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stringlengths 4
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stringlengths 2
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| library
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values | modelCard
large_stringlengths 0
100k
|
|---|---|---|---|---|---|---|---|---|
sunguk/sunguk-bert | 2021-03-19T08:37:20.000Z | [
"pytorch",
"transformers"
] | [
".gitattributes",
"config.json",
"import test",
"pytorch_model.bin",
"tokenizer_config.json",
"vocab.txt"
] | sunguk | 6 | transformers | ||
sunhao666/chi-sina | 2021-06-04T06:43:10.000Z | [
"pytorch",
"gpt2",
"transformers"
] | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sunhao666 | 10 | transformers | ||
sunhao666/chi-sum | 2021-05-19T17:32:16.000Z | [
"pytorch",
"bert",
"transformers"
] | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin"
] | sunhao666 | 8 | transformers | ||
sunhao666/chi-sum2 | 2021-05-20T04:01:09.000Z | [
"pytorch",
"t5",
"transformers"
] | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sunhao666 | 27 | transformers | ||
superspray/distilbert_base_squad2_custom_dataset | 2021-02-20T07:33:31.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers"
] | question-answering | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] | superspray | 7 | transformers | # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Distilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset
This model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custom dataset as:
```
!python3 run_squad.py --model_type distilbert \
--model_name_or_path /content/distilbert_base_384 \
--do_lower_case \
--output_dir /content/model/\
--do_train \
--train_file $data_dir/additional_qa.json\
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size 4
```
We used Google Colab for training the model, |
superspray/electra_large_discriminator_squad2_custom_dataset | 2021-02-20T07:00:12.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers"
] | question-answering | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] | superspray | 12 | transformers | # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset
This model is [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512/blob/main/README.md)
trained on additional custom dataset as:
```
!python3 run_squad.py --model_type electra \
--model_name_or_path /content/electra_large_512 \
--do_lower_case \
--output_dir /content/model/\
--do_train \
--train_file $data_dir/additional_qa.json\
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size 4
```
We used Google Colab for training the model, |
surajp/RoBERTa-hindi-guj-san | 2021-05-20T22:02:11.000Z | [
"pytorch",
"jax",
"roberta",
"masked-lm",
"hi",
"sa",
"gu",
"dataset:Wikipedia (Hindi, Sanskrit, Gujarati)",
"transformers",
"Indic",
"license:mit",
"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"
] | surajp | 113 | transformers | ---
language:
- hi
- sa
- gu
tags:
- Indic
license: mit
datasets:
- Wikipedia (Hindi, Sanskrit, Gujarati)
metrics:
- perplexity
---
# RoBERTa-hindi-guj-san
## Model description
Multillingual RoBERTa like model trained on Wikipedia articles of Hindi, Sanskrit, Gujarati languages. The tokenizer was trained on combined text.
However, Hindi text was used to pre-train the model and then it was fine-tuned on Sanskrit and Gujarati Text combined hoping that pre-training with Hindi
will help the model learn similar languages.
### Configuration
| Parameter | Value |
|---|---|
| `hidden_size` | 768 |
| `num_attention_heads` | 12 |
| `num_hidden_layers` | 6 |
| `vocab_size` | 30522 |
|`model_type`|`roberta`|
## Intended uses & limitations
#### How to use
```python
# Example usage
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("surajp/RoBERTa-hindi-guj-san")
model = AutoModelWithLMHead.from_pretrained("surajp/RoBERTa-hindi-guj-san")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
# Sanskrit: इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।
# Hindi: अगर आप अब अभ्यास नहीं करते हो तो आप अपने परीक्षा में मूर्खतापूर्ण गलतियाँ करोगे।
# Gujarati: ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.
fill_mask("ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.")
'''
Output:
--------
[
{'score': 0.07849744707345963, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો જ હતો.</s>', 'token': 390},
{'score': 0.06273336708545685, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો ન હતો.</s>', 'token': 478},
{'score': 0.05160355195403099, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો થઇ હતો.</s>', 'token': 2075},
{'score': 0.04751499369740486, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો એક હતો.</s>', 'token': 600},
{'score': 0.03788900747895241, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો પણ હતો.</s>', 'token': 840}
]
```
## Training data
Cleaned wikipedia articles in Hindi, Sanskrit and Gujarati on Kaggle. It contains training as well as evaluation text.
Used in [iNLTK](https://github.com/goru001/inltk)
- [Hindi](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-172k)
- [Gujarati](https://www.kaggle.com/disisbig/gujarati-wikipedia-articles)
- [Sanskrit](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles)
## Training procedure
- On TPU (using `xla_spawn.py`)
- For language modelling
- Iteratively increasing `--block_size` from 128 to 256 over epochs
- Tokenizer trained on combined text
- Pre-training with Hindi and fine-tuning on Sanskrit and Gujarati texts
```
--model_type distillroberta-base \
--model_name_or_path "/content/SanHiGujBERTa" \
--mlm_probability 0.20 \
--line_by_line \
--save_total_limit 2 \
--per_device_train_batch_size 128 \
--per_device_eval_batch_size 128 \
--num_train_epochs 5 \
--block_size 256 \
--seed 108 \
--overwrite_output_dir \
```
## Eval results
perplexity = 2.920005983224673
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99) | [LinkedIn](https://www.linkedin.com/in/parmarsuraj99/)
> Made with <span style="color: #e25555;">♥</span> in India
|
surajp/SanBERTa | 2021-05-20T22:03:36.000Z | [
"pytorch",
"jax",
"roberta",
"masked-lm",
"sa",
"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"
] | surajp | 25 | transformers | ---
language: sa
---
# RoBERTa trained on Sanskrit (SanBERTa)
**Mode size** (after training): **340MB**
### Dataset:
[Wikipedia articles](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles) (used in [iNLTK](https://github.com/goru001/nlp-for-sanskrit)).
It contains evaluation set.
[Sanskrit scraps from CLTK](http://cltk.org/)
### Configuration
| Parameter | Value |
|---|---|
| `num_attention_heads` | 12 |
| `num_hidden_layers` | 6 |
| `hidden_size` | 768 |
| `vocab_size` | 29407 |
### Training :
- On TPU
- For language modelling
- Iteratively increasing `--block_size` from 128 to 256 over epochs
### Evaluation
|Metric| # Value |
|---|---|
|Perplexity (`block_size=256`)|4.04|
## Example of usage:
### For Embeddings
```
tokenizer = AutoTokenizer.from_pretrained("surajp/SanBERTa")
model = RobertaModel.from_pretrained("surajp/SanBERTa")
op = tokenizer.encode("इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।", return_tensors="pt")
ps = model(op)
ps[0].shape
```
```
'''
Output:
--------
torch.Size([1, 47, 768])
```
### For \<mask\> Prediction
```
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="surajp/SanBERTa",
tokenizer="surajp/SanBERTa"
)
## इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।
fill_mask("इयं भाषा न केवल<mask> भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।")
ps = model(torch.tensor(enc).unsqueeze(1))
print(ps[0].shape)
```
```
'''
Output:
--------
[{'score': 0.7516744136810303,
'sequence': '<s> इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 280,
'token_str': 'à¤Ĥ'},
{'score': 0.06230105459690094,
'sequence': '<s> इयं भाषा न केवली भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 289,
'token_str': 'à¥Ģ'},
{'score': 0.055410224944353104,
'sequence': '<s> इयं भाषा न केवला भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 265,
'token_str': 'ा'},
...]
```
### It works!! 🎉 🎉 🎉
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99) | [LinkedIn](https://www.linkedin.com/in/parmarsuraj99/)
> Made with <span style="color: #e25555;">♥</span> in India
|
surajp/albert-base-sanskrit | 2020-12-11T22:02:34.000Z | [
"pytorch",
"albert",
"sa",
"transformers"
] | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | surajp | 89 | transformers | ---
language: sa
---
# ALBERT-base-Sanskrit
Explaination Notebook Colab: [SanskritALBERT.ipynb](https://colab.research.google.com/github/parmarsuraj99/suraj-parmar/blob/master/_notebooks/2020-05-02-SanskritALBERT.ipynb)
Size of the model is **46MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained("surajp/albert-base-sanskrit")
model = AutoModel.from_pretrained("surajp/albert-base-sanskrit")
enc=tokenizer.encode("ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥")
print(tokenizer.decode(enc))
ps = model(torch.tensor(enc).unsqueeze(1))
print(ps[0].shape)
```
```
'''
Output:
--------
[CLS] ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥[SEP]
torch.Size([28, 1, 768])
```
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99)
> Made with <span style="color: #e25555;">♥</span> in India
|
|
surajp/gpt2-hindi | 2021-05-23T13:02:32.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"vocab.json"
] | surajp | 161 | transformers | |
susumu2357/bert-base-swedish-squad2 | 2021-05-20T07:20:04.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"sv",
"dataset:susumu2357/squad_v2_sv",
"transformers",
"squad",
"license:apache-2.0"
] | question-answering | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"vocab.txt"
] | susumu2357 | 57 | transformers | ---
language:
- sv
tags:
- squad
license: apache-2.0
datasets:
- susumu2357/squad_v2_sv
metrics:
- squad_v2
---
# Swedish BERT Fine-tuned on SQuAD v2
This model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.
## Training data
Fine-tuning was done based on the pre-trained model [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased).
Training and dev datasets are our
[Swedish translation of SQuAD v2](https://github.com/susumu2357/SQuAD_v2_sv).
[Here](https://huggingface.co/datasets/susumu2357/squad_v2_sv) is the HuggingFace Datasets.
## Hyperparameters
```
batch_size = 16
n_epochs = 2
max_seq_len = 386
learning_rate = 3e-5
warmup_steps = 2900 # warmup_proportion = 0.2
doc_stride=128
max_query_length=64
```
## Eval results
```
'exact': 66.72642524202223
'f1': 70.11149581003404
'total': 11156
'HasAns_exact': 55.574745730186144
'HasAns_f1': 62.821693965983044
'HasAns_total': 5211
'NoAns_exact': 76.50126156433979
'NoAns_f1': 76.50126156433979
'NoAns_total': 5945
```
## Limitations and bias
This model may contain biases due to mistranslations of the SQuAD dataset.
## BibTeX entry and citation info
```bibtex
@misc{svSQuADbert,
author = {Susumu Okazawa},
title = {Swedish BERT Fine-tuned on Swedish SQuAD 2.0},
year = {2021},
howpublished = {\url{https://huggingface.co/susumu2357/bert-base-swedish-squad2}},
}
```
|
svalabs/bi-electra-ms-marco-german-uncased | 2021-06-14T07:46:23.000Z | [
"pytorch",
"electra",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.08663",
"arxiv:2104.12741",
"transformers"
] | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | svalabs | 37 | transformers | # SVALabs - German Uncased Electra Bi-Encoder
In this repository, we present our german, uncased bi-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetuned as a bi-encoder for Passage Retrieval using the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) package.
For this purpose, we translated the [MSMARCO-Passage-Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) dataset using the [fairseq-wmt19-en-de](https://github.com/pytorch/fairseq/tree/master/examples/wmt19) translation model.
### Model Details
| | Description or Link |
|---|---|
|**Base model** | [```german-nlp-group/electra-base-german-uncased```](https://huggingface.co/german-nlp-group/electra-base-german-uncased) |
|**Finetuning task**| Passage Retrieval / Semantic Search |
|**Source dataset**| [```MSMARCO-Passage-Ranking```](https://github.com/microsoft/MSMARCO-Passage-Ranking) |
|**Translation model**| [```fairseq-wmt19-en-de```](https://github.com/pytorch/fairseq/tree/master/examples/wmt19) |
### Performance
We evaluated our model on the [GermanDPR testset](https://deepset.ai/germanquad) and followed the benchmark framework of [BEIR](https://github.com/UKPLab/beir).
In order to compare our results, we conducted an evaluation on the same test data with BM25 and presented the results in the table below.
We took every paragraph with negative and positive context out of the testset and deduplicated them. The resulting corpus size is 2871 against 1025 queries.
| Model | NDCG@1 | NDCG@5 | NDCG@10 | Recall@1 | Recall@5 | Recall@10 |
|:-------:|:--------:|:--------:|:---------:|:--------:|:----------:|:-----------:|
| BM25 | 0.1463 | 0.3451 | 0.4097 | 0.1463 | 0.5424 | 0.7415 |
| Ours | 0.4624 | 0.6218 | 0.6425 | 0.4624 | 0.7581 | 0.8205 |
### How to Use
With ```sentence-transformers``` package (see [UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers) on GitHub for more details):
```python
from sentence_transformers import SentenceTransformer
bi_model = SentenceTransformer("svalabs/bi-electra-ms-marco-german-uncased")
```
### Semantic Search Example
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
K = 3 # number of top ranks to retrieve
# specify documents and queries
docs = [
"Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.",
"Der Gepard jagt seine Beute.",
"Wir haben in der Agentur ein neues System für Zeiterfassung.",
"Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.",
"Einen Impftermin kann mir der Arzt momentan noch nicht anbieten.",
"Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.",
"Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.",
"Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.",
"Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.",
"Bei ALDI sind die Bananen gerade im Angebot.",
"Die Entstehung der Erde ist 4,5 milliarden jahre her.",
"Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.",
"DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.",
]
queries = [
"dax steigt",
"dax sinkt",
"probleme mit knieschmerzen",
"software für urlaubsstunden",
"raubtier auf der jagd",
"alter der erde",
"wie alt ist unser planet?",
"wie kapital sichern",
"supermarkt lebensmittel reduziert",
"wodurch ist der tyrannosaurus aussgestorben",
"serien streamen"
]
# encode documents and queries
features_docs = bi_model.encode(docs)
features_queries = bi_model.encode(queries)
# compute pairwise cosine similarity scores
sim = cosine_similarity(features_queries, features_docs)
# print results
for i, query in enumerate(queries):
ranks = np.argsort(-sim[i])
print("Query:", query)
for j, r in enumerate(ranks[:K]):
print(f"[{j}: {sim[i, r]: .3f}]", docs[r])
print("-"*96)
```
**Console Output**:
```
Query: dax steigt
[0: 0.811] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[1: 0.719] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
[2: 0.218] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
------------------------------------------------------------------------------------------------
Query: dax sinkt
[0: 0.815] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
[1: 0.719] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[2: 0.243] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
------------------------------------------------------------------------------------------------
Query: probleme mit knieschmerzen
[0: 0.237] Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.
[1: 0.209] Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.
[2: 0.182] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
------------------------------------------------------------------------------------------------
Query: software für urlaubsstunden
[0: 0.478] Wir haben in der Agentur ein neues System für Zeiterfassung.
[1: 0.208] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[2: 0.190] Bei ALDI sind die Bananen gerade im Angebot.
------------------------------------------------------------------------------------------------
Query: raubtier auf der jagd
[0: 0.599] Der Gepard jagt seine Beute.
[1: 0.264] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[2: 0.159] Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.
------------------------------------------------------------------------------------------------
Query: alter der erde
[0: 0.705] Die Entstehung der Erde ist 4,5 milliarden jahre her.
[1: 0.413] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
[2: 0.262] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
------------------------------------------------------------------------------------------------
Query: wie alt ist unser planet?
[0: 0.441] Die Entstehung der Erde ist 4,5 milliarden jahre her.
[1: 0.335] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[2: 0.302] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
------------------------------------------------------------------------------------------------
Query: wie kapital sichern
[0: 0.547] Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.
[1: 0.331] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[2: 0.143] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
------------------------------------------------------------------------------------------------
Query: supermarkt lebensmittel reduziert
[0: 0.455] Bei ALDI sind die Bananen gerade im Angebot.
[1: 0.362] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
[2: 0.345] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
------------------------------------------------------------------------------------------------
Query: wodurch ist der tyrannosaurus aussgestorben
[0: 0.457] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
[1: 0.216] Der Gepard jagt seine Beute.
[2: 0.195] Die Entstehung der Erde ist 4,5 milliarden jahre her.
------------------------------------------------------------------------------------------------
Query: serien streamen
[0: 0.570] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[1: 0.361] Wir haben in der Agentur ein neues System für Zeiterfassung.
[2: 0.282] Bei ALDI sind die Bananen gerade im Angebot.
------------------------------------------------------------------------------------------------
```
### Contact
- Baran Avinc, [email protected]
- Jonas Grebe, [email protected]
- Lisa Stolz, [email protected]
- Bonian Riebe, [email protected]
### References
- N. Reimers and I. Gurevych (2019), ['Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'](https://arxiv.org/abs/1908.10084).
- Payal Bajaj et al. (2018), ['MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'](https://arxiv.org/abs/1611.09268).
- N. Thakur et al. (2021), ['BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models'](https://arxiv.org/abs/2104.08663).
- T. Möller, J. Risch and M. Pietsch (2021), ['GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval'](https://arxiv.org/abs/2104.12741).
|
|
svalabs/cross-electra-ms-marco-german-uncased | 2021-06-10T07:20:46.000Z | [
"pytorch",
"electra",
"text-classification",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.08663",
"arxiv:2104.12741",
"arxiv:2010.02666",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | svalabs | 66 | transformers | # SVALabs - German Uncased Electra Cross-Encoder
In this repository, we present our german, uncased cross-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetuned as a cross-encoder for Passage Retrieval using the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) package.
For this purpose, we translated the [MSMARCO-Passage-Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) dataset using the [fairseq-wmt19-en-de](https://github.com/pytorch/fairseq/tree/master/examples/wmt19) translation model.
### Model Details
| | Description or Link |
|---|---|
|**Base model** | [```german-nlp-group/electra-base-german-uncased```](https://huggingface.co/german-nlp-group/electra-base-german-uncased) |
|**Finetuning task**| Passage Retrieval / Semantic Search |
|**Source dataset**| [```MSMARCO-Passage-Ranking```](https://github.com/microsoft/MSMARCO-Passage-Ranking) |
|**Translation model**| [```fairseq-wmt19-en-de```](https://github.com/pytorch/fairseq/tree/master/examples/wmt19) |
### Performance
We evaluated our model on the [GermanDPR testset](https://deepset.ai/germanquad) and followed the benchmark framework of [BEIR](https://github.com/UKPLab/beir).
In order to compare our results, we conducted an evaluation on the same test data with BM25 and presented the results in the table below.
We took every paragraph with negative and positive context out of the testset and deduplicated them. The resulting corpus size is 2871 against 1025 queries.
| Model | NDCG@1 | NDCG@5 | NDCG@10 | Recall@1 | Recall@5 | Recall@10 |
|:-------------------:|:------:|:------:|:-------:|:--------:|:--------:|:---------:|
| BM25 | 0.1463 | 0.3451 | 0.4097 | 0.1463 | 0.5424 | 0.7415 |
| BM25(Top 100) +Ours | 0.6410 | 0.7885 | 0.7943 | 0.6410 | 0.8576 | 0.9024 |
### How to Use
With ```sentence-transformers``` package (see [UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers) on GitHub for more details):
```python
from sentence_transformers.cross_encoder import CrossEncoder
cross_model = CrossEncoder("svalabs/cross-electra-ms-marco-german-uncased")
```
### Semantic Search Example
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
K = 3 # number of top ranks to retrieve
docs = [
"Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.",
"Der Gepard jagt seine Beute.",
"Wir haben in der Agentur ein neues System für Zeiterfassung.",
"Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.",
"Einen Impftermin kann mir der Arzt momentan noch nicht anbieten.",
"Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.",
"Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.",
"Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.",
"Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.",
"Bei ALDI sind die Bananen gerade im Angebot.",
"Die Entstehung der Erde ist 4,5 milliarden jahre her.",
"Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.",
"DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main."
]
queries = [
"dax steigt",
"dax sinkt",
"probleme mit knieschmerzen",
"software für urlaubsstunden",
"raubtier auf der jagd",
"alter der erde",
"wie alt ist unser planet?",
"wie kapital sichern",
"supermarkt lebensmittel reduziert",
"wodurch ist der tyrannosaurus aussgestorben",
"serien streamen"
]
# encode each query document pair
from itertools import product
combs = list(product(queries, docs))
outputs = cross_model.predict(combs).reshape((len(queries), len(docs)))
# print results
for i, query in enumerate(queries):
ranks = np.argsort(-outputs[i])
print("Query:", query)
for j, r in enumerate(ranks[:3]):
print(f"[{j}: {outputs[i, r]: .3f}]", docs[r])
print("-"*96)
```
**Console Output**:
```
Query: dax steigt
[0: 7.676] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[1: 0.821] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
[2: -9.905] Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.
------------------------------------------------------------------------------------------------
Query: dax sinkt
[0: 8.079] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
[1: -0.491] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[2: -9.224] Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.
------------------------------------------------------------------------------------------------
Query: probleme mit knieschmerzen
[0: 6.753] Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.
[1: -5.866] Einen Impftermin kann mir der Arzt momentan noch nicht anbieten.
[2: -9.461] Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.
------------------------------------------------------------------------------------------------
Query: software für urlaubsstunden
[0: 1.707] Wir haben in der Agentur ein neues System für Zeiterfassung.
[1: -10.649] Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.
[2: -11.280] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
------------------------------------------------------------------------------------------------
Query: raubtier auf der jagd
[0: 4.596] Der Gepard jagt seine Beute.
[1: -6.809] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[2: -8.392] Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.
------------------------------------------------------------------------------------------------
Query: alter der erde
[0: 7.343] Die Entstehung der Erde ist 4,5 milliarden jahre her.
[1: -7.664] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
[2: -8.020] Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.
------------------------------------------------------------------------------------------------
Query: wie alt ist unser planet?
[0: 7.672] Die Entstehung der Erde ist 4,5 milliarden jahre her.
[1: -9.638] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
[2: -10.251] Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.
------------------------------------------------------------------------------------------------
Query: wie kapital sichern
[0: 3.927] Um in Zukunft sein Vermögen zu schützen, sollte man andere Investmentstrategien in Betracht ziehen.
[1: -8.733] Finanzwerte treiben DAX um mehr als sechs Prozent nach oben Frankfurt/Main gegeben.
[2: -10.090] Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.
------------------------------------------------------------------------------------------------
Query: supermarkt lebensmittel reduziert
[0: 3.508] Bei ALDI sind die Bananen gerade im Angebot.
[1: -10.057] Das historische Zentrum (centro storico) liegt auf mehr als 100 Inseln in der Lagune von Venedig.
[2: -10.470] DAX dreht ins Minus. Konjunkturdaten und Gewinnmitnahmen belasten Frankfurt/Main.
------------------------------------------------------------------------------------------------
Query: wodurch ist der tyrannosaurus aussgestorben
[0: 0.079] Die Ära der Dinosaurier wurde vermutlich durch den Einschlag eines gigantischen Meteoriten auf der Erde beendet.
[1: -10.701] Mein Arzt sagt, dass mir dabei eher ein Orthopäde helfen könnte.
[2: -11.200] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
------------------------------------------------------------------------------------------------
Query: serien streamen
[0: 3.392] Auf Netflix gibt es endlich die neue Staffel meiner Lieblingsserie.
[1: -5.725] Der Gepard jagt seine Beute.
[2: -8.378] Auf Kreta hat meine Tochter mit Muscheln eine schöne Sandburg gebaut.
------------------------------------------------------------------------------------------------
```
### Contact
- Baran Avinc, [email protected]
- Jonas Grebe, [email protected]
- Lisa Stolz, [email protected]
- Bonian Riebe, [email protected]
### References
- N. Reimers and I. Gurevych (2019), ['Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'](https://arxiv.org/abs/1908.10084).
- Payal Bajaj et al. (2018), ['MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'](https://arxiv.org/abs/1611.09268).
- N. Thakur et al. (2021), ['BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models'](https://arxiv.org/abs/2104.08663).
- T. Möller, J. Risch and M. Pietsch (2021), ['GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval'](https://arxiv.org/abs/2104.12741).
- Hofstätter et al. (2021), ['Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation'](https://arxiv.org/abs/2010.02666)
|
svalabs/ger-roberta | 2021-05-20T22:04:35.000Z | [
"pytorch",
"jax",
"roberta",
"masked-lm",
"transformers",
"fill-mask"
] | fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"merges.txt",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"training_args.bin",
"vocab.json"
] | svalabs | 40 | transformers | |
sven1977/test_model | 2021-06-13T18:51:21.000Z | [] | [
".gitattributes"
] | sven1977 | 0 | |||
sw005320/Shinji-Watanabe-ami_asr_train_asr_e85_raw_en_bpe100_optim_conflr5.0_sp_valid.acc.ave-fs16k-langen | 2020-12-29T21:42:07.000Z | [] | [
".gitattributes"
] | sw005320 | 0 | |||
swapnil2911/DialoGPT-small-arya | 2021-06-09T06:27:55.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swapnil2911 | 9 | transformers | pipeline_tag:conversational |
swapnil2911/DialoGPT-test-arya | 2021-06-09T06:19:33.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swapnil2911 | 2 | transformers | pipeline_tag: conversational |
swapnil2911/test | 2021-06-09T06:28:29.000Z | [] | [
".gitattributes",
"README.md"
] | swapnil2911 | 0 | pipeline_tag: conversational |
||
swcrazyfan/TE-v3-10K | 2021-05-29T03:21:08.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 14 | transformers | |
swcrazyfan/TE-v3-12K | 2021-05-29T06:32:52.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 28 | transformers | |
swcrazyfan/TE-v3-3K | 2021-05-28T06:38:28.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 22 | transformers | |
swcrazyfan/TE-v3-8K | 2021-05-28T12:26:43.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 23 | transformers | |
swcrazyfan/TEFL-2.7B-10K | 2021-06-10T03:25:02.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 22 | transformers | |
swcrazyfan/TEFL-2.7B-15K | 2021-06-10T09:20:21.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 0 | transformers | |
swcrazyfan/TEFL-2.7B-4K | 2021-06-04T15:58:19.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 6 | transformers | |
swcrazyfan/TEFL-2.7B-6K | 2021-06-05T07:53:03.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 1 | transformers | |
swcrazyfan/TEFL-V3 | 2021-06-14T07:17:34.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 5 | transformers | |
swcrazyfan/TEFL-blogging-9K | 2021-06-03T01:32:49.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 22 | transformers | |
swcrazyfan/gpt-neo-1.3B-TBL | 2021-05-21T05:43:27.000Z | [
"pytorch",
"gpt_neo",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | swcrazyfan | 99 | transformers | |
swheel/nkuCordBot | 2021-06-04T03:35:36.000Z | [] | [
".gitattributes"
] | swheel | 0 | |||
sy7/first | 2021-05-29T12:20:33.000Z | [] | [
".gitattributes"
] | sy7 | 0 | |||
sybae/BertPractice | 2021-01-31T10:03:33.000Z | [] | [
".gitattributes",
"README.md"
] | sybae | 0 | BERT Implication |
||
sybk/highkick-soonjae-v2 | 2021-05-31T04:23:02.000Z | [
"pytorch",
"gpt2",
"transformers"
] | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sybk | 65 | transformers | ||
sybk/highkick-soonjae | 2021-05-23T14:38:21.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sybk | 39 | transformers | |
sybk/hk-backward | 2021-05-23T14:41:39.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sybk | 38 | transformers | |
sybk/hk_backward_v2 | 2021-05-31T04:17:16.000Z | [
"pytorch",
"gpt2",
"transformers"
] | [
".gitattributes",
"config.json",
"pytorch_model.bin"
] | sybk | 90 | transformers | ||
t4peter/testModel | 2021-04-14T19:38:45.000Z | [] | [
".gitattributes"
] | t4peter | 0 | |||
taeminlee/kodialogpt2-base | 2021-05-23T13:03:30.000Z | [
"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",
"vocab.json"
] | taeminlee | 66 | transformers | |
taeminlee/kogpt2 | 2021-05-23T13:04:34.000Z | [
"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",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | taeminlee | 476 | transformers | # KoGPT2-Transformers
KoGPT2 on Huggingface Transformers
### KoGPT2-Transformers
- [SKT-AI 에서 공개한 KoGPT2 (ver 1.0)](https://github.com/SKT-AI/KoGPT2)를 [Transformers](https://github.com/huggingface/transformers)에서 사용하도록 하였습니다.
- **SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. https://huggingface.co/skt/kogpt2-base-v2/**
### Demo
- 일상 대화 챗봇 : http://demo.tmkor.com:36200/dialo
- 화장품 리뷰 생성 : http://demo.tmkor.com:36200/ctrl
### Example
```python
from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast
model = GPT2LMHeadModel.from_pretrained("taeminlee/kogpt2")
tokenizer = PreTrainedTokenizerFast.from_pretrained("taeminlee/kogpt2")
input_ids = tokenizer.encode("안녕", add_special_tokens=False, return_tensors="pt")
output_sequences = model.generate(input_ids=input_ids, do_sample=True, max_length=100, num_return_sequences=3)
for generated_sequence in output_sequences:
generated_sequence = generated_sequence.tolist()
print("GENERATED SEQUENCE : {0}".format(tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)))
``` |
taharushain/postive_negative_emotions | 2021-03-12T03:36:15.000Z | [] | [
".gitattributes"
] | taharushain | 0 | |||
tals/albert-base-mnli | 2021-03-22T00:37:13.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 90 | transformers | |
tals/albert-base-vitaminc-fever | 2021-03-22T14:06:31.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 103 | transformers | |
tals/albert-base-vitaminc-mnli | 2021-03-22T16:06:08.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 15 | transformers | |
tals/albert-base-vitaminc | 2021-03-22T16:03:08.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 164 | transformers | |
tals/albert-base-vitaminc_flagging | 2021-03-22T16:13:02.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 300 | transformers | |
tals/albert-base-vitaminc_rationale | 2021-06-11T16:51:37.000Z | [
"pytorch",
"albert",
"transformers"
] | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 7 | transformers | ||
tals/albert-base-vitaminc_wnei-fever | 2021-06-11T16:25:01.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 5 | transformers | |
tals/albert-xlarge-vitaminc-fever | 2021-03-22T13:55:16.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 67 | transformers | |
tals/albert-xlarge-vitaminc-mnli | 2021-03-22T16:08:15.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 1,122 | transformers | |
tals/albert-xlarge-vitaminc | 2021-03-22T01:58:15.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tals | 36 | transformers | |
tanay/xlm-fine-tuned | 2021-03-22T05:13:25.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
] | tanay | 7 | transformers | |
tankhead200/J123897 | 2021-03-06T07:40:51.000Z | [] | [
".gitattributes"
] | tankhead200 | 0 | |||
tanmaylaud/wav2vec2-large-xlsr-hindi-marathi | 2021-04-19T18:40:07.000Z | [
"pytorch",
"wav2vec2",
"mr",
"hi",
"dataset:openslr",
"dataset:interspeech_2021_asr",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"hindi",
"marathi",
"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",
".ipynb_checkpoints/README-checkpoint.md",
".ipynb_checkpoints/vocab-checkpoint.json"
] | tanmaylaud | 266 | transformers | ---
language: mr
datasets:
- openslr
- interspeech_2021_asr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hindi
- marathi
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 Hindi-Marathi by Tanmay Laud
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR hi, OpenSLR mr
type: openslr, interspeech_2021_asr
metrics:
- name: Test WER
type: wer
value: 24.92
---
# Wav2Vec2-Large-XLSR-53-Hindi-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi text and audio_path fields:
```
import torch
import torchaudio
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
# 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["audio_path"])
batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary
return batch
test_data= test_data.map(speech_file_to_array_fn)
inputs = processor(test_data["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_data["text"][:2])
Evaluation
The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
```
```
import torchaudio
from datasets import load_metric
from transformers import Wav2Vec2Processor,Wav2Vec2ForCTC
import torch
import librosa
import numpy as np
import re
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।]'
# 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"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array[0].numpy()
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
batch["speech"] = librosa.resample(np.asarray(batch["speech"]), sampling_rate, 16_000)
batch["sampling_rate"] = 16_000
return batch
test= test.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, group_tokens=False)
# we do not want to group tokens when computing the metrics
return batch
result = test.map(evaluate, batched=True, batch_size=32)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
```
Link to eval notebook : https://colab.research.google.com/drive/1nZRTgKfxCD9cvy90wikTHkg2il3zgcqW#scrollTo=cXWFbhb0d7DT
|
tanmoyio/wav2vec2-large-xlsr-bengali | 2021-03-29T17:28:42.000Z | [
"pytorch",
"wav2vec2",
"Bengali",
"dataset:OpenSLR",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:attribution-sharealike 4.0 international"
] | automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] | tanmoyio | 97 | transformers | ---
language: Bengali
datasets:
- OpenSLR
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: Attribution-ShareAlike 4.0 International
model-index:
- name: XLSR Wav2Vec2 Bengali by Tanmoy Sarkar
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR
type: OpenSLR
args: ben
metrics:
- name: Test WER
type: wer
value: 88.58
---
# Wav2Vec2-Large-XLSR-Bengali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using the [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Dataset must be downloaded from [this website](https://www.openslr.org/53/) and preprocessed accordingly. For example 1250 test samples has been chosen.
```python
import pandas as pd
test_dataset = pd.read_csv('utt_spk_text.tsv', sep='\\t', header=None)[60000:61250]
test_dataset.columns = ["audio_path", "__", "label"]
test_dataset = test_data.drop("__", axis=1)
def add_file_path(text):
path = "data/" + text[:2] + "/" + text + '.flac'
return path
test_dataset['audio_path'] = test_dataset['audio_path'].map(lambda x: add_file_path(x))
```
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
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["audio_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["label"][:2])
```
## Evaluation
The model can be evaluated as follows on the Bengali test data of OpenSLR.
```python
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model.to("cuda")
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["label"]).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**: 88.58 %
## Training
The script used for training can be found [Bengali ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1Bkc5C_cJV9BeS0FD0MuHyayl8hqcbdRZ?usp=sharing)
|
tareknaous/arabic-empathetic-bert2bert | 2021-05-30T15:53:49.000Z | [] | [
".gitattributes"
] | tareknaous | 0 | |||
tartuNLP/EstBERT | 2021-05-20T07:21:03.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"et",
"transformers",
"fill-mask"
] | fill-mask | [
".gitattributes",
"README.md",
"bert_config.json",
"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"
] | tartuNLP | 728 | transformers | ---
language: et
---
# EstBERT
### What's this?
The EstBERT model is a pretrained BERT<sub>Base</sub> model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.
### How to use?
You can use the model transformer library both in tensorflow and pytorch version.
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT")
model = AutoModelForMaskedLM.from_pretrained("tartuNLP/EstBERT")
```
You can also download the pretrained model from here, [EstBERT_128]() [EstBERT_512]()
#### Dataset used to train the model
The EstBERT model is trained both on 128 and 512 sequence length of data. For training the EstBERT we used the [Estonian National Corpus 2017](https://metashare.ut.ee/repository/browse/estonian-national-corpus-2017/b616ceda30ce11e8a6e4005056b40024880158b577154c01bd3d3fcfc9b762b3/), which was the largest Estonian language corpus available at the time. It consists of four sub-corpora: Estonian Reference Corpus 1990-2008, Estonian Web Corpus 2013, Estonian Web Corpus 2017 and Estonian Wikipedia Corpus 2017.
### Why would I use?
Overall EstBERT performs better in parts of speech (POS), name entity recognition (NER), rubric, and sentiment classification tasks compared to mBERT and XLM-RoBERTa. The comparative results can be found below;
|Model |UPOS |XPOS |Morph |bf UPOS |bf XPOS |Morph |
|--------------|----------------------------|-------------|-------------|-------------|----------------------------|----------------------------|
| EstBERT | **_97.89_** | **98.40** | **96.93** | **97.84** | **_98.43_** | **_96.80_** |
| mBERT | 97.42 | 98.06 | 96.24 | 97.43 | 98.13 | 96.13 |
| XLM-RoBERTa | 97.78 | 98.36 | 96.53 | 97.80 | 98.40 | 96.69 |
|Model|Rubric<sub>128</sub> |Sentiment<sub>128</sub> | Rubric<sub>128</sub> |Sentiment<sub>512</sub> |
|-------------------|----------------------------|--------------------|-----------------------------------------------|----------------------------|
| EstBERT | **_81.70_** | 74.36 | **80.96** | 74.50 |
| mBERT | 75.67 | 70.23 | 74.94 | 69.52 |
| XLM\-RoBERTa | 80.34 | **74.50** | 78.62 | **_76.07_**|
|Model |Precicion<sub>128</sub> |Recall<sub>128</sub> |F1-Score<sub>128</sub> |Precision<sub>512</sub> |Recall<sub>512</sub> |F1-Score<sub>512</sub> |
|--------------|----------------|----------------------------|----------------------------|----------------------------|-------------|----------------|
| EstBERT | **88.42** | 90.38 |**_89.39_** | 88.35 | 89.74 | 89.04 |
| mBERT | 85.88 | 87.09 | 86.51 |**_88.47_** | 88.28 | 88.37 |
| XLM\-RoBERTa | 87.55 |**_91.19_** | 89.34 | 87.50 | **90.76** | **89.10** |
|
tartuNLP/EstBERT_512 | 2021-05-20T07:22:02.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
] | fill-mask | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] | tartuNLP | 25 | transformers | |
tartuNLP/EstBERT_Morph_128 | 2021-05-26T06:48:09.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers"
] | token-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | tartuNLP | 11 | transformers | |
tartuNLP/EstBERT_NER | 2021-05-20T07:23:20.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"arxiv:2011.04784",
"transformers"
] | token-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | tartuNLP | 45 | transformers | # EstBERT_NER
## Model description
EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.org/anthology/W13-2412.pdf). It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER).
## How to use
You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities.
```
from transformers import BertTokenizer, BertForTokenClassification
from transformers import pipeline
tokenizer = BertTokenizer.from_pretrained('tartuNLP/EstBERT_NER')
bertner = BertForTokenClassification.from_pretrained('tartuNLP/EstBERT_NER')
nlp = pipeline("ner", model=bertner, tokenizer=tokenizer)
sentence = 'Eesti Ekspressi teada on Eesti Pank uurinud Hansapanga tehinguid , mis toimusid kaks aastat tagasi suvel ja mille käigus voolas panka ligi miljardi krooni ulatuses kahtlast raha .'
ner_results = nlp(sentence)
print(ner_results)
```
```
[{'word': 'Eesti', 'score': 0.9964128136634827, 'entity': 'B-ORG', 'index': 1}, {'word': 'Ekspressi', 'score': 0.9978809356689453, 'entity': 'I-ORG', 'index': 2}, {'word': 'Eesti', 'score': 0.9988121390342712, 'entity': 'B-ORG', 'index': 5}, {'word': 'Pank', 'score': 0.9985784292221069, 'entity': 'I-ORG', 'index': 6}, {'word': 'Hansapanga', 'score': 0.9979034662246704, 'entity': 'B-ORG', 'index': 8}]
```
## BibTeX entry and citation info
```
@misc{tanvir2020estbert,
title={EstBERT: A Pretrained Language-Specific BERT for Estonian},
author={Hasan Tanvir and Claudia Kittask and Kairit Sirts},
year={2020},
eprint={2011.04784},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
tartuNLP/EstBERT_UPOS_128 | 2021-05-26T06:52:21.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers"
] | token-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | tartuNLP | 27 | transformers | |
tartuNLP/EstBERT_XPOS_128 | 2021-05-26T06:55:41.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers"
] | token-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | tartuNLP | 35 | transformers | |
taylor/testing | 2021-03-27T04:14:41.000Z | [] | [
".gitattributes",
"README.md"
] | taylor | 0 | |||
tblard/tf-allocine | 2020-12-11T22:02:40.000Z | [
"tf",
"camembert",
"text-classification",
"fr",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json"
] | tblard | 6,183 | transformers | ---
language: fr
---
# tf-allociné
A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
## Results
| Validation Accuracy | Validation F1-Score | Test Accuracy | Test F1-Score |
|--------------------:| -------------------:| -------------:|--------------:|
| 97.39 | 97.36 | 97.44 | 97.34 |
The dataset and the evaluation code are available on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).
## Usage
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("tblard/tf-allocine")
model = TFAutoModelForSequenceClassification.from_pretrained("tblard/tf-allocine")
nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
print(nlp("Alad'2 est clairement le meilleur film de l'année 2018.")) # POSITIVE
print(nlp("Juste whoaaahouuu !")) # POSITIVE
print(nlp("NUL...A...CHIER ! FIN DE TRANSMISSION.")) # NEGATIVE
print(nlp("Je m'attendais à mieux de la part de Franck Dubosc !")) # NEGATIVE
```
## Author
Théophile Blard – :email: [email protected]
If you use this work (code, model or dataset), please cite as:
> Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>
|
tbs17/MathBERT-custom | 2021-06-17T16:41:41.000Z | [
"pytorch",
"bert",
"masked-lm",
"transformers",
"fill-mask"
] | fill-mask | [
".gitattributes",
"README.md",
"config.json",
"math-vocab.txt",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json"
] | tbs17 | 31 | transformers | #### MathBERT model (custom vocab)
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of English math corpus data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the math language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MathBERT model as inputs.
#### Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a math-related downstream task.
Note that this model is primarily aimed at being fine-tuned on math-related tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as math text generation you should look at model like GPT2.
#### How to use
<!---You can use this model directly with a pipeline for masked language modeling:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]--->
Here is how to use this model to get the features of a given text in PyTorch:
```from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT-custom')
model = BertModel.from_pretrained("tbs17/MathBERT-custom")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(encoded_input)
```
and in TensorFlow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT-custom')
model = TFBertModel.from_pretrained("tbs17/MathBERT-custom")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
<!---Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
This bias will also affect all fine-tuned versions of this model.--->
Training data
The BERT model was pretrained on pre-k to HS math curriculum (engageNY, Utah Math, Illustrative Math), college math books from openculture.com as well as graduate level math from arxiv math paper abstracts. There is about 100M tokens got pretrained on.
#### Training procedure
The texts are lowercased and tokenized using WordPiece and a customized vocabulary size of 30,522. We use the ```bert_tokenizer``` from huggingface tokenizers library to generate a custom vocab file from our training raw math texts. The inputs of the model are then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
+ 15% of the tokens are masked.
+ In 80% of the cases, the masked tokens are replaced by [MASK].
+ In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+ In the 10% remaining cases, the masked tokens are left as is.
#### Pretraining
The model was trained on a 8-core cloud TPUs from Google Colab for 600k steps with a batch size of 128. The sequence length was limited to 512 for the entire time. The optimizer used is Adam with a learning rate of 5e-5, beta_{1} = 0.9 and beta_{2} =0.999, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. |
tbs17/MathBERT | 2021-06-17T19:04:57.000Z | [
"pytorch",
"bert",
"masked-lm",
"transformers",
"fill-mask"
] | fill-mask | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | tbs17 | 161 | transformers | #### MathBERT model (original vocab)
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of English math corpus data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the math language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MathBERT model as inputs.
#### Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a math-related downstream task.
Note that this model is primarily aimed at being fine-tuned on math-related tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as math text generation you should look at model like GPT2.
#### How to use
<!---You can use this model directly with a pipeline for masked language modeling:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]--->
Here is how to use this model to get the features of a given text in PyTorch:
```from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT')
model = BertModel.from_pretrained("tbs17/MathBERT")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(encoded_input)
```
and in TensorFlow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT')
model = TFBertModel.from_pretrained("tbs17/MathBERT")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
<!---Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
This bias will also affect all fine-tuned versions of this model.--->
#### Training data
The MathBERT model was pretrained on pre-k to HS math curriculum (engageNY, Utah Math, Illustrative Math), college math books from openculture.com as well as graduate level math from arxiv math paper abstracts. There is about 100M tokens got pretrained on.
#### Training procedure
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522 which is from original BERT vocab.txt. The inputs of the model are then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentence spans from the original corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence, but less than 512 tokens.
The details of the masking procedure for each sentence are the following:
+ 15% of the tokens are masked.
+ In 80% of the cases, the masked tokens are replaced by [MASK].
+ In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+ In the 10% remaining cases, the masked tokens are left as is.
#### Pretraining
The model was trained on a 8-core cloud TPUs from Google Colab for 600k steps with a batch size of 128. The sequence length was limited to 512 for the entire time. The optimizer used is Adam with a learning rate of 5e-5, beta_{1} = 0.9 and beta_{2} =0.999, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. |
tcaputi/guns-relevant-b300 | 2021-05-20T07:24:39.000Z | [
"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"
] | tcaputi | 13 | transformers | |
tcaputi/guns-relevant | 2021-05-20T07:25:33.000Z | [
"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"
] | tcaputi | 44 | transformers | |
tdrt67ijk/oefsjdkx | 2021-06-13T05:46:15.000Z | [] | [
".gitattributes",
"README.md"
] | tdrt67ijk | 0 | |||
techthiyanes/Bert_Bahasa_Sentiment | 2021-05-20T07:26:52.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | techthiyanes | 20 | transformers | from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained('techthiyanes/Bert_Bahasa_Sentiment')
inputs = tokenizer("saya tidak", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
outputs
hello
|
techthiyanes/bert-base-mulitilingual-bahasa-sentiment | 2021-05-09T02:34:07.000Z | [] | [
".gitattributes"
] | techthiyanes | 0 | |||
techthiyanes/chinese_sentiment | 2021-05-20T07:28:06.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
] | techthiyanes | 490 | transformers | |
techthiyanes/trainedones | 2021-05-09T02:42:36.000Z | [] | [
".gitattributes",
"README.md"
] | techthiyanes | 0 | |||
tehyw/test | 2021-05-10T01:25:58.000Z | [] | [
".gitattributes",
"README.md"
] | tehyw | 0 | |||
teleportHQ/predicto_css | 2021-05-23T13:05:04.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] | teleportHQ | 13 | transformers | predicto css model
|
teleportHQ/predicto_tsx | 2021-05-23T13:05:19.000Z | [
"pytorch",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
] | text-generation | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
] | teleportHQ | 6 | transformers | predicto css model
|
tengzhiyong/risk-cls | 2020-12-02T02:37:42.000Z | [] | [
".gitattributes"
] | tengzhiyong | 0 | |||
tennessejoyce/titlewave-bert-base-uncased | 2021-05-20T07:29:09.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"transformers",
"license:cc-by-4.0"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | tennessejoyce | 18 | transformers | ---
language: en
license: cc-by-4.0
widget:
- text: "[Gmail API] How can I extract plain text from an email sent to me?"
---
# Titlewave: bert-base-uncased
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the [github repository](https://github.com/tennessejoyce/TitleWave) for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to classify whether question will be answered or not just based on the title. The [companion model](https://huggingface.co/tennessejoyce/titlewave-t5-small) suggests a new title based on on the body of the question.
## Intended use
Try out different titles for your Stack Overflow post, and see which one gives you the best chance of receiving an answer.
You can use the model through the API on this page (hosted by HuggingFace) or install the Chrome extension by following the instructions on the [github repository](https://github.com/tennessejoyce/TitleWave), which integrates the tool directly into the Stack Overflow website.
You can also run the model locally in Python like this (which automatically downloads the model to your machine):
```python
>>> from transformers import pipeline
>>> classifier = pipeline('sentiment-analysis', model='tennessejoyce/titlewave-bert-base-uncased')
>>> classifier('[Gmail API] How can I extract plain text from an email sent to me?')
[{'label': 'Answered', 'score': 0.8053370714187622}]
```
The 'score' in the output represents the probability of getting an answer with this title: 80.5%.
## Training data
The weights were initialized from the [BERT base model](https://huggingface.co/bert-base-uncased), which was trained on BookCorpus and English Wikipedia.
Then the model was fine-tuned on the dataset of previous Stack Overflow post titles, which is publicly available [here](https://archive.org/details/stackexchange).
Specifically I used three years of posts from 2017-2019, filtered out posts which were closed (e.g., duplicates, off-topic), and selected 5% of the remaining posts at random to use in the training set, and the same amount for validation and test sets (278,155 posts each).
## Training procedure
The model was fine-tuned for two epochs with a batch size of 32 (17,384 steps total) using 16-bit mixed precision.
After some hyperparameter tuning, I found that the following two-phase training procedure yields the best performance (ROC-AUC score) on the validation set:
* In the first epoch, all layers were frozen except for the last two (pooling layer and classification layer) and a learning rate of 3e-4 was used.
* In the second epoch all layers were unfrozen, and the learning rate was decreased by a factor of 10 to 3e-5.
Otherwise, all parameters we set to the defaults listed [here](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments),
including the AdamW optimizer and a linearly decreasing learning schedule (both of which were reset between the two epochs). See the [github repository](https://github.com/tennessejoyce/TitleWave) for the scripts that were used to train the model.
## Evaluation
See [this notebook](https://github.com/tennessejoyce/TitleWave/blob/master/model_training/test_classifier.ipynb) for the performance of the title classification model on the test set.
|
tennessejoyce/titlewave-t5-base | 2021-03-09T16:47:18.000Z | [
"pytorch",
"t5",
"seq2seq",
"en",
"transformers",
"license:cc-by-4.0",
"summarization",
"pipeline_tag:summarization",
"text2text-generation"
] | summarization | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
] | tennessejoyce | 178 | transformers | ---
language: en
license: cc-by-4.0
pipeline_tag: summarization
widget:
- text: "Example question body."
---
# Titlewave: t5-base
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See https://github.com/tennessejoyce/TitleWave for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title based on on the body of the question. The companion model (https://huggingface.co/tennessejoyce/titlewave-bert-base-uncased) classifies whether question will be answered or not just based on the title
## Intended use
Try out different titles for your Stack Overflow post, and see which one gives you the best chance of recieving an answer.
This model can be used in your browser as a Chrome extension by following the installation instructions at https://github.com/tennessejoyce/TitleWave.
Or load it in Python like this (which will automatically download the model to your machine):
```python
>>> from transformers import pipeline
>>> classifier = pipeline('summarization', model='tennessejoyce/titlewave-t5-base')
>>> body = """"Example question body."""
>>> classifier(body)
[{'summary_text': 'Example title suggestion?'}]
```
## Training data
The weights were initialized from the BERT base model (https://huggingface.co/bert-base-uncased), which was trained on BookCorpus and English Wikipedia.
Then the model was fine-tuned on the dataset of previous Stack Overflow post titles (https://archive.org/details/stackexchange).
Specifically I used three years of posts from 2017-2019, filtered out posts which were closed, and selected 25% of the remaining posts at random to use in
the training set. In order to improve the quality of the titles generated, the model was trained only on questions with an accepted answer.
## Evaluation
See https://github.com/tennessejoyce/TitleWave/blob/master/model_training/test_summarizer.ipynb for the performance of the title generation model on the test set.
|
tennessejoyce/titlewave-t5-small | 2021-03-09T04:03:11.000Z | [
"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",
"training_args.bin"
] | tennessejoyce | 10 | transformers | # Titlewave: t5-small
This is one of two models used in the Titlewave project. See https://github.com/tennessejoyce/TitleWave for more information.
This model was fine-tuned on a dataset of Stack Overflow posts, with a ConditionalGeneration head that summarizes the body of a question in order to suggest a title.
|
tensorspeech/tts-fastspeech-ljspeech-en | 2021-06-01T09:52:36.000Z | [
"eng",
"dataset:LJSpeech",
"arxiv:1905.09263",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: eng
license: apache-2.0
datasets:
- LJSpeech
widget:
- text: "How are you?"
---
# FastSpeech trained on LJSpeech (Eng)
This repository provides a pretrained [FastSpeech](https://arxiv.org/abs/1905.09263) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech-ljspeech-en")
fastspeech = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech-ljspeech-en")
text = "How are you?"
input_ids = processor.text_to_sequence(text)
mel_before, mel_after, duration_outputs = fastspeech.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
)
```
#### Referencing FastSpeech
```
@article{DBLP:journals/corr/abs-1905-09263,
author = {Yi Ren and
Yangjun Ruan and
Xu Tan and
Tao Qin and
Sheng Zhao and
Zhou Zhao and
Tie{-}Yan Liu},
title = {FastSpeech: Fast, Robust and Controllable Text to Speech},
journal = {CoRR},
volume = {abs/1905.09263},
year = {2019},
url = {http://arxiv.org/abs/1905.09263},
archivePrefix = {arXiv},
eprint = {1905.09263},
timestamp = {Wed, 11 Nov 2020 08:48:07 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-09263.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-fastspeech2-baker-ch | 2021-06-02T02:51:55.000Z | [
"chinese",
"dataset:Baker",
"arxiv:2006.04558",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: chinese
license: apache-2.0
datasets:
- Baker
widget:
- text: "这是一个开源的端到端中文语音合成系统"
---
# FastSpeech2 trained on Baker (Chinese)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-baker-ch")
fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-baker-ch")
text = "这是一个开源的端到端中文语音合成系统"
input_ids = processor.text_to_sequence(text, inference=True)
mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)
```
#### Referencing FastSpeech2
```
@misc{ren2021fastspeech,
title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
year={2021},
eprint={2006.04558},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-fastspeech2-kss-ko | 2021-06-11T03:03:15.000Z | [
"ko",
"dataset:KSS",
"arxiv:2006.04558",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: ko
license: apache-2.0
datasets:
- KSS
widget:
- text: "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
---
# FastSpeech2 trained on KSS (Korean)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-kss-ko")
fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-kss-ko")
text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
input_ids = processor.text_to_sequence(text)
mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)
```
#### Referencing FastSpeech2
```
@misc{ren2021fastspeech,
title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
year={2021},
eprint={2006.04558},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-fastspeech2-ljspeech-en | 2021-06-01T09:54:05.000Z | [
"eng",
"dataset:LJSpeech",
"arxiv:2006.04558",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: eng
license: apache-2.0
datasets:
- LJSpeech
widget:
- text: "How are you?"
---
# FastSpeech2 trained on LJSpeech (Eng)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")
fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")
text = "How are you?"
input_ids = processor.text_to_sequence(text)
mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)
```
#### Referencing FastSpeech2
```
@misc{ren2021fastspeech,
title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
year={2021},
eprint={2006.04558},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-mb_melgan-baker-ch | 2021-06-02T02:50:59.000Z | [
"ch",
"dataset:Baker",
"arxiv:2005.05106",
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- mel-to-wav
language: ch
license: apache-2.0
datasets:
- Baker
widget:
- text: "这是一个开源的端到端中文语音合成系统"
---
# Multi-band MelGAN trained on Baker (Ch)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Baker dataset (ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Wav
```python
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-baker-ch")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-baker-ch")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-baker-ch")
text = "这是一个开源的端到端中文语音合成系统"
input_ids = processor.text_to_sequence(text, inference=True)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
```
#### Referencing Multi-band MelGAN
```
@misc{yang2020multiband,
title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech},
author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie},
year={2020},
eprint={2005.05106},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-mb_melgan-kss-ko | 2021-06-01T09:06:04.000Z | [
"ko",
"dataset:KSS",
"arxiv:2005.05106",
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- mel-to-wav
language: ko
license: apache-2.0
datasets:
- KSS
widget:
- text: "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
---
# Multi-band MelGAN trained on KSS (Korean)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Wav
```python
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-kss-ko")
text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
input_ids = processor.text_to_sequence(text)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
```
#### Referencing Multi-band MelGAN
```
@misc{yang2020multiband,
title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech},
author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie},
year={2020},
eprint={2005.05106},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-mb_melgan-ljspeech-en | 2021-06-01T09:54:44.000Z | [
"en",
"dataset:ljspeech",
"arxiv:2005.05106",
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- mel-to-wav
language: en
license: apache-2.0
datasets:
- ljspeech
widget:
- text: "Hello, how are you doing?"
---
# Multi-band MelGAN trained on LJSpeech (En)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Wav
```python
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en")
text = "This is a demo to show how to use our model to generate mel spectrogram from raw text."
input_ids = processor.text_to_sequence(text)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
```
#### Referencing Multi-band MelGAN
```
@misc{yang2020multiband,
title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech},
author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie},
year={2020},
eprint={2005.05106},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-mb_melgan-thorsten-ger | 2021-06-01T09:07:00.000Z | [
"ger",
"dataset:Thorsten",
"arxiv:2005.05106",
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- mel-to-wav
language: ger
license: apache-2.0
datasets:
- Thorsten
widget:
- text: "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht."
---
# Multi-band MelGAN trained on Thorsten (Ger)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Thorsten dataset (ger). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Wav
```python
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-thorsten-ger")
text = "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht."
input_ids = processor.text_to_sequence(text)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
```
#### Referencing Multi-band MelGAN
```
@misc{yang2020multiband,
title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech},
author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie},
year={2020},
eprint={2005.05106},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-melgan-ljspeech-en | 2021-06-01T09:55:16.000Z | [
"en",
"dataset:ljspeech",
"arxiv:1910.06711",
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- mel-to-wav
language: en
license: apache-2.0
datasets:
- ljspeech
widget:
- text: "Hello, how are you doing?"
---
# MelGAN trained on LJSpeech (En)
This repository provides a pretrained [MelGAN](https://arxiv.org/abs/1910.06711) trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Wav
```python
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
melgan = TFAutoModel.from_pretrained("tensorspeech/tts-melgan-ljspeech-en")
text = "This is a demo to show how to use our model to generate mel spectrogram from raw text."
input_ids = processor.text_to_sequence(text)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
```
#### Referencing MelGAN
```
@misc{kumar2019melgan,
title={MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis},
author={Kundan Kumar and Rithesh Kumar and Thibault de Boissiere and Lucas Gestin and Wei Zhen Teoh and Jose Sotelo and Alexandre de Brebisson and Yoshua Bengio and Aaron Courville},
year={2019},
eprint={1910.06711},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-tacotron2-baker-ch | 2021-06-02T02:50:20.000Z | [
"ch",
"dataset:baker",
"arxiv:1712.05884",
"arxiv:1710.08969",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: ch
license: apache-2.0
datasets:
- baker
widget:
- text: "这是一个开源的端到端中文语音合成系统"
---
# Tacotron 2 with Guided Attention trained on Baker (Chinese)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-baker-ch")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-baker-ch")
text = "这是一个开源的端到端中文语音合成系统"
input_ids = processor.text_to_sequence(text, inference=True)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
```
#### Referencing Tacotron 2
```
@article{DBLP:journals/corr/abs-1712-05884,
author = {Jonathan Shen and
Ruoming Pang and
Ron J. Weiss and
Mike Schuster and
Navdeep Jaitly and
Zongheng Yang and
Zhifeng Chen and
Yu Zhang and
Yuxuan Wang and
R. J. Skerry{-}Ryan and
Rif A. Saurous and
Yannis Agiomyrgiannakis and
Yonghui Wu},
title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions},
journal = {CoRR},
volume = {abs/1712.05884},
year = {2017},
url = {http://arxiv.org/abs/1712.05884},
archivePrefix = {arXiv},
eprint = {1712.05884},
timestamp = {Thu, 28 Nov 2019 08:59:52 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-tacotron2-kss-ko | 2021-06-01T09:56:01.000Z | [
"ko",
"dataset:kss",
"arxiv:1712.05884",
"arxiv:1710.08969",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: ko
license: apache-2.0
datasets:
- kss
widget:
- text: "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
---
# Tacotron 2 with Guided Attention trained on KSS (Korean)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on KSS dataset (KO). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
input_ids = processor.text_to_sequence(text)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
```
#### Referencing Tacotron 2
```
@article{DBLP:journals/corr/abs-1712-05884,
author = {Jonathan Shen and
Ruoming Pang and
Ron J. Weiss and
Mike Schuster and
Navdeep Jaitly and
Zongheng Yang and
Zhifeng Chen and
Yu Zhang and
Yuxuan Wang and
R. J. Skerry{-}Ryan and
Rif A. Saurous and
Yannis Agiomyrgiannakis and
Yonghui Wu},
title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions},
journal = {CoRR},
volume = {abs/1712.05884},
year = {2017},
url = {http://arxiv.org/abs/1712.05884},
archivePrefix = {arXiv},
eprint = {1712.05884},
timestamp = {Thu, 28 Nov 2019 08:59:52 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-tacotron2-ljspeech-en | 2021-06-01T09:56:19.000Z | [
"en",
"dataset:ljspeech",
"arxiv:1712.05884",
"arxiv:1710.08969",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: en
license: apache-2.0
datasets:
- ljspeech
widget:
- text: "Hello, how are you doing?"
---
# Tacotron 2 with Guided Attention trained on LJSpeech (En)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
text = "This is a demo to show how to use our model to generate mel spectrogram from raw text."
input_ids = processor.text_to_sequence(text)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
```
#### Referencing Tacotron 2
```
@article{DBLP:journals/corr/abs-1712-05884,
author = {Jonathan Shen and
Ruoming Pang and
Ron J. Weiss and
Mike Schuster and
Navdeep Jaitly and
Zongheng Yang and
Zhifeng Chen and
Yu Zhang and
Yuxuan Wang and
R. J. Skerry{-}Ryan and
Rif A. Saurous and
Yannis Agiomyrgiannakis and
Yonghui Wu},
title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions},
journal = {CoRR},
volume = {abs/1712.05884},
year = {2017},
url = {http://arxiv.org/abs/1712.05884},
archivePrefix = {arXiv},
eprint = {1712.05884},
timestamp = {Thu, 28 Nov 2019 08:59:52 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
tensorspeech/tts-tacotron2-thorsten-ger | 2021-06-01T09:56:43.000Z | [
"german",
"dataset:Thorsten",
"arxiv:1712.05884",
"arxiv:1710.08969",
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"license:apache-2.0"
] | text-to-speech | [
".gitattributes",
"README.md",
"config.yml",
"model.h5",
"processor.json"
] | tensorspeech | 0 | tensorflowtts | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: german
license: apache-2.0
datasets:
- Thorsten
widget:
- text: "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht."
---
# Tacotron 2 with Guided Attention trained on Thorsten (Ger)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Thorsten dataset (Ger). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
text = "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht."
input_ids = processor.text_to_sequence(text)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
```
#### Referencing Tacotron 2
```
@article{DBLP:journals/corr/abs-1712-05884,
author = {Jonathan Shen and
Ruoming Pang and
Ron J. Weiss and
Mike Schuster and
Navdeep Jaitly and
Zongheng Yang and
Zhifeng Chen and
Yu Zhang and
Yuxuan Wang and
R. J. Skerry{-}Ryan and
Rif A. Saurous and
Yannis Agiomyrgiannakis and
Yonghui Wu},
title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions},
journal = {CoRR},
volume = {abs/1712.05884},
year = {2017},
url = {http://arxiv.org/abs/1712.05884},
archivePrefix = {arXiv},
eprint = {1712.05884},
timestamp = {Thu, 28 Nov 2019 08:59:52 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` |
teshnizi/bert-lossy | 2021-02-10T15:46:36.000Z | [] | [
".gitattributes",
"README.md"
] | teshnizi | 0 | hello
hello
|
||
textattack/albert-base-v2-CoLA | 2020-07-06T16:28:50.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 179 | transformers | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8245445829338447, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-MRPC | 2020-07-06T16:29:43.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 1,520 | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8970588235294118, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-QQP | 2020-07-06T16:30:55.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 13 | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9073707642839476, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-RTE | 2020-07-06T16:31:05.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 26 | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.776173285198556, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-SST-2 | 2020-07-06T16:32:15.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 118 | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 64.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9254587155963303, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-STS-B | 2020-07-06T16:32:24.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | [
".gitattributes",
"README.md",
"config.json",
"log.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"train_args.json"
] | textattack | 86 | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a regression task, the model was trained with a mean squared error loss function.
The best score the model achieved on this task was 0.9064220351504577, as measured by the
eval set pearson correlation, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
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