 |4.497828 |4.549605 |0.277328 |94.595070 |2:09:58|
|1 |4.503929 |4.519456 |0.275071 |91.785645 |2:04:30|
|2 |3.612716 |3.921146 |0.344802 |50.458256 |2:03:22|
|3 |3.777645 |4.072006 |0.326130 |58.674530 |1:56:14|
|4 |2.934462 |3.801303 |0.363719 |44.759476 |1:58:55|
Note: 1cycle rule training is used and epochs are at different times
```
|
gorkemgoknar/wav2vec2-large-xlsr-53-turkish
|
2021-04-05T06:55:19.000Z
|
[
"pytorch",
"wav2vec2",
"tr",
"dataset:common_voice",
"dataset:movies",
"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"
] |
gorkemgoknar
| 9 |
transformers
|
---
language:
- tr
datasets:
- common_voice
- movies
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large Turkish with extended dataset by Gorkem Goknar
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: 50.41
---
# Wav2Vec2-Large-XLSR-53-Turkish
Note: This model is trained with 5 Turkish movies additional to common voice dataset.
Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good.
Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogues as they tend to do better in clean sounds with less background noise.
Dataset building from csv and merging code can be found on below of this Readme.
Please try speech yourself on the right side to see its performance.
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) and 5 Turkish movies that include background noise/talkers .
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
import pydub
from pydub.utils import mediainfo
import array
from pydub import AudioSegment
from pydub.utils import get_array_type
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
new_sample_rate = 16000
def audio_resampler(batch, new_sample_rate = 16000):
#not working without complex library compilation in windows for mp3
#speech_array, sampling_rate = torchaudio.load(batch["path"])
#speech_array, sampling_rate = librosa.load(batch["path"])
#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate
sound = pydub.AudioSegment.from_file(file=batch["path"])
sampling_rate = new_sample_rate
sound = sound.set_frame_rate(new_sample_rate)
left = sound.split_to_mono()[0]
bit_depth = left.sample_width * 8
array_type = pydub.utils.get_array_type(bit_depth)
numeric_array = np.array(array.array(array_type, left._data) )
speech_array = torch.FloatTensor(numeric_array)
batch["speech"] = numeric_array
batch["sampling_rate"] = sampling_rate
#batch["target_text"] = batch["sentence"]
return batch
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch = audio_resampler(batch, new_sample_rate = new_sample_rate)
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
import pydub
import array
import numpy as np
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model.to("cuda")
#Note: Not ignoring "'" on this one
#Note: Not ignoring "'" on this one
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\#\\>\\<\\_\\’\\[\\]\\{\\}]'
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
#using custom load and transformer for audio -> see audio_resampler
new_sample_rate = 16000
def audio_resampler(batch, new_sample_rate = 16000):
#not working without complex library compilation in windows for mp3
#speech_array, sampling_rate = torchaudio.load(batch["path"])
#speech_array, sampling_rate = librosa.load(batch["path"])
#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate
sound = pydub.AudioSegment.from_file(file=batch["path"])
sound = sound.set_frame_rate(new_sample_rate)
left = sound.split_to_mono()[0]
bit_depth = left.sample_width * 8
array_type = pydub.utils.get_array_type(bit_depth)
numeric_array = np.array(array.array(array_type, left._data) )
speech_array = torch.FloatTensor(numeric_array)
return speech_array, new_sample_rate
def remove_special_characters(batch):
##this one comes from subtitles if additional timestamps not processed -> 00:01:01 00:01:01,33
batch["sentence"] = re.sub('\\b\\d{2}:\\d{2}:\\d{2}(,+\\d{2})?\\b', ' ', batch["sentence"])
##remove all caps in text [AÇIKLAMA] etc, do it before..
batch["sentence"] = re.sub('\\[(\\b[A-Z]+\\])', '', batch["sentence"])
##replace three dots (that are inside string with single)
batch["sentence"] = re.sub("([a-zA-Z]+)\\.\\.\\.", r"\\1.", batch["sentence"])
#standart ignore list
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
# Preprocessing the datasets.
# We need to read the aduio files as arrays
new_sample_rate = 16000
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"])
##load and conversion done in resampler , takes and returns batch
speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate)
batch["speech"] = speech_array
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
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
print("EVALUATING:")
##for 8GB RAM on GPU best is batch_size 2 for windows, 4 may fit in linux only
result = test_dataset.map(evaluate, batched=True, batch_size=2)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 50.41 %
## Training
The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.
Similar training model used as base fine-tuning, additional audio resampler is on above code.
Putting model building and merging code below for reference
```python
import pandas as pd
from datasets import load_dataset, load_metric
import os
from pathlib import Path
from datasets import Dataset
import csv
#Walk all subdirectories of base_set_path and find csv files
base_set_path = r'C:\\dataset_extracts'
csv_files = []
for path, subdirs, files in os.walk(base_set_path):
for name in files:
if name.endswith(".csv"):
deckfile= os.path.join(path, name)
csv_files.append(deckfile)
def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']):
path = Path(csvfilename)
csv_delimiter="\\t" ##tab seperated, change if something else
##Pandas has bug reading non-ascii file names, make sure use open with encoding
df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8')
return Dataset.from_pandas(df)
custom_datasets= []
for csv_file in csv_files:
this_dataset=get_dataset_from_csv_file(csv_file)
custom_datasets.append(this_dataset)
from datasets import concatenate_datasets, load_dataset
from datasets import load_from_disk
# Merge datasets together (from csv files)
dataset_file_path = ".\\dataset_file"
custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] )
#save this one to disk
custom_datasets_concat.save_to_disk( dataset_file_path )
#load back from disk
custom_datasets_from_disk = load_from_disk(dataset_file_path)
```
|
goumbalamm/EsperBERTo
|
2021-05-20T16:34:49.000Z
|
[
"pytorch",
"jax",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"training_args.bin",
"vocab.json",
"checkpoint-10000/optimizer.pt",
"checkpoint-10000/pytorch_model.bin",
"checkpoint-10000/scheduler.pt",
"checkpoint-10000/training_args.bin"
] |
goumbalamm
| 22 |
transformers
| |
goumbalamm/wofraBERT
|
2021-05-20T16:35:27.000Z
|
[
"pytorch",
"jax",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"training_args.bin",
"vocab.json",
"checkpoint-10000/optimizer.pt",
"checkpoint-10000/pytorch_model.bin"
] |
goumbalamm
| 11 |
transformers
| |
gpt2-adstext/gpt2-adstext
|
2021-05-21T16:17:48.000Z
|
[
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] |
gpt2-adstext
| 50 |
transformers
| |
graviraja/covid_squad
|
2021-05-19T17:39:35.000Z
|
[
"pytorch",
"jax",
"bert",
"question-answering",
"transformers"
] |
question-answering
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] |
graviraja
| 19 |
transformers
| |
graviraja/covidbert_squad
|
2021-05-19T17:40:49.000Z
|
[
"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"
] |
graviraja
| 34 |
transformers
| |
groadabike/ConvTasNet_DAMP-VSEP_enhboth
|
2021-03-01T17:45:42.000Z
|
[
"pytorch",
"dataset:DAMP-VSEP",
"asteroid",
"audio",
"ConvTasNet",
"audio-source-separation",
"license:cc-by-sa-3.0"
] |
audio-source-separation
|
[
".gitattributes",
"README.md",
"pytorch_model.bin"
] |
groadabike
| 0 |
asteroid
|
---
tags:
- asteroid
- audio
- ConvTasNet
- audio-source-separation
datasets:
- DAMP-VSEP
license: cc-by-sa-3.0
inference: false
---
## Asteroid model `groadabike/ConvTasNet_DAMP-VSEP_enhboth`
Imported from [Zenodo](https://zenodo.org/record/3994193)
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
```yaml
data:
channels: 1
n_src: 2
root_path: data
sample_rate: 16000
samples_per_track: 10
segment: 3.0
task: enh_both
filterbank:
kernel_size: 20
n_filters: 256
stride: 10
main_args:
exp_dir: exp/train_convtasnet
help: None
masknet:
bn_chan: 256
conv_kernel_size: 3
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 4
n_src: 2
norm_type: gLN
skip_chan: 256
optim:
lr: 0.0003
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 12
early_stop: True
epochs: 50
half_lr: True
num_workers: 12
```
### Results:
```yaml
si_sdr: 14.018196157142519
si_sdr_imp: 14.017103133809577
sdr: 14.498517291333885
sdr_imp: 14.463389151567865
sir: 24.149634529133372
sir_imp: 24.11450638936735
sar: 15.338597389045935
sar_imp: -137.30634122401517
stoi: 0.7639416744417206
stoi_imp: 0.1843383526963759
```
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
grtg/ytfyh
|
2021-04-17T16:20:53.000Z
|
[] |
[
".gitattributes",
"README.md"
] |
grtg
| 0 | |||
grumpy/orcas_roberta_mlm
|
2021-05-20T16:36:08.000Z
|
[
"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"
] |
grumpy
| 12 |
transformers
| |
grumpy/weights
|
2021-05-20T16:37:59.000Z
|
[
"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"
] |
grumpy
| 12 |
transformers
| |
grumpy/weights_bert_mlm_epoch50
|
2021-05-19T17:41:43.000Z
|
[
"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"
] |
grumpy
| 19 |
transformers
| |
gsarti/biobert-nli
|
2021-05-19T17:45:15.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gsarti
| 283 |
transformers
|
# BioBERT-NLI
This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
The model uses the original BERT wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
**Base model**: `monologg/biobert_v1.1_pubmed` from HuggingFace's `AutoModel`.
**Training time**: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 30000 |
| Warmup steps | 1450 |
| Lowercasing | False |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
| Model | Score |
|-------------------------------|-------------|
| `biobert-nli` (this) | 73.40 |
| `gsarti/scibert-nli` | 74.50 |
| `bert-base-nli-mean-tokens`[3]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] J. Lee et al, [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://academic.oup.com/bioinformatics/article/36/4/1234/5566506)
[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
|
gsarti/covidbert-nli
|
2021-05-19T17:48:24.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gsarti
| 42 |
transformers
|
# CovidBERT-NLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
**Training time**: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 23000 |
| Warmup steps | 1450 |
| Lowercasing | True |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of similar models obtained with the same procedure to verify its performances.
| Model | Score |
|-------------------------------|-------------|
| `covidbert-nli` (this) | 67.52 |
| `gsarti/biobert-nli` | 73.40 |
| `gsarti/scibert-nli` | 74.50 |
| `bert-base-nli-mean-tokens`[2]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid-19 Semantic Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[2] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
|
gsarti/electra-cord19-generator
|
2020-06-01T08:58:12.000Z
|
[
"pytorch",
"electra",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gsarti
| 15 |
transformers
| |
gsarti/electra-msmarco-med-cord19-discriminator
|
2020-06-01T09:06:39.000Z
|
[
"pytorch",
"electra",
"pretraining",
"transformers"
] |
[
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gsarti
| 19 |
transformers
| ||
gsarti/scibert-nli
|
2021-05-19T17:49:18.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gsarti
| 452 |
transformers
|
# SciBERT-NLI
This is the model [SciBERT](https://github.com/allenai/scibert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace's `AutoModel`.
**Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 20000 |
| Warmup steps | 1450 |
| Lowercasing | True |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
| Model | Score |
|-------------------------------|-------------|
| `scibert-nli` (this) | 74.50 |
| `bert-base-nli-mean-tokens`[3]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
|
gsliwoski/ketobot
|
2021-06-04T19:59:12.000Z
|
[
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"conversational",
"text-generation"
] |
conversational
|
[
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] |
gsliwoski
| 21 |
transformers
|
---
tags:
- conversational
---
# My Awesome Model
|
gukuma/DesignFeedback
|
2021-04-08T09:56:07.000Z
|
[] |
[
".gitattributes"
] |
gukuma
| 0 | |||
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
|
2021-05-26T08:22:58.000Z
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers"
] |
token-classification
|
[
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] |
gunghio
| 91 |
transformers
|
---
metrics:
- precision: 0.9360
- recall: 0.9458
- f1: 0.9409
- accuracy: 0.9902
model-index:
- name: gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
This model was trained from scratch on an conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0388
- Precision: 0.9360
- Recall: 0.9458
- F1: 0.9409
- Accuracy: 0.9902
## Model description
It is based on distilbert-base-multilingual-cased
## Intended uses & limitations
More information needed
## Training and evaluation data
Training dataset: [conll2003](https://huggingface.co/datasets/conll2003)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1653 | 1.0 | 878 | 0.0465 | 0.9267 | 0.9300 | 0.9283 | 0.9883 |
| 0.0322 | 2.0 | 1756 | 0.0404 | 0.9360 | 0.9431 | 0.9396 | 0.9897 |
| 0.0185 | 3.0 | 2634 | 0.0388 | 0.9360 | 0.9458 | 0.9409 | 0.9902 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2
|
guocheng98/HelsinkiNLP-FineTuned-Legal-es-zh
|
2021-06-18T17:13:12.000Z
|
[
"pytorch",
"marian",
"seq2seq",
"es",
"zh",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
] |
translation
|
[
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
] |
guocheng98
| 0 |
transformers
| |
guolingqi/bert_base
|
2021-05-19T17:49:35.000Z
|
[
"bert",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"bert_model.ckpt.data-00000-of-00001",
"bert_model.ckpt.index",
"bert_model.ckpt.meta",
"config.json",
"vocab.txt"
] |
guolingqi
| 21 |
transformers
| |
gurkan08/bert-turkish-text-classification
|
2021-05-19T17:50:18.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"transformers"
] |
text-classification
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"label2id.pickle",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gurkan08
| 44 |
transformers
|
---
language: tr
---
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
70% of the data were used for training and 30% for testing.
train f1-weighted score = %97
test f1-weighted score = %94
# Usage
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
out = nlp(text)
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
results = []
for result in out:
result['label'] = label_dict[result['label']]
results.append(result)
print(results)
# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
|
gurkan08/turkish-ner
|
2021-05-19T17:52:22.000Z
|
[
"pytorch",
"jax",
"bert",
"token-classification",
"transformers"
] |
token-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tag2id.pickle",
"tokenizer_config.json",
"vocab.txt"
] |
gurkan08
| 35 |
transformers
| |
gurkan08/turkish-product-comment-sentiment-classification
|
2021-05-19T17:53:17.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"label2id.pickle",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
gurkan08
| 26 |
transformers
| |
gvs/wav2vec2-large-xlsr-malayalam
|
2021-04-01T21:13:16.000Z
|
[
"pytorch",
"wav2vec2",
"ml",
"dataset:Indic TTS Malayalam Speech Corpus",
"dataset:Openslr Malayalam Speech Corpus",
"dataset:SMC Malayalam Speech Corpus",
"dataset:IIIT-H Indic Speech Databases",
"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"
] |
gvs
| 211 |
transformers
|
---
language: ml
datasets:
- Indic TTS Malayalam Speech Corpus
- Openslr Malayalam Speech Corpus
- SMC Malayalam Speech Corpus
- IIIT-H Indic Speech Databases
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Malayalam XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Test split of combined dataset using all datasets mentioned above
type: custom
args: ml
metrics:
- name: Test WER
type: wer
value: 28.43
---
# Wav2Vec2-Large-XLSR-53-ml
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on ml (Malayalam) using the [Indic TTS Malayalam Speech Corpus (via Kaggle)](https://www.kaggle.com/kavyamanohar/indic-tts-malayalam-speech-corpus), [Openslr Malayalam Speech Corpus](http://openslr.org/63/), [SMC Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and [IIIT-H Indic Speech Databases](http://speech.iiit.ac.in/index.php/research-svl/69.html). The notebooks used to train model are available [here](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/). 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-test-split-of-combined-dataset> # Details on loading this dataset in the evaluation section
processor = Wav2Vec2Processor.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model = Wav2Vec2ForCTC.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
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):
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"])
```
## Evaluation
The model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from datasets import load_dataset, load_metric
from pathlib import Path
# The custom dataset needs to be created using notebook mentioned at the end of this file
data_dir = Path('<path-to-custom-dataset>')
dataset_folders = {
'iiit': 'iiit_mal_abi',
'openslr': 'openslr',
'indic-tts': 'indic-tts-ml',
'msc-reviewed': 'msc-reviewed-speech-v1.0+20200825',
}
# Set directories for datasets
openslr_male_dir = data_dir / dataset_folders['openslr'] / 'male'
openslr_female_dir = data_dir / dataset_folders['openslr'] / 'female'
iiit_dir = data_dir / dataset_folders['iiit']
indic_tts_male_dir = data_dir / dataset_folders['indic-tts'] / 'male'
indic_tts_female_dir = data_dir / dataset_folders['indic-tts'] / 'female'
msc_reviewed_dir = data_dir / dataset_folders['msc-reviewed']
# Load the datasets
openslr_male = load_dataset("json", data_files=[f"{str(openslr_male_dir.absolute())}/sample_{i}.json" for i in range(2023)], split="train")
openslr_female = load_dataset("json", data_files=[f"{str(openslr_female_dir.absolute())}/sample_{i}.json" for i in range(2103)], split="train")
iiit = load_dataset("json", data_files=[f"{str(iiit_dir.absolute())}/sample_{i}.json" for i in range(1000)], split="train")
indic_tts_male = load_dataset("json", data_files=[f"{str(indic_tts_male_dir.absolute())}/sample_{i}.json" for i in range(5649)], split="train")
indic_tts_female = load_dataset("json", data_files=[f"{str(indic_tts_female_dir.absolute())}/sample_{i}.json" for i in range(2950)], split="train")
msc_reviewed = load_dataset("json", data_files=[f"{str(msc_reviewed_dir.absolute())}/sample_{i}.json" for i in range(1541)], split="train")
# Create test split as 20%, set random seed as well.
test_size = 0.2
random_seed=1
openslr_male_splits = openslr_male.train_test_split(test_size=test_size, seed=random_seed)
openslr_female_splits = openslr_female.train_test_split(test_size=test_size, seed=random_seed)
iiit_splits = iiit.train_test_split(test_size=test_size, seed=random_seed)
indic_tts_male_splits = indic_tts_male.train_test_split(test_size=test_size, seed=random_seed)
indic_tts_female_splits = indic_tts_female.train_test_split(test_size=test_size, seed=random_seed)
msc_reviewed_splits = msc_reviewed.train_test_split(test_size=test_size, seed=random_seed)
# Get combined test dataset
split_list = [openslr_male_splits, openslr_female_splits, indic_tts_male_splits, indic_tts_female_splits, msc_reviewed_splits, iiit_splits]
test_dataset = datasets.concatenate_datasets([split['test'] for split in split_list)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model = Wav2Vec2ForCTC.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model.to("cuda")
resamplers = {
48000: torchaudio.transforms.Resample(48_000, 16_000),
}
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�Utrnle\\\\_]'
unicode_ignore_regex = r'[\\\\u200e]'
# 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"])
batch["sentence"] = re.sub(unicode_ignore_regex, '', batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
# Resample if its not in 16kHz
if sampling_rate != 16000:
batch["speech"] = resamplers[sampling_rate](speech_array).squeeze().numpy()
else:
batch["speech"] = speech_array.squeeze().numpy()
# If more than one dimension is present, pick first one
if batch["speech"].ndim > 1:
batch["speech"] = batch["speech"][0]
return 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):
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 (WER)**: 28.43 %
## Training
A combined dataset was created using [Indic TTS Malayalam Speech Corpus (via Kaggle)](https://www.kaggle.com/kavyamanohar/indic-tts-malayalam-speech-corpus), [Openslr Malayalam Speech Corpus](http://openslr.org/63/), [SMC Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and [IIIT-H Indic Speech Databases](http://speech.iiit.ac.in/index.php/research-svl/69.html). The datasets were downloaded and was converted to HF Dataset format using [this notebook](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/blob/main/make_hf_dataset.ipynb)
The notebook used for training and evaluation can be found [here](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/blob/main/fine-tune-xlsr-wav2vec2-on-malayalam-asr-with-transformers_v2.ipynb)
|
gwangjogong/quora-insincere-electra-small
|
2021-06-17T05:11:09.000Z
|
[
"pytorch",
"electra",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"pytorch_model.bin"
] |
gwangjogong
| 24 |
transformers
| |
gyre/200wordrpgmodel
|
2021-05-23T17:53:09.000Z
|
[
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] |
gyre
| 157 |
transformers
| |
h1t0ro/hayden
|
2021-04-06T00:42:28.000Z
|
[] |
[
".gitattributes"
] |
h1t0ro
| 0 | |||
ha-mulan/arxiv-abstracts
|
2021-01-26T01:58:15.000Z
|
[] |
[
".gitattributes"
] |
ha-mulan
| 0 | |||
ha-mulan/hiphopLyrics
|
2021-01-18T07:16:27.000Z
|
[] |
[
".gitattributes"
] |
ha-mulan
| 0 | |||
ha-mulan/moby-dick
|
2021-05-21T16:19:33.000Z
|
[
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] |
ha-mulan
| 7 |
transformers
|
hello
|
haisam90/tapas
|
2021-05-24T03:16:12.000Z
|
[] |
[
".gitattributes"
] |
haisam90
| 0 | |||
haisongzhang/roberta-tiny-cased
|
2021-05-19T17:53:53.000Z
|
[
"pytorch",
"tf",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"vocab.txt"
] |
haisongzhang
| 1,307 |
transformers
|
Github: https://github.com/haisongzhang/roberta-tiny-cased
|
|
hajime9652/textgen
|
2021-03-11T02:45:42.000Z
|
[] |
[
".gitattributes",
"save_pretrainedModel.py",
".ipynb_checkpoints/save_pretrainedModel-checkpoint.py",
"test/saved_model/config.json",
"test/saved_model/pytorch_model.bin"
] |
hajime9652
| 0 | |||
hajime9652/textgen_model
|
2021-03-11T02:20:44.000Z
|
[] |
[
".gitattributes"
] |
hajime9652
| 0 | |||
hajime9652/xlnet-japanese
|
2021-04-01T06:14:01.000Z
|
[
"pytorch",
"xlnet",
"lm-head",
"causal-lm",
"ja",
"transformers",
"text-generation"
] |
text-generation
|
[
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] |
hajime9652
| 62 |
transformers
|
---
language:
- ja
thumbnail:
tags:
- xlnet
- lm-head
- causal-lm
license:
datasets:
metrics:
---
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
## Intended uses & limitations
#### How to use
```python
import MeCab
import subprocess
from transformers import (
pipeline,
XLNetLMHeadModel,
XLNetTokenizer
)
class XLNet():
def __init__(self):
cmd = 'echo `mecab-config --dicdir`"/mecab-ipadic-neologd"'
path = (subprocess.Popen(cmd, stdout=subprocess.PIPE,
shell=True).communicate()[0]).decode('utf-8')
self.m = MeCab.Tagger(f"-Owakati -d {path}")
self.gen_model = XLNetLMHeadModel.from_pretrained("hajime9652/xlnet-japanese")
self.gen_tokenizer = XLNetTokenizer.from_pretrained("hajime9652/xlnet-japanese")
def generate(self, prompt="福岡のご飯は美味しい。コンパクトで暮らしやすい街。"):
prompt = self.m.parse(prompt)
inputs = self.gen_tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
prompt_length = len(self.gen_tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
outputs = self.gen_model.generate(inputs, max_length=200, do_sample=True, top_p=0.95, top_k=60)
generated = prompt + self.gen_tokenizer.decode(outputs[0])[prompt_length:]
return generated
```
#### Limitations and bias
## Training data
## Training procedure
## Eval results
###
See this documents https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
published by https://github.com/mkt3
|
halo/model
|
2021-01-28T05:36:50.000Z
|
[
"pytorch",
"albert",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"config.json",
"pytorch_model.bin",
"spiece.model",
"tokenizer_config.json"
] |
halo
| 7 |
transformers
| |
halo/test_albert
|
2021-01-25T07:56:47.000Z
|
[
"pytorch",
"albert",
"transformers"
] |
[
".gitattributes",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] |
halo
| 8 |
transformers
| ||
hamishs/mBART50-en-az-tr1
|
2021-05-30T13:47:22.000Z
|
[
"pytorch",
"mbart",
"seq2seq",
"transformers",
"text2text-generation"
] |
text2text-generation
|
[
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] |
hamishs
| 36 |
transformers
|
# mBart50 for Zeroshot Azerbaijani-Turkish Translation
The mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance.
|
hamxxxa/SBert
|
2021-03-02T12:17:29.000Z
|
[] |
[
".gitattributes",
"README.md"
] |
hamxxxa
| 0 |
hello
|
||
hana/bertbase
|
2021-04-19T03:57:49.000Z
|
[] |
[
".gitattributes"
] |
hana
| 0 | |||
hanmaroo/xlm_roberta_large_korquad_v1
|
2021-05-13T13:06:12.000Z
|
[
"pytorch",
"xlm-roberta",
"transformers"
] |
[
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] |
hanmaroo
| 59 |
transformers
| ||
hanmaroo/xlm_roberta_large_korquad_v2
|
2021-05-14T03:12:15.000Z
|
[
"pytorch",
"xlm-roberta",
"transformers"
] |
[
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] |
hanmaroo
| 34 |
transformers
| ||
hanseokhyeon/bert-11street
|
2021-05-19T17:57:02.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 18 |
transformers
| |
hanseokhyeon/bert-badword-base
|
2021-05-19T18:00:19.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 11 |
transformers
| |
hanseokhyeon/bert-badword-large
|
2021-05-19T18:02:17.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 8 |
transformers
| |
hanseokhyeon/bert-badword-puri-000
|
2021-05-19T18:04:48.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 9 |
transformers
| |
hanseokhyeon/bert-badword-puri-1200-base
|
2021-05-19T18:08:32.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 10 |
transformers
| |
hanseokhyeon/bert-badword-puri-2400
|
2021-05-19T18:10:27.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 10 |
transformers
| |
hanseokhyeon/bert-badword-puri
|
2021-05-19T18:13:40.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 25 |
transformers
| |
hanseokhyeon/bert-badword
|
2021-05-19T18:16:30.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
] |
hanseokhyeon
| 18 |
transformers
| |
hansgun/model_test
|
2021-05-19T18:19:58.000Z
|
[
"tf",
"bert",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"README.md",
"config.json",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"vocab.txt"
] |
hansgun
| 30 |
transformers
|
## Not yet
|
hansgun/model_test2
|
2021-05-19T18:20:34.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
hansgun
| 8 |
transformers
| ||
haoxiangsnr/FullSubNet-DNS-INTERSPEECH-2020
|
2021-03-16T02:39:17.000Z
|
[] |
[
".gitattributes"
] |
haoxiangsnr
| 0 | |||
harish/AllTokenFineTunedSB-E1
|
2021-06-18T12:11:04.000Z
|
[
"transformers"
] |
[
".gitattributes",
"config.json",
"modules.json",
"similarity_evaluation_sts-dev_results.csv",
"0_Transformer/config.json",
"0_Transformer/pytorch_model.bin",
"0_Transformer/sentence_bert_config.json",
"0_Transformer/special_tokens_map.json",
"0_Transformer/tokenizer_config.json",
"0_Transformer/vocab.txt",
"1_Pooling/config.json"
] |
harish
| 0 |
transformers
| ||
harish/BERT-Plus-CxG-100K
|
2021-05-19T18:21:36.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt-100000.data-00000-of-00001",
"model.ckpt-100000.index",
"model.ckpt-100000.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 24 |
transformers
| ||
harish/BERT-Plus-CxG-20K
|
2021-05-19T18:22:32.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt-20000.data-00000-of-00001",
"model.ckpt-20000.index",
"model.ckpt-20000.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 23 |
transformers
| ||
harish/BERTBaseClone-10000-6000000
|
2021-05-19T18:23:26.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt-500000.data-00000-of-00001",
"model.ckpt-500000.index",
"model.ckpt-500000.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 22 |
transformers
| ||
harish/BERTBaseClone-2-10000
|
2021-05-19T18:24:51.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt.data-00000-of-00001",
"model.ckpt.index",
"model.ckpt.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 23 |
transformers
| ||
harish/BERTRand-10000-6000000
|
2021-05-19T18:26:56.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt-500000.data-00000-of-00001",
"model.ckpt-500000.index",
"model.ckpt-500000.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 21 |
transformers
| ||
harish/BERTRand-2-10000
|
2021-05-19T18:28:38.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt.data-00000-of-00001",
"model.ckpt.index",
"model.ckpt.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 23 |
transformers
| ||
harish/CxGBERT-10000-6000000
|
2021-05-19T18:29:30.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt-400000.data-00000-of-00001",
"model.ckpt-400000.index",
"model.ckpt-400000.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 19 |
transformers
| ||
harish/CxGBERT-2-10000
|
2021-05-19T18:30:31.000Z
|
[
"pytorch",
"jax",
"bert",
"transformers"
] |
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"model.ckpt.data-00000-of-00001",
"model.ckpt.index",
"model.ckpt.meta",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] |
harish
| 12 |
transformers
| ||
harish/PT-FalseFalse-0_2_BEST
|
2021-05-19T18:32:16.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
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"training_args.bin",
"vocab.txt"
] |
harish
| 6 |
transformers
| |
harish/PT-FalseTrue-0_2_BEST
|
2021-05-19T18:34:21.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
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"vocab.txt"
] |
harish
| 6 |
transformers
| |
harish/PT-STS-bert-base-multilingual-cased-4-BEST
|
2021-05-09T23:23:59.000Z
|
[
"transformers"
] |
[
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"0_Transformer/vocab.txt",
"1_Pooling/config.json"
] |
harish
| 6 |
transformers
| ||
harish/PT-STS-bert-base-multilingual-cased-original-2-BEST
|
2021-05-09T23:27:14.000Z
|
[
"transformers"
] |
[
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"0_Transformer/vocab.txt",
"1_Pooling/config.json"
] |
harish
| 8 |
transformers
| ||
harish/PT-STS-pt-e5-all-4-BEST
|
2021-05-09T23:30:31.000Z
|
[
"transformers"
] |
[
".gitattributes",
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] |
harish
| 6 |
transformers
| ||
harish/PT-STS-pt-e5-select-5-BEST
|
2021-05-09T23:32:42.000Z
|
[
"transformers"
] |
[
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] |
harish
| 6 |
transformers
| ||
harish/PT-TrueTrue-0_0_BEST
|
2021-05-19T18:36:20.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
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] |
text-classification
|
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] |
harish
| 7 |
transformers
| |
harish/PT-XLM_R-FalseFalse-0_2_BEST
|
2021-05-09T10:25:56.000Z
|
[
"pytorch",
"xlm-roberta",
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] |
text-classification
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"training_args.bin"
] |
harish
| 6 |
transformers
| |
harish/PT-XLM_R-FalseTrue-0_2_BEST
|
2021-05-09T00:56:11.000Z
|
[
"pytorch",
"xlm-roberta",
"text-classification",
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] |
text-classification
|
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] |
harish
| 11 |
transformers
| |
harish/PT-mbert-train-from-test-and-dev-FalseTrue-0_0_BEST
|
2021-05-19T18:38:31.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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] |
harish
| 7 |
transformers
| |
harish/PT-mbert-train-from-test-and-dev-SHORT-FalseTrue-0_2_BEST
|
2021-05-19T18:40:47.000Z
|
[
"pytorch",
"jax",
"bert",
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] |
text-classification
|
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] |
harish
| 6 |
transformers
| |
harish/PT-v3-dev-test-all-PreTrain-e10-all
|
2021-05-19T18:42:52.000Z
|
[
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fill-mask
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harish
| 6 |
transformers
| |
harish/PT-v3-dev-test-all-PreTrain-e5-all
|
2021-05-19T18:44:56.000Z
|
[
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fill-mask
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harish
| 6 |
transformers
| |
harish/PT-v3-dev-test-all-PreTrain-e5-select
|
2021-05-19T18:47:08.000Z
|
[
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fill-mask
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harish
| 7 |
transformers
| |
harish/PT-v3-dev-test-all-PreTrain-e6-all
|
2021-05-19T18:49:04.000Z
|
[
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fill-mask
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harish
| 6 |
transformers
| |
harish/PT-v3-dev-test-all-PreTrain-e7-select
|
2021-05-19T18:50:30.000Z
|
[
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fill-mask
|
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] |
harish
| 6 |
transformers
| |
harish/preTrained-xlm-pt-e5-select
|
2021-05-03T00:12:17.000Z
|
[
"pytorch",
"xlm-roberta",
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] |
fill-mask
|
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] |
harish
| 15 |
transformers
| |
harish/preTrained-xlm-pt-e8-all
|
2021-05-03T12:11:05.000Z
|
[
"pytorch",
"xlm-roberta",
"masked-lm",
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] |
fill-mask
|
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] |
harish
| 6 |
transformers
| |
harish/preTrained-xlm-pt-e8-select
|
2021-05-03T12:32:44.000Z
|
[
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] |
fill-mask
|
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] |
harish
| 6 |
transformers
| |
harish/v3-dev-test-all-xlm-all-e5-s6
|
2021-05-02T15:58:17.000Z
|
[
"transformers"
] |
[
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] |
harish
| 6 |
transformers
| ||
harish/v3-dev-test-all-xlm-all-e8-s4
|
2021-05-03T16:00:27.000Z
|
[
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] |
[
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] |
harish
| 11 |
transformers
| ||
harish/v3-dev-test-all-xlm-all-e8-s7
|
2021-05-03T16:02:34.000Z
|
[
"transformers"
] |
[
".gitattributes",
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] |
harish
| 11 |
transformers
| ||
harish/v3-dev-test-all-xlm-baseline-s7
|
2021-05-02T16:32:41.000Z
|
[
"transformers"
] |
[
".gitattributes",
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"0_Transformer/tokenizer_config.json",
"1_Pooling/config.json"
] |
harish
| 11 |
transformers
| ||
harish/v3-dev-test-all-xlm-select-e8-s7
|
2021-05-03T16:06:18.000Z
|
[
"transformers"
] |
[
".gitattributes",
"config.json",
"modules.json",
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"0_Transformer/special_tokens_map.json",
"0_Transformer/tokenizer_config.json",
"1_Pooling/config.json"
] |
harish
| 9 |
transformers
| ||
harish/v3-dev-test-all-xlm-select-s6
|
2021-05-02T18:43:06.000Z
|
[
"transformers"
] |
[
".gitattributes",
"config.json",
"modules.json",
"similarity_evaluation_sts-dev_results.csv",
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"0_Transformer/special_tokens_map.json",
"0_Transformer/tokenizer_config.json",
"1_Pooling/config.json"
] |
harish
| 6 |
transformers
| ||
harish/v3-xlm-roberta-base-s4
|
2021-05-02T20:11:23.000Z
|
[
"transformers"
] |
[
".gitattributes",
"config.json",
"modules.json",
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"0_Transformer/special_tokens_map.json",
"0_Transformer/tokenizer_config.json",
"1_Pooling/config.json"
] |
harish
| 7 |
transformers
| ||
harish/xlm-roberta-base-ID
|
2021-05-02T23:37:55.000Z
|
[
"pytorch",
"xlm-roberta",
"masked-lm",
"transformers",
"fill-mask"
] |
fill-mask
|
[
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"tokenizer_config.json"
] |
harish
| 7 |
transformers
| |
hartzeer/rtefsdfsdf
|
2021-04-02T18:06:44.000Z
|
[] |
[
".gitattributes",
"README.md"
] |
hartzeer
| 0 |
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||
hashk1/EsperBERTo-malgranda
|
2021-05-20T16:38:46.000Z
|
[
"pytorch",
"jax",
"roberta",
"masked-lm",
"eo",
"transformers",
"fill-mask"
] |
fill-mask
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] |
hashk1
| 16 |
transformers
|
---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Ĉu vi paloras la <mask> Esperanto?"
---
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
|
hassanzadeh/test_model
|
2021-05-19T18:52:10.000Z
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] |
hassanzadeh
| 12 |
transformers
| |
hatmimoha/arabic-ner
|
2021-05-19T18:53:15.000Z
|
[
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"ar",
"transformers"
] |
token-classification
|
[
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model_args.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] |
hatmimoha
| 639 |
transformers
|
---
language: ar
---
# Arabic Named Entity Recognition Model
Pretrained BERT-based ([arabic-bert-base](https://huggingface.co/asafaya/bert-base-arabic)) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. **PERSON**
- و هذا ما نفاه المعاون السياسي للرئيس ***نبيه بري*** ، النائب ***علي حسن خليل***
- لكن أوساط ***الحريري*** تعتبر أنه ضحى كثيرا في سبيل البلد
- و ستفقد الملكة ***إليزابيث الثانية*** بذلك سيادتها على واحدة من آخر ممالك الكومنولث
2. **ORGANIZATION**
- حسب أرقام ***البنك الدولي***
- أعلن ***الجيش العراقي***
- و نقلت وكالة ***رويترز*** عن ثلاثة دبلوماسيين في ***الاتحاد الأوروبي*** ، أن ***بلجيكا*** و ***إيرلندا*** و ***لوكسمبورغ*** تريد أيضاً مناقشة
- ***الحكومة الاتحادية*** و ***حكومة إقليم كردستان***
- و هو ما يثير الشكوك حول مشاركة النجم البرتغالي في المباراة المرتقبة أمام ***برشلونة*** الإسباني في
3. ***LOCATION***
- الجديد هو تمكين اللاجئين من “ مغادرة الجزيرة تدريجياً و بهدوء إلى ***أثينا*** ”
- ***جزيرة ساكيز*** تبعد 1 كم عن ***إزمير***
4. **DATE**
- ***غدا الجمعة***
- ***06 أكتوبر 2020***
- ***العام السابق***
5. **PRODUCT**
- عبر حسابه ب ***تطبيق “ إنستغرام ”***
- الجيل الثاني من ***نظارة الواقع الافتراضي أوكولوس كويست*** تحت اسم " ***أوكولوس كويست 2*** "
6. **COMPETITION**
- عدم المشاركة في ***بطولة فرنسا المفتوحة للتنس***
- في مباراة ***كأس السوبر الأوروبي***
7. **PRIZE**
- ***جائزة نوبل ل لآداب***
- الذي فاز ب ***جائزة “ إيمي ” لأفضل دور مساند***
8. **EVENT**
- تسجّل أغنية جديدة خاصة ب ***العيد الوطني السعودي***
- ***مهرجان المرأة يافوية*** في دورته الرابعة
9. **DISEASE**
- في مكافحة فيروس ***كورونا*** و عدد من الأمراض
- الأزمات المشابهة مثل “ ***انفلونزا الطيور*** ” و ” ***انفلونزا الخنازير***
## Example
[Find here a complete example to use this model](https://github.com/hatmimoha/arabic-ner)
Here is the map from index to label:
```
id2label = {
"0": "B-PERSON",
"1": "I-PERSON",
"2": "B-ORGANIZATION",
"3": "I-ORGANIZATION",
"4": "B-LOCATION",
"5": "I-LOCATION",
"6": "B-DATE",
"7": "I-DATE"",
"8": "B-COMPETITION",
"9": "I-COMPETITION",
"10": "B-PRIZE",
"11": "I-PRIZE",
"12": "O",
"13": "B-PRODUCT",
"14": "I-PRODUCT",
"15": "B-EVENT",
"16": "I-EVENT",
"17": "B-DISEASE",
"18": "I-DISEASE",
}
```
## Training Corpus
The training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.
## Results
The results on a valid corpus made of 30.000 tokens shows an F-measure of ~87%.
|
hbredin/VoiceActivityDetection-PyanNet-DIHARD
|
2021-03-19T15:40:12.000Z
|
[
"pytorch",
"dataset:dihard",
"pyannote",
"audio",
"voice",
"speaker",
"voice-activity-detection",
"license:mit"
] |
voice-activity-detection
|
[
".gitattributes",
"README.md",
"config.yaml",
"hparams.yaml",
"pipeline.yaml",
"pytorch_model.bin",
"tfevents.bin"
] |
hbredin
| 226 |
pyannote
|
---
tags:
- pyannote
- audio
- voice
- speaker
- voice-activity-detection
datasets:
- dihard
license: mit
inference: false
---
## Voice activity detection trained on DIHARD III development set
Relies on `pyannote.audio` 2.0 (which is still in development):
```bash
$ pip install https://github.com/pyannote/pyannote-audio/archive/develop.zip
```
### Usage
```python
MODEL = "hbredin/VoiceActivityDetection-PyanNet-DIHARD"
```
The simplest way of getting voice activity detection results is to use
the pretrained pipeline:
```python
from pyannote.audio import Pipeline
vad = Pipeline.from_pretrained(MODEL, device="cuda")
speech_regions = vad("audio.wav")
for (start_time, end_time) in speech_regions.get_timeline():
pass
```
`speech_regions` is a [`pyannote.core.Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance.
If you need more control (e.g. to lower the detection threshold for better recall),
speech probabilites can be obtained by running the pretrained model:
```python
from pyannote.audio import Inference
vad = Inference(MODEL, device="cuda")
speech_prob = vad("audio.wav")
```
`speech_prob` is a [`pyannote.core.SlidingWindowFeature`](http://pyannote.github.io/pyannote-core/reference.html#pyannote.core.SlidingWindowFeature) instance
**Note:** audio files can also be provided as a `{"waveform": (num_channels, num_samples) numpy array, "sample_rate": int}` dictionary.
If you need even more control (e.g. to fine-tune the model), the model can be loaded like that:
```python
from pyannote.audio import Model
model = Model.from_pretrained(MODEL)
```
### Citations
#### Model
```bibtex
@inproceedings{Lavechin2020,
Title = {{End-to-end Domain-Adversarial Voice Activity Detection}},
Author = {{Lavechin}, Marvin and {Gill}, Marie-Philippe and {Bousbib}, Ruben and {Bredin}, Herv{\'e} and {Garcia-Perera}, Leibny Paola},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
#### pyannote.audio toolkit
```bibtex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
|
hd10/semeval2020_task11_tc
|
2021-03-09T18:01:57.000Z
|
[
"pytorch",
"deberta",
"text-classification",
"transformers"
] |
text-classification
|
[
".gitattributes",
"README.md",
"bpe_encoder.bin",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json"
] |
hd10
| 13 |
transformers
|
Technique Classification for https://propaganda.qcri.org/ptc/index.html
|
healx/gpt-2-pubmed-large
|
2020-12-11T21:43:38.000Z
|
[
"pytorch",
"arxiv:2004.13845",
"transformers"
] |
[
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] |
healx
| 31 |
transformers
|
GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
|
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