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int64 0
11.7k
| library_name
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tftransformers/gpt2
|
tftransformers
| 2021-10-24T08:41:46Z | 1 | 0 |
transformers
|
[
"transformers",
"exbert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
from tf_transformers.models import GPT2Model
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained("gpt2")
text = "Replace me by any text you'd like."
inputs_tf = {}
inputs = tokenizer(text, return_tensors='tf')
inputs_tf["input_ids"] = inputs["input_ids"]
outputs_tf = model(inputs_tf)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
tftransformers/albert-xxlarge-v1
|
tftransformers
| 2021-10-24T08:38:28Z | 1 | 0 |
transformers
|
[
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
ALBERT is a transformers model pretrained on a large corpus of English 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.
- Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
This way, the model learns an inner representation of the English 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 ALBERT model as inputs.
ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks.
This model has the following configuration:
- 12 repeating layers
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 11M parameters
## 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 downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as 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:
In tf_transformers
```python
from tf_transformers.models import AlbertModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v1')
model = AlbertModel.from_pretrained("albert-xxlarge-v1")
text = "Replace me by any text you'd like."
inputs_tf = {}
inputs = tokenizer(text, return_tensors='tf')
inputs_tf["input_ids"] = inputs["input_ids"]
inputs_tf["input_type_ids"] = inputs["token_type_ids"]
inputs_tf["input_mask"] = inputs["attention_mask"]
outputs_tf = model(inputs_tf)
```
## Training data
The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
### Training
The ALBERT procedure follows the BERT setup.
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.
## Evaluation results
When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
| | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE |
|----------------|----------|----------|----------|----------|----------|----------|
|V2 |
|ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 |
|ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 |
|ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 |
|ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 |
|V1 |
|ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 |
|ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 |
|ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 |
|ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1909-11942,
author = {Zhenzhong Lan and
Mingda Chen and
Sebastian Goodman and
Kevin Gimpel and
Piyush Sharma and
Radu Soricut},
title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language
Representations},
journal = {CoRR},
volume = {abs/1909.11942},
year = {2019},
url = {http://arxiv.org/abs/1909.11942},
archivePrefix = {arXiv},
eprint = {1909.11942},
timestamp = {Fri, 27 Sep 2019 13:04:21 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
tftransformers/albert-xlarge-v1
|
tftransformers
| 2021-10-24T08:37:26Z | 3 | 0 |
transformers
|
[
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
ALBERT is a transformers model pretrained on a large corpus of English 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.
- Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
This way, the model learns an inner representation of the English 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 ALBERT model as inputs.
ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks.
This model has the following configuration:
- 12 repeating layers
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 11M parameters
## 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 downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as 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:
In tf_transformers
```python
from tf_transformers.models import AlbertModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-xlarge-v1')
model = AlbertModel.from_pretrained("albert-xlarge-v1")
text = "Replace me by any text you'd like."
inputs_tf = {}
inputs = tokenizer(text, return_tensors='tf')
inputs_tf["input_ids"] = inputs["input_ids"]
inputs_tf["input_type_ids"] = inputs["token_type_ids"]
inputs_tf["input_mask"] = inputs["attention_mask"]
outputs_tf = model(inputs_tf)
```
## Training data
The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
### Training
The ALBERT procedure follows the BERT setup.
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.
## Evaluation results
When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
| | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE |
|----------------|----------|----------|----------|----------|----------|----------|
|V2 |
|ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 |
|ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 |
|ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 |
|ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 |
|V1 |
|ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 |
|ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 |
|ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 |
|ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1909-11942,
author = {Zhenzhong Lan and
Mingda Chen and
Sebastian Goodman and
Kevin Gimpel and
Piyush Sharma and
Radu Soricut},
title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language
Representations},
journal = {CoRR},
volume = {abs/1909.11942},
year = {2019},
url = {http://arxiv.org/abs/1909.11942},
archivePrefix = {arXiv},
eprint = {1909.11942},
timestamp = {Fri, 27 Sep 2019 13:04:21 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
tftransformers/albert-base-v2
|
tftransformers
| 2021-10-24T08:36:40Z | 3 | 0 |
transformers
|
[
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
ALBERT is a transformers model pretrained on a large corpus of English 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.
- Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
This way, the model learns an inner representation of the English 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 ALBERT model as inputs.
ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks.
This model has the following configuration:
- 12 repeating layers
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 11M parameters
## 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 downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as 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:
In tf_transformers
```python
from tf_transformers.models import AlbertModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
model = AlbertModel.from_pretrained("albert-base-v2")
text = "Replace me by any text you'd like."
inputs_tf = {}
inputs = tokenizer(text, return_tensors='tf')
inputs_tf["input_ids"] = inputs["input_ids"]
inputs_tf["input_type_ids"] = inputs["token_type_ids"]
inputs_tf["input_mask"] = inputs["attention_mask"]
outputs_tf = model(inputs_tf)
```
## Training data
The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
### Training
The ALBERT procedure follows the BERT setup.
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.
## Evaluation results
When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
| | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE |
|----------------|----------|----------|----------|----------|----------|----------|
|V2 |
|ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 |
|ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 |
|ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 |
|ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 |
|V1 |
|ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 |
|ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 |
|ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 |
|ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1909-11942,
author = {Zhenzhong Lan and
Mingda Chen and
Sebastian Goodman and
Kevin Gimpel and
Piyush Sharma and
Radu Soricut},
title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language
Representations},
journal = {CoRR},
volume = {abs/1909.11942},
year = {2019},
url = {http://arxiv.org/abs/1909.11942},
archivePrefix = {arXiv},
eprint = {1909.11942},
timestamp = {Fri, 27 Sep 2019 13:04:21 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
tftransformers/bart-large
|
tftransformers
| 2021-10-24T08:24:25Z | 2 | 0 |
transformers
|
[
"transformers",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: en
---
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in tf_transformers:
```python
from tf_transformers.models import BartModel
from transformers import BartTokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartModel.from_pretrained('facebook/bart-large')
inputs_tf = {}
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
inputs_tf["encoder_input_ids"] = inputs["input_ids"]
inputs_tf["encoder_input_mask"] = inputs["attention_mask"]
inputs_tf["decoder_input_ids"] = decoder_input_ids
outputs_tf = model(inputs_tf)
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
tftransformers/mt5-small
|
tftransformers
| 2021-10-24T08:18:10Z | 4 | 0 |
transformers
|
[
"transformers",
"multilingual",
"dataset:mc4",
"arxiv:2010.11934",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: multilingual
datasets:
- mc4
license: apache-2.0
---
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
**Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5)
Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel*
## Abstract
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.
## Usage
```
from tf_transformers.models import MT5Model
# Any MT5 model (mt5-small, mt5-base etc)
model_name = 'mt5-small'
model = MT5Model.from_pretrained(model_name)
```
|
tftransformers/t5-base
|
tftransformers
| 2021-10-24T08:16:17Z | 3 | 0 |
transformers
|
[
"transformers",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpusâ€Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Usage
```
from tf_transformers.models import T5Model
# Any T5 model (t5-small, t5-base, t5-large etc)
model_name = 't5-small'
model = T5Model.from_pretrained(model_name)
```
|
tftransformers/t5-large
|
tftransformers
| 2021-10-24T08:15:07Z | 2 | 0 |
transformers
|
[
"transformers",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpusâ€, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Usage
```
from tf_transformers.models import T5Model
# Any T5 model (t5-small, t5-base, t5-large etc)
model_name = 't5-small'
model = T5Model.from_pretrained(model_name)
```
|
mathew/layoutlmv2-finetuned-funsd-1024
|
mathew
| 2021-10-24T06:13:48Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-funsd-1024
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-finetuned-funsd-1024
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3
|
aditeyabaral/sentencetransformer-xlm-roberta-base
|
aditeyabaral
| 2021-10-24T04:56:00Z | 49 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-xlm-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-xlm-roberta-base')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-xlm-roberta-base')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-xlm-roberta-base')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-xlm-roberta-base)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9234 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
huggingartists/sqwore
|
huggingartists
| 2021-10-24T04:23:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/sqwore",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/sqwore
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3557a234d4c5912569afbea078a23eff.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sqwore</div>
<a href="https://genius.com/artists/sqwore">
<div style="text-align: center; font-size: 14px;">@sqwore</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Sqwore.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/sqwore).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sqwore")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3gzd5crq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Sqwore's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/sqwore')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/sqwore")
model = AutoModelWithLMHead.from_pretrained("huggingartists/sqwore")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingtweets/praisegodbarbon
|
huggingtweets
| 2021-10-24T03:47:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/praisegodbarbon/1635047234116/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1381764452098437120/74IgKP07_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Boston Psychology PhD</div>
<div style="text-align: center; font-size: 14px;">@praisegodbarbon</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Boston Psychology PhD.
| Data | Boston Psychology PhD |
| --- | --- |
| Tweets downloaded | 3212 |
| Retweets | 810 |
| Short tweets | 265 |
| Tweets kept | 2137 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/h4r5tyq8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @praisegodbarbon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o2225sd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o2225sd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/praisegodbarbon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan
|
espnet
| 2021-10-23T20:55:12Z | 17 | 16 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/ljspeech_joint_finetune_conformer_fastspeech2_hifigan`
♻️ Imported from https://zenodo.org/record/5498896/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_ljspeech_joint_train_conformer_fastspeech2_hifigan
|
espnet
| 2021-10-23T20:54:48Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/ljspeech_joint_train_conformer_fastspeech2_hifigan`
♻️ Imported from https://zenodo.org/record/5498487/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_-truncated-737899
|
espnet
| 2021-10-23T20:54:27Z | 2 | 1 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5498896/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_libritts_xvector_vits
|
espnet
| 2021-10-23T20:52:03Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:libritts",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/libritts_xvector_vits`
♻️ Imported from https://zenodo.org/record/5521416/
This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_full_band_vits_prosody
|
espnet
| 2021-10-23T20:47:17Z | 11 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_full_band_vits_prosody`
♻️ Imported from https://zenodo.org/record/5521340/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave
|
espnet
| 2021-10-23T20:44:44Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5521354/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_vctk_full_band_multi_spk_vits
|
espnet
| 2021-10-23T20:44:14Z | 0 | 1 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_full_band_multi_spk_vits`
♻️ Imported from https://zenodo.org/record/5521431/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_vctk_multi_spk_vits
|
espnet
| 2021-10-23T20:42:58Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_multi_spk_vits`
♻️ Imported from https://zenodo.org/record/5500759/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
|
espnet
| 2021-10-23T20:32:45Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5500759/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_transformer_prosody
|
espnet
| 2021-10-23T20:32:15Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody`
♻️ Imported from https://zenodo.org/record/5499066/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-f43d8f
|
espnet
| 2021-10-23T20:31:48Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`
♻️ Imported from https://zenodo.org/record/5499066/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_tacotron2_prosody
|
espnet
| 2021-10-23T20:31:24Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody`
♻️ Imported from https://zenodo.org/record/5499050/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_transformer_prosody
|
espnet
| 2021-10-23T20:30:42Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_transformer_prosody`
♻️ Imported from https://zenodo.org/record/5499040/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave
|
espnet
| 2021-10-23T20:30:29Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`
♻️ Imported from https://zenodo.org/record/5499040/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
|
espnet
| 2021-10-23T20:27:27Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5443814/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jvs_jvs010_vits_accent_with_pause
|
espnet
| 2021-10-23T20:26:30Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_jvs010_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5432566/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-d57a28
|
espnet
| 2021-10-23T20:25:39Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest`
♻️ Imported from https://zenodo.org/record/5432566/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_vits_accent_with_pause
|
espnet
| 2021-10-23T20:23:56Z | 0 | 3 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5414980/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_a-truncated-d7d5d0
|
espnet
| 2021-10-23T20:23:41Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5431984/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
huggingtweets/islamocommunism
|
huggingtweets
| 2021-10-23T18:38:04Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/islamocommunism/1635014280450/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1448436144388009985/zWh5cSQ3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">نورهان</div>
<div style="text-align: center; font-size: 14px;">@islamocommunism</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from نورهان.
| Data | نورهان |
| --- | --- |
| Tweets downloaded | 3196 |
| Retweets | 1205 |
| Short tweets | 227 |
| Tweets kept | 1764 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2l8ikj22/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamocommunism's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kngkxcq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kngkxcq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamocommunism')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
tiennvcs/bert-large-uncased-finetuned-docvqa
|
tiennvcs
| 2021-10-23T17:43:43Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-finetuned-docvqa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-finetuned-docvqa
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6367
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.5228 | 0.05 | 1000 | 2.6645 |
| 2.4909 | 0.1 | 2000 | 2.8985 |
| 2.1679 | 0.16 | 3000 | 2.3551 |
| 1.9451 | 0.21 | 4000 | 2.2226 |
| 1.6814 | 0.26 | 5000 | 2.1590 |
| 1.8868 | 0.31 | 6000 | 2.6197 |
| 1.6618 | 0.36 | 7000 | 2.3632 |
| 1.8313 | 0.41 | 8000 | 2.4519 |
| 1.7017 | 0.47 | 9000 | 2.2682 |
| 1.8169 | 0.52 | 10000 | 2.4486 |
| 1.7074 | 0.57 | 11000 | 2.3862 |
| 1.7674 | 0.62 | 12000 | 2.1801 |
| 1.8134 | 0.67 | 13000 | 2.3032 |
| 1.8334 | 0.73 | 14000 | 2.4205 |
| 1.6819 | 0.78 | 15000 | 2.2398 |
| 1.5846 | 0.83 | 16000 | 2.3834 |
| 1.6758 | 0.88 | 17000 | 1.9683 |
| 1.6303 | 0.93 | 18000 | 2.3297 |
| 1.5652 | 0.98 | 19000 | 2.0581 |
| 1.3045 | 1.04 | 20000 | 2.4950 |
| 1.2393 | 1.09 | 21000 | 2.6622 |
| 1.1526 | 1.14 | 22000 | 2.3749 |
| 1.2631 | 1.19 | 23000 | 2.3915 |
| 1.1846 | 1.24 | 24000 | 2.2592 |
| 1.2731 | 1.3 | 25000 | 2.4239 |
| 1.3057 | 1.35 | 26000 | 2.2920 |
| 1.134 | 1.4 | 27000 | 2.3107 |
| 1.2017 | 1.45 | 28000 | 2.4271 |
| 1.2202 | 1.5 | 29000 | 2.1814 |
| 1.2179 | 1.56 | 30000 | 2.3365 |
| 1.2359 | 1.61 | 31000 | 2.1256 |
| 1.1964 | 1.66 | 32000 | 2.1720 |
| 1.269 | 1.71 | 33000 | 2.4363 |
| 1.1812 | 1.76 | 34000 | 2.2372 |
| 1.2187 | 1.81 | 35000 | 2.2318 |
| 1.1805 | 1.87 | 36000 | 2.3693 |
| 1.1458 | 1.92 | 37000 | 2.5128 |
| 1.1958 | 1.97 | 38000 | 2.1311 |
| 0.8924 | 2.02 | 39000 | 2.4635 |
| 0.869 | 2.07 | 40000 | 2.8231 |
| 0.8333 | 2.13 | 41000 | 2.6762 |
| 0.9194 | 2.18 | 42000 | 2.4588 |
| 0.8089 | 2.23 | 43000 | 2.6443 |
| 0.8612 | 2.28 | 44000 | 2.4300 |
| 0.7981 | 2.33 | 45000 | 2.7418 |
| 0.9765 | 2.38 | 46000 | 2.6543 |
| 0.8646 | 2.44 | 47000 | 2.5990 |
| 1.0316 | 2.49 | 48000 | 2.4625 |
| 0.9862 | 2.54 | 49000 | 2.4691 |
| 1.027 | 2.59 | 50000 | 2.4156 |
| 0.9412 | 2.64 | 51000 | 2.4204 |
| 0.9353 | 2.7 | 52000 | 2.4933 |
| 0.9509 | 2.75 | 53000 | 2.4708 |
| 0.9351 | 2.8 | 54000 | 2.5351 |
| 0.9968 | 2.85 | 55000 | 2.2506 |
| 1.025 | 2.9 | 56000 | 2.6317 |
| 1.627 | 2.95 | 57000 | 2.7843 |
| 0.9294 | 3.01 | 58000 | 2.9396 |
| 0.6043 | 3.06 | 59000 | 3.1560 |
| 0.7903 | 3.11 | 60000 | 2.8330 |
| 0.7373 | 3.16 | 61000 | 2.9422 |
| 0.6499 | 3.21 | 62000 | 3.0948 |
| 0.6411 | 3.27 | 63000 | 2.7900 |
| 0.625 | 3.32 | 64000 | 2.5268 |
| 0.6264 | 3.37 | 65000 | 2.8701 |
| 0.6143 | 3.42 | 66000 | 3.2544 |
| 0.6286 | 3.47 | 67000 | 2.6208 |
| 0.739 | 3.53 | 68000 | 2.8107 |
| 0.5981 | 3.58 | 69000 | 2.8073 |
| 0.6502 | 3.63 | 70000 | 2.6293 |
| 0.6548 | 3.68 | 71000 | 2.9501 |
| 0.7243 | 3.73 | 72000 | 2.7917 |
| 0.598 | 3.78 | 73000 | 2.9341 |
| 0.6159 | 3.84 | 74000 | 2.7629 |
| 0.5905 | 3.89 | 75000 | 2.6441 |
| 0.6393 | 3.94 | 76000 | 2.6660 |
| 0.677 | 3.99 | 77000 | 2.7616 |
| 0.3281 | 4.04 | 78000 | 3.6873 |
| 0.4524 | 4.1 | 79000 | 3.3441 |
| 0.3994 | 4.15 | 80000 | 3.3129 |
| 0.4686 | 4.2 | 81000 | 3.1813 |
| 0.5293 | 4.25 | 82000 | 2.9088 |
| 0.3961 | 4.3 | 83000 | 3.0765 |
| 0.4406 | 4.35 | 84000 | 3.1254 |
| 0.401 | 4.41 | 85000 | 3.2415 |
| 0.4594 | 4.46 | 86000 | 3.0691 |
| 0.4523 | 4.51 | 87000 | 3.0493 |
| 0.4719 | 4.56 | 88000 | 3.1352 |
| 0.4895 | 4.61 | 89000 | 2.8991 |
| 0.423 | 4.67 | 90000 | 3.1738 |
| 0.3984 | 4.72 | 91000 | 3.1862 |
| 0.4206 | 4.77 | 92000 | 3.1213 |
| 0.4587 | 4.82 | 93000 | 3.0030 |
| 0.381 | 4.87 | 94000 | 3.3218 |
| 0.4138 | 4.92 | 95000 | 3.1529 |
| 0.4003 | 4.98 | 96000 | 3.1375 |
| 0.2098 | 5.03 | 97000 | 3.7443 |
| 0.2334 | 5.08 | 98000 | 3.7359 |
| 0.2534 | 5.13 | 99000 | 3.7814 |
| 0.3067 | 5.18 | 100000 | 3.7128 |
| 0.2363 | 5.24 | 101000 | 3.6091 |
| 0.2652 | 5.29 | 102000 | 3.4015 |
| 0.3311 | 5.34 | 103000 | 3.4793 |
| 0.2344 | 5.39 | 104000 | 3.6792 |
| 0.2741 | 5.44 | 105000 | 3.5385 |
| 0.2896 | 5.5 | 106000 | 3.8118 |
| 0.2071 | 5.55 | 107000 | 3.8690 |
| 0.3023 | 5.6 | 108000 | 3.7087 |
| 0.3299 | 5.65 | 109000 | 3.4925 |
| 0.1943 | 5.7 | 110000 | 3.6739 |
| 0.2488 | 5.75 | 111000 | 3.7614 |
| 0.3138 | 5.81 | 112000 | 3.5156 |
| 0.2555 | 5.86 | 113000 | 3.6056 |
| 0.2918 | 5.91 | 114000 | 3.6533 |
| 0.2751 | 5.96 | 115000 | 3.6367 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.8.0+cu101
- Datasets 1.11.0
- Tokenizers 0.10.3
|
2umm3r/distilbert-base-uncased-finetuned-cola
|
2umm3r
| 2021-10-23T11:46:51Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5155709926752544
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7816
- Matthews Correlation: 0.5156
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5291 | 1.0 | 535 | 0.5027 | 0.4092 |
| 0.3492 | 2.0 | 1070 | 0.5136 | 0.4939 |
| 0.2416 | 3.0 | 1605 | 0.6390 | 0.5056 |
| 0.1794 | 4.0 | 2140 | 0.7816 | 0.5156 |
| 0.1302 | 5.0 | 2675 | 0.8836 | 0.5156 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
nepalprabin/xlm-roberta-base-finetuned-marc-en
|
nepalprabin
| 2021-10-23T09:53:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0442
- Mae: 0.5385
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0371 | 1.0 | 1105 | 1.0522 | 0.5256 |
| 0.8925 | 2.0 | 2210 | 1.0442 | 0.5385 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
stamas01/vgg19_skin_auto_encoder
|
stamas01
| 2021-10-23T06:04:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
A simple Auto Encoder made up of VGG19 trained to reconstruct skin lesion images.
|
jx88/xlm-roberta-base-finetuned-marc-en-j-run
|
jx88
| 2021-10-23T03:13:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-j-run
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en-j-run
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9189
- Mae: 0.4634
## Model description
Trained following the MLT Tokyo Transformers workshop run by huggingface.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.2327 | 1.0 | 235 | 1.0526 | 0.6341 |
| 0.9943 | 2.0 | 470 | 0.9189 | 0.4634 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
tiennvcs/bert-base-uncased-finetuned-infovqa
|
tiennvcs
| 2021-10-23T00:21:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-infovqa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8276
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2765 | 0.23 | 1000 | 3.0678 |
| 2.9987 | 0.46 | 2000 | 2.9525 |
| 2.826 | 0.69 | 3000 | 2.7870 |
| 2.7084 | 0.93 | 4000 | 2.7051 |
| 2.1286 | 1.16 | 5000 | 2.9286 |
| 2.0009 | 1.39 | 6000 | 3.1037 |
| 2.0323 | 1.62 | 7000 | 2.8567 |
| 1.9905 | 1.85 | 8000 | 2.8276 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.8.0+cu101
- Datasets 1.11.0
- Tokenizers 0.10.3
|
espnet/sujay_catslu_map
|
espnet
| 2021-10-22T21:01:58Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:catslu",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- catslu
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/sujay_catslu_map`
This model was trained by Sujay S Kumar using catslu recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout e31965d55993766461f0964216a0bb9aea3cfb7a
pip install -e .
cd egs2/catslu/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/sujay_catslu_map
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Oct 3 12:53:16 EDT 2021`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a3`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c`
- Commit date: `Wed Sep 22 10:02:03 2021 -0400`
## asr_train_asr_smaller_aishell_xlsr_raw_zh_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|1577|11441|46.1|30.1|23.7|2.5|56.4|81.3|
|inference_asr_model_valid.acc.ave_5best/valid|921|6438|49.4|29.2|21.4|2.7|53.4|79.2|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|1577|45924|74.4|13.0|12.5|3.2|28.8|81.3|
|inference_asr_model_valid.acc.ave_5best/valid|921|26110|77.0|11.9|11.1|2.7|25.7|79.2|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr_smaller_aishell_xlsr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp_train_asr_smaller_aishell_xlsr/asr_train_asr_smaller_aishell_xlsr_raw_zh_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/speech_shape
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/text_shape.word
valid_shape_file:
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/speech_shape
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 2500
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- request_剩余距离_none
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- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wav2vec2_xlsr
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 15
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 4
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: 0.10.3a3
distributed: false
```
</details>
## LM config
<details><summary>expand</summary>
```
NONE
```
</details>
|
patrickvonplaten/sat-base
|
patrickvonplaten
| 2021-10-22T17:51:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: sat-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sat-base
This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7014
- Wer: 0.5374
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9958 | 0.69 | 100 | 6.7171 | 1.0 |
| 3.0453 | 1.38 | 200 | 3.0374 | 1.0 |
| 2.9989 | 2.07 | 300 | 2.9807 | 1.0 |
| 2.969 | 2.76 | 400 | 2.9579 | 1.0 |
| 2.903 | 3.45 | 500 | 2.9072 | 1.0 |
| 2.8565 | 4.14 | 600 | 2.8804 | 1.0 |
| 2.8195 | 4.83 | 700 | 2.7916 | 1.0 |
| 2.3134 | 5.52 | 800 | 2.1456 | 1.0004 |
| 1.5475 | 6.21 | 900 | 1.4663 | 0.9549 |
| 1.1295 | 6.9 | 1000 | 1.1140 | 0.7227 |
| 1.0181 | 7.59 | 1100 | 0.9258 | 0.6497 |
| 1.0252 | 8.28 | 1200 | 0.8430 | 0.6255 |
| 0.835 | 8.97 | 1300 | 0.8063 | 0.6032 |
| 0.662 | 9.66 | 1400 | 0.7595 | 0.5931 |
| 0.5558 | 10.34 | 1500 | 0.7322 | 0.5819 |
| 0.7596 | 11.03 | 1600 | 0.7120 | 0.5708 |
| 0.6169 | 11.72 | 1700 | 0.7073 | 0.5606 |
| 0.4565 | 12.41 | 1800 | 0.7124 | 0.5586 |
| 0.4554 | 13.1 | 1900 | 0.6880 | 0.5501 |
| 0.6216 | 13.79 | 2000 | 0.6783 | 0.5494 |
| 0.5393 | 14.48 | 2100 | 0.7067 | 0.5499 |
| 0.4095 | 15.17 | 2200 | 0.7014 | 0.5438 |
| 0.3551 | 15.86 | 2300 | 0.7000 | 0.5426 |
| 0.5112 | 16.55 | 2400 | 0.6866 | 0.5426 |
| 0.5139 | 17.24 | 2500 | 0.7134 | 0.5446 |
| 0.3638 | 17.93 | 2600 | 0.7130 | 0.5434 |
| 0.3327 | 18.62 | 2700 | 0.6980 | 0.5377 |
| 0.4385 | 19.31 | 2800 | 0.7017 | 0.5390 |
| 0.4986 | 20.0 | 2900 | 0.7014 | 0.5374 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-random
|
patrickvonplaten
| 2021-10-22T17:20:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-random
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-random
This model is a fine-tuned version of [patrickvonplaten/wav2vec2-base-random](https://huggingface.co/patrickvonplaten/wav2vec2-base-random) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1593
- Wer: 0.8364
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9043 | 0.69 | 100 | 2.9683 | 1.0 |
| 2.8537 | 1.38 | 200 | 2.9281 | 0.9997 |
| 2.7803 | 2.07 | 300 | 2.7330 | 0.9999 |
| 2.6806 | 2.76 | 400 | 2.5792 | 1.0 |
| 2.4136 | 3.45 | 500 | 2.4327 | 0.9948 |
| 2.1682 | 4.14 | 600 | 2.3508 | 0.9877 |
| 2.2577 | 4.83 | 700 | 2.2176 | 0.9773 |
| 2.355 | 5.52 | 800 | 2.1753 | 0.9542 |
| 1.8588 | 6.21 | 900 | 2.0650 | 0.8851 |
| 1.6831 | 6.9 | 1000 | 2.0109 | 0.8618 |
| 1.888 | 7.59 | 1100 | 1.9660 | 0.8418 |
| 2.0066 | 8.28 | 1200 | 1.9847 | 0.8531 |
| 1.7044 | 8.97 | 1300 | 1.9760 | 0.8527 |
| 1.3168 | 9.66 | 1400 | 2.0708 | 0.8327 |
| 1.2143 | 10.34 | 1500 | 2.0601 | 0.8419 |
| 1.6189 | 11.03 | 1600 | 2.0960 | 0.8299 |
| 1.13 | 11.72 | 1700 | 2.2540 | 0.8408 |
| 0.8001 | 12.41 | 1800 | 2.4260 | 0.8306 |
| 0.7769 | 13.1 | 1900 | 2.4182 | 0.8445 |
| 1.2165 | 13.79 | 2000 | 2.3666 | 0.8284 |
| 0.8026 | 14.48 | 2100 | 2.7118 | 0.8662 |
| 0.5148 | 15.17 | 2200 | 2.7957 | 0.8526 |
| 0.4921 | 15.86 | 2300 | 2.8244 | 0.8346 |
| 0.7629 | 16.55 | 2400 | 2.8944 | 0.8370 |
| 0.5762 | 17.24 | 2500 | 3.0335 | 0.8367 |
| 0.4076 | 17.93 | 2600 | 3.0776 | 0.8358 |
| 0.3395 | 18.62 | 2700 | 3.1572 | 0.8261 |
| 0.4862 | 19.31 | 2800 | 3.1319 | 0.8414 |
| 0.5061 | 20.0 | 2900 | 3.1593 | 0.8364 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
tiennvcs/bert-base-uncased-finetuned-docvqa
|
tiennvcs
| 2021-10-22T15:49:05Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-docvqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-docvqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9146
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2151 | 0.1 | 1000 | 2.6299 |
| 1.8885 | 0.21 | 2000 | 2.2217 |
| 1.7353 | 0.31 | 3000 | 2.1675 |
| 1.6188 | 0.41 | 4000 | 2.2436 |
| 1.5802 | 0.52 | 5000 | 2.0539 |
| 1.4875 | 0.62 | 6000 | 2.0551 |
| 1.4675 | 0.73 | 7000 | 1.9368 |
| 1.3485 | 0.83 | 8000 | 1.9456 |
| 1.3273 | 0.93 | 9000 | 1.9281 |
| 1.1048 | 1.04 | 10000 | 1.9333 |
| 0.9529 | 1.14 | 11000 | 2.2019 |
| 0.9418 | 1.24 | 12000 | 2.0381 |
| 0.9209 | 1.35 | 13000 | 1.8753 |
| 0.8788 | 1.45 | 14000 | 1.9964 |
| 0.8729 | 1.56 | 15000 | 1.9690 |
| 0.8671 | 1.66 | 16000 | 1.8513 |
| 0.8379 | 1.76 | 17000 | 1.9627 |
| 0.8722 | 1.87 | 18000 | 1.8988 |
| 0.7842 | 1.97 | 19000 | 1.9146 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
muhtasham/autonlp-Doctor_DE-24595547
|
muhtasham
| 2021-10-22T14:04:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"autonlp",
"de",
"dataset:muhtasham/autonlp-data-Doctor_DE",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 396.5529429198159
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595547
- CO2 Emissions (in grams): 396.5529429198159
## Validation Metrics
- Loss: 1.9565489292144775
- MSE: 1.9565489292144775
- MAE: 0.9890901446342468
- R2: -7.68965036332947e-05
- RMSE: 1.3987668752670288
- Explained Variance: 0.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595547
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595547", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595547", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
yokonav/xlm-roberta-base-finetuned-marc-en
|
yokonav
| 2021-10-22T13:36:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9177
- Mae: 0.4756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.136 | 1.0 | 235 | 0.9515 | 0.4756 |
| 0.9724 | 2.0 | 470 | 0.9177 | 0.4756 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3
|
muhtasham/autonlp-Doctor_DE-24595546
|
muhtasham
| 2021-10-22T12:23:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"de",
"dataset:muhtasham/autonlp-data-Doctor_DE",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 210.5957437893554
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Loss: 0.3092539310455322
- MSE: 0.30925390124320984
- MAE: 0.25015318393707275
- R2: 0.841926941198094
- RMSE: 0.5561060309410095
- Explained Variance: 0.8427215218544006
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595546
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595546", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595546", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
meghana/hitalm-xlmroberta-finetuned
|
meghana
| 2021-10-22T11:51:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: hitalm-xlmroberta-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hitalm-xlmroberta-finetuned
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 48 | 5.4501 |
| No log | 2.0 | 96 | 5.2843 |
| No log | 3.0 | 144 | 4.7745 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
muhtasham/autonlp-Doctor_DE-24595544
|
muhtasham
| 2021-10-22T10:51:44Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"de",
"dataset:muhtasham/autonlp-data-Doctor_DE",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 92.87363201770962
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595544
- CO2 Emissions (in grams): 92.87363201770962
## Validation Metrics
- Loss: 0.3001164197921753
- MSE: 0.3001164197921753
- MAE: 0.24272102117538452
- R2: 0.8465975006681247
- RMSE: 0.5478288531303406
- Explained Variance: 0.8468209505081177
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595544
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595544", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595544", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
model-attribution-challenge/german-gpt2
|
model-attribution-challenge
| 2021-10-22T08:58:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-09T20:17:28Z |
---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases for this model 😉
**Note**: The model was initially released under an anonymous alias (`anonymous-german-nlp/german-gpt2`) so we now "de-anonymize" it.
More details about GPT-2 can be found in the great [Hugging Face](https://huggingface.co/transformers/model_doc/gpt2.html) documentation.
# Changelog
16.08.2021: Public release of re-trained version of our German GPT-2 model with better results.
15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0).
# Training corpora
We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in [this repository](https://github.com/dbmdz/berts).
Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome [Tokenizers](https://github.com/huggingface/tokenizers) library.
With the previously mentioned awesome Tokenizers library we created a 50K byte-level BPE vocab based on the training corpora.
After creating the vocab, we could train the GPT-2 for German on a v3-8 TPU over the complete training corpus for 20 epochs. All hyperparameters
can be found in the official JAX/FLAX documentation [here](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/README.md)
from Transformers.
# Using the model
The model itself can be used in this way:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2")
model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2")
```
However, text generation is a bit more interesting, so here's an example that shows how to use the great Transformers *Pipelines* for generating text:
```python
from transformers import pipeline
pipe = pipeline('text-generation', model="dbmdz/german-gpt2",
tokenizer="dbmdz/german-gpt2")
text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"]
print(text)
```
This could output this beautiful text:
```
Der Sinn des Lebens ist es, im Geist zu verweilen, aber nicht in der Welt zu sein, sondern ganz im Geist zu leben.
Die Menschen beginnen, sich nicht nach der Natur und nach der Welt zu richten, sondern nach der Seele,'
```
# License
All models are licensed under [MIT](LICENSE).
# Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
# Contact (Bugs, Feedback, Contribution and more)
For questions about our BERT models just open an issue
[here](https://github.com/stefan-it/german-gpt/issues/new) 🤗
# Acknowledgments
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤️
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗
|
teacookies/autonlp-roberta-base-squad2-24465516
|
teacookies
| 2021-10-22T08:21:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 65.5797497320557
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465516
- CO2 Emissions (in grams): 65.5797497320557
## Validation Metrics
- Loss: 0.6545609831809998
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465516
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465516", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465516", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465524
|
teacookies
| 2021-10-22T08:14:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 58.51753681929935
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465524
- CO2 Emissions (in grams): 58.51753681929935
## Validation Metrics
- Loss: 0.5759999752044678
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465524
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465524", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465524", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465520
|
teacookies
| 2021-10-22T08:13:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 57.56554511511173
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465520
- CO2 Emissions (in grams): 57.56554511511173
## Validation Metrics
- Loss: 0.6455457806587219
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465520
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465520", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465520", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465517
|
teacookies
| 2021-10-22T08:13:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 54.75747617143382
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465517
- CO2 Emissions (in grams): 54.75747617143382
## Validation Metrics
- Loss: 0.6653227806091309
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465517
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465517", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465517", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465519
|
teacookies
| 2021-10-22T08:13:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 58.19097299648645
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465519
- CO2 Emissions (in grams): 58.19097299648645
## Validation Metrics
- Loss: 0.566668689250946
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465519
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465519", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465519", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465523
|
teacookies
| 2021-10-22T08:13:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 56.99866929988893
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465523
- CO2 Emissions (in grams): 56.99866929988893
## Validation Metrics
- Loss: 0.5468788146972656
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465523
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465523", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465523", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465515
|
teacookies
| 2021-10-22T08:11:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 56.45146749922553
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465515
- CO2 Emissions (in grams): 56.45146749922553
## Validation Metrics
- Loss: 0.5932255387306213
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465515
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465515", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465515", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
teacookies/autonlp-roberta-base-squad2-24465518
|
teacookies
| 2021-10-22T08:04:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-roberta-base-squad2
co2_eq_emissions: 45.268576304018616
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465518
- CO2 Emissions (in grams): 45.268576304018616
## Validation Metrics
- Loss: 0.5742421746253967
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465518
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465518", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465518", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
aditeyabaral/sentencetransformer-distilbert-base-cased
|
aditeyabaral
| 2021-10-21T22:30:29Z | 129 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-distilbert-base-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-distilbert-base-cased')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-distilbert-base-cased')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-distilbert-base-cased')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-distilbert-base-cased)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9234 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
pritoms/distilgpt2-finetuned-wikitext2
|
pritoms
| 2021-10-21T21:16:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0540
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 130 | 3.1733 |
| No log | 2.0 | 260 | 3.0756 |
| No log | 3.0 | 390 | 3.0540 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
JonatanGk/roberta-base-bne-finetuned-sqac
|
JonatanGk
| 2021-10-21T21:06:47Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:sqac",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sqac
model-index:
- name: roberta-base-bne-finetuned-sqac
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the sqac dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9924 | 1.0 | 1196 | 0.8670 |
| 0.474 | 2.0 | 2392 | 0.8923 |
| 0.1637 | 3.0 | 3588 | 1.2066 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
lewtun/xlm-roberta-base-finetuned-marc-en-dummy
|
lewtun
| 2021-10-21T20:03:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-dummy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en-dummy
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8931
- Mae: 0.4634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1258 | 1.0 | 235 | 0.9538 | 0.4390 |
| 0.9445 | 2.0 | 470 | 0.8931 | 0.4634 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
lewtun/xlm-roberta-base-finetuned-marc-en
|
lewtun
| 2021-10-21T18:53:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8850
- Mae: 0.4390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1589 | 1.0 | 235 | 0.9769 | 0.5122 |
| 0.974 | 2.0 | 470 | 0.8850 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
abhishek/autonlp-hindi-question-answering-23865268
|
abhishek
| 2021-10-21T13:51:44Z | 14 | 5 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"hi",
"dataset:abhishek/autonlp-data-hindi-question-answering",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- autonlp
- question-answering
language: hi
widget:
- text: "´सतीश धवन अंतरिक्ष केंद्र´ किस राज्य में स्थित है?"
context: "सतीश धवन अंतरिक्ष केंद्र, भारतीय अंतरिक्ष अनुसंधान संगठन (इसरो) का प्रक्षेपण केंद्र है। यह आंध्र प्रदेश के श्रीहरीकोटा में स्थित है, इसे 'श्रीहरीकोटा रेंज' या 'श्रीहरीकोटा लाँचिंग रेंज' के नाम से भी जाना जाता है। 2002 में इसरो के पूर्व प्रबंधक और वैज्ञानिक सतीश धवन के मरणोपरांत उनके सम्मान में इसका नाम बदला गया। प्रक्षेपण यान की असेम्\u200dबली के लिए दूसरा भवन केन्\u200dद्रीय मंत्रिमंडल ने 12 सितम्\u200dबर, 2013 को सतीश धवन अंतरिक्ष केन्\u200dद्र, श्रीहरिकोटा में प्रक्षेपण यान की असेम्\u200dबली के लिए दूसरे भवन के निर्माण की मंजूरी दी। इस पर 363.95 करोड़ रुपये की अनुमानित लागत आएगी, जिसमें सात करोड़ रुपये का खर्च विदेशी मुद्रा में होगा। इस दूसरी बिल्डिंग के उपलब्\u200dध हो जाने से पीएसएलवी और जीएसएलवी की प्रक्षेपण फ्रीक्वेंसी बढ़ेगी। यह जीएसएलवी एमके-III के एकीकरण के लिए वर्तमान व्\u200dहीकल असेम्\u200dबली बिल्डिंग को अतिरिक्\u200dत सुविधा मुहैया करायेगी। तीसरे प्रक्षेपण पैड तथा भविष्\u200dय में सामान्\u200dय यान प्रक्षेपण के लिए भी इससे काफी सुविधा मिलेगी।[1]\nलांच पैड\nउपग्रह प्रक्षेपण यान लॉन्च पैड\nइस लांच पैड से उपग्रह प्रक्षेपण यान और संवर्धित उपग्रह प्रक्षेपण यान को लांच किया गया था। यह वर्तमान प्रक्षेपण स्थल के दक्षिणी सिरे पर स्थित है। इसे सेवामुक्त कर दिया गया है। शुरू में इसे उपग्रह प्रक्षेपण यान लांच करने के लिए बनाया गया था। लेकिन बाद में इसे संवर्धित उपग्रह प्रक्षेपण यान प्रक्षेपण परिसर के रूप में इस्तेमाल किया गया था।\nप्रथम लांच पैड\nद्वितीय लॉन्च पैड\nतृतीय लांच पैड\nसन्दर्भ श्रेणी:भारतीय अंतरिक्ष अनुसंधान संगठन\nश्रेणी:भारत के रॉकेट प्रक्षेपण स्थल"
datasets:
- abhishek/autonlp-data-hindi-question-answering
co2_eq_emissions: 39.76330395590446
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-hindi-question-answering-23865268
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
tiennvcs/distilbert-base-uncased-finetuned-infovqa
|
tiennvcs
| 2021-10-21T11:37:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-infovqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.02 | 100 | 4.7706 |
| No log | 0.05 | 200 | 4.4399 |
| No log | 0.07 | 300 | 3.8175 |
| No log | 0.09 | 400 | 3.8306 |
| 3.3071 | 0.12 | 500 | 3.6480 |
| 3.3071 | 0.14 | 600 | 3.6451 |
| 3.3071 | 0.16 | 700 | 3.4974 |
| 3.3071 | 0.19 | 800 | 3.4686 |
| 3.3071 | 0.21 | 900 | 3.4703 |
| 3.5336 | 0.23 | 1000 | 3.3165 |
| 3.5336 | 0.25 | 1100 | 3.3634 |
| 3.5336 | 0.28 | 1200 | 3.3466 |
| 3.5336 | 0.3 | 1300 | 3.3411 |
| 3.5336 | 0.32 | 1400 | 3.2456 |
| 3.3593 | 0.35 | 1500 | 3.3257 |
| 3.3593 | 0.37 | 1600 | 3.2941 |
| 3.3593 | 0.39 | 1700 | 3.2581 |
| 3.3593 | 0.42 | 1800 | 3.1680 |
| 3.3593 | 0.44 | 1900 | 3.2077 |
| 3.2436 | 0.46 | 2000 | 3.2422 |
| 3.2436 | 0.49 | 2100 | 3.2529 |
| 3.2436 | 0.51 | 2200 | 3.2681 |
| 3.2436 | 0.53 | 2300 | 3.1055 |
| 3.2436 | 0.56 | 2400 | 3.0174 |
| 3.093 | 0.58 | 2500 | 3.0608 |
| 3.093 | 0.6 | 2600 | 3.0200 |
| 3.093 | 0.63 | 2700 | 2.9884 |
| 3.093 | 0.65 | 2800 | 3.0041 |
| 3.093 | 0.67 | 2900 | 2.9700 |
| 3.0087 | 0.69 | 3000 | 3.0993 |
| 3.0087 | 0.72 | 3100 | 3.0499 |
| 3.0087 | 0.74 | 3200 | 2.9317 |
| 3.0087 | 0.76 | 3300 | 3.0817 |
| 3.0087 | 0.79 | 3400 | 3.0035 |
| 2.9694 | 0.81 | 3500 | 3.0850 |
| 2.9694 | 0.83 | 3600 | 2.9948 |
| 2.9694 | 0.86 | 3700 | 2.9874 |
| 2.9694 | 0.88 | 3800 | 2.9202 |
| 2.9694 | 0.9 | 3900 | 2.9322 |
| 2.8277 | 0.93 | 4000 | 2.9195 |
| 2.8277 | 0.95 | 4100 | 2.8638 |
| 2.8277 | 0.97 | 4200 | 2.8809 |
| 2.8277 | 1.0 | 4300 | 2.8872 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anton-l/wav2vec2-base-finetuned-ks
|
anton-l
| 2021-10-21T11:04:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7908 | 1.0 | 399 | 0.6776 | 0.9009 |
| 0.3202 | 2.0 | 798 | 0.2061 | 0.9763 |
| 0.221 | 3.0 | 1197 | 0.1257 | 0.9785 |
| 0.1773 | 4.0 | 1596 | 0.0990 | 0.9813 |
| 0.1729 | 5.0 | 1995 | 0.0952 | 0.9823 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
BSC-LT/roberta-large-bne
|
BSC-LT
| 2021-10-21T10:32:31Z | 37 | 7 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las <mask> Pedroche y Chicote."
- text: "El artista Antonio Orozco es un colaborador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne
# RoBERTa large trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-large-bne-sqac
|
BSC-LT
| 2021-10-21T10:32:05Z | 28 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "qa"
- "question answering"
datasets:
- "BSC-TeMU/SQAC"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-sqac
# Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC).
## Evaluation and results
F1 Score: 0.7993 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-large-bne-capitel-ner
|
BSC-LT
| 2021-10-21T10:31:30Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-ner
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
## Evaluation and results
F1 Score: 0.8998
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne
|
BSC-LT
| 2021-10-21T10:30:31Z | 2,054 | 9 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las presentará <mask>."
- text: "David Broncano es un presentador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
# RoBERTa base trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-capitel-pos
|
BSC-LT
| 2021-10-21T10:29:55Z | 27 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "pos"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
widget:
- text: "Festival de San Sebastián: Johnny Depp recibirá el premio Donostia en pleno rifirrafe judicial con Amber Heard"
- text: "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."
- text: "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."
- text: "El Tribunal Superior de Justicia se pronunció ayer: \"Hay base legal dentro del marco jurídico actual\"."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).
## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-capitel-ner
|
BSC-LT
| 2021-10-21T10:29:35Z | 43 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
## Evaluation and results
F1 Score: 0.8960
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-capitel-ner-plus
|
BSC-LT
| 2021-10-21T10:29:17Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
inference:
parameters:
aggregation_strategy: "first"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
**IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne-capitel-ner
Examples:
This model:
- "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {as:S-PER}{ier:S-PER} y vivo en {barcelona:S-LOC} todo el año."
- "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {par:B-LOC}{k:I-LOC} {gü:E-LOC}{ell:E-LOC} tras salir del {barcelona:B-ORG} {super:I-ORG}{com:I-ORG}{pu:I-ORG}{ting:I-ORG} {cen:E-ORG}{ter:E-ORG}."
Model trained on original data:
- "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing)
- "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing)
## Evaluation and results
F1 Score: 0.8867
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
patrickvonplaten/unispeech-sat-base-plus-timit-ft
|
patrickvonplaten
| 2021-10-21T10:05:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: unispeech-sat-base-plus-timit-ft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# unispeech-sat-base-plus-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus](https://huggingface.co/microsoft/unispeech-sat-base-plus) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6549
- Wer: 0.4051
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3838 | 0.69 | 100 | 3.2528 | 1.0 |
| 2.9608 | 1.38 | 200 | 2.9682 | 1.0 |
| 2.9574 | 2.07 | 300 | 2.9346 | 1.0 |
| 2.8555 | 2.76 | 400 | 2.7612 | 1.0 |
| 1.7418 | 3.45 | 500 | 1.5732 | 0.9857 |
| 0.9606 | 4.14 | 600 | 1.0014 | 0.7052 |
| 0.8334 | 4.83 | 700 | 0.7691 | 0.6161 |
| 0.852 | 5.52 | 800 | 0.7169 | 0.5997 |
| 0.5707 | 6.21 | 900 | 0.6821 | 0.5527 |
| 0.4235 | 6.9 | 1000 | 0.6078 | 0.5140 |
| 0.4357 | 7.59 | 1100 | 0.5927 | 0.4982 |
| 0.5004 | 8.28 | 1200 | 0.5814 | 0.4826 |
| 0.3757 | 8.97 | 1300 | 0.5951 | 0.4643 |
| 0.2579 | 9.66 | 1400 | 0.5990 | 0.4581 |
| 0.2087 | 10.34 | 1500 | 0.5864 | 0.4488 |
| 0.3155 | 11.03 | 1600 | 0.5836 | 0.4464 |
| 0.2701 | 11.72 | 1700 | 0.6045 | 0.4348 |
| 0.172 | 12.41 | 1800 | 0.6494 | 0.4344 |
| 0.1529 | 13.1 | 1900 | 0.5915 | 0.4241 |
| 0.2411 | 13.79 | 2000 | 0.6156 | 0.4246 |
| 0.2348 | 14.48 | 2100 | 0.6363 | 0.4206 |
| 0.1429 | 15.17 | 2200 | 0.6394 | 0.4161 |
| 0.1151 | 15.86 | 2300 | 0.6186 | 0.4167 |
| 0.1723 | 16.55 | 2400 | 0.6498 | 0.4124 |
| 0.1997 | 17.24 | 2500 | 0.6541 | 0.4076 |
| 0.1297 | 17.93 | 2600 | 0.6546 | 0.4117 |
| 0.101 | 18.62 | 2700 | 0.6471 | 0.4075 |
| 0.1272 | 19.31 | 2800 | 0.6586 | 0.4065 |
| 0.1901 | 20.0 | 2900 | 0.6549 | 0.4051 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
MINYOUNG/distilbert-base-uncased-finetuned-cola
|
MINYOUNG
| 2021-10-21T09:42:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5494735380761103
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8540
- Matthews Correlation: 0.5495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5219 | 1.0 | 535 | 0.5314 | 0.4095 |
| 0.346 | 2.0 | 1070 | 0.5141 | 0.5054 |
| 0.2294 | 3.0 | 1605 | 0.6351 | 0.5200 |
| 0.1646 | 4.0 | 2140 | 0.7575 | 0.5459 |
| 0.1235 | 5.0 | 2675 | 0.8540 | 0.5495 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
pritoms/distilgpt2-finetuned-mit-lecture
|
pritoms
| 2021-10-21T08:59:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-mit-lecture
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-mit-lecture
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8377
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 144 | 3.8737 |
| No log | 2.0 | 288 | 3.8436 |
| No log | 3.0 | 432 | 3.8377 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
bochaowei/t5-small-finetuned-xsum-wei2
|
bochaowei
| 2021-10-21T07:21:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 29.2287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-wei2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4131
- Rouge1: 29.2287
- Rouge2: 8.4073
- Rougel: 23.0934
- Rougelsum: 23.0954
- Gen Len: 18.8236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.633 | 1.0 | 17004 | 2.4131 | 29.2287 | 8.4073 | 23.0934 | 23.0954 | 18.8236 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/s66jewelevans
|
huggingtweets
| 2021-10-20T23:06:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/s66jewelevans/1634771194675/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1313199276852342784/fJ8Lb2C__400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jewel Evans</div>
<div style="text-align: center; font-size: 14px;">@s66jewelevans</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jewel Evans.
| Data | Jewel Evans |
| --- | --- |
| Tweets downloaded | 1714 |
| Retweets | 2 |
| Short tweets | 20 |
| Tweets kept | 1692 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ec5yuuj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @s66jewelevans's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kxbfdnt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kxbfdnt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/s66jewelevans')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AyushPJ/ai-club-inductions-21-nlp-roBERTa
|
AyushPJ
| 2021-10-20T22:33:57Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-club-inductions-21-nlp-roBERTa
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
bochaowei/t5-small-finetuned-xsum-wei1
|
bochaowei
| 2021-10-20T18:33:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
20% of the training data
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 27.5875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-wei1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5287
- Rouge1: 27.5875
- Rouge2: 7.4083
- Rougel: 21.5654
- Rougelsum: 21.5716
- Gen Len: 18.8205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7677 | 1.0 | 3401 | 2.5441 | 27.4235 | 7.2208 | 21.3535 | 21.3636 | 18.8311 |
| 2.735 | 2.0 | 6802 | 2.5287 | 27.5875 | 7.4083 | 21.5654 | 21.5716 | 18.8205 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
monologg/koelectra-base-generator
|
monologg
| 2021-10-20T16:55:00Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-generator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3]
```
## Example using ElectraForMaskedLM
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="monologg/koelectra-base-generator",
tokenizer="monologg/koelectra-base-generator"
)
print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token)))
```
|
monologg/koelectra-base-v3-discriminator
|
monologg
| 2021-10-20T16:53:40Z | 31,234 | 30 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v3 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-v3-discriminator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 11229, 29173, 13352, 25541, 4110, 7824, 17788, 18, 3]
```
## Example using ElectraForPreTraining
```python
import torch
from transformers import ElectraForPreTraining, ElectraTokenizer
discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-v3-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
sentence = "나는 방금 밥을 먹었다."
fake_sentence = "나는 내일 밥을 먹었다."
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
print(list(zip(fake_tokens, predictions.tolist()[1:-1])))
```
|
bochaowei/t5-small-finetuned-xsum-wei0
|
bochaowei
| 2021-10-20T15:10:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei0
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 25.7398
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6289
- Rouge1: 25.7398
- Rouge2: 6.1361
- Rougel: 19.8262
- Rougelsum: 19.8284
- Gen Len: 18.7984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
YushiUeda/test
|
YushiUeda
| 2021-10-20T14:48:21Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"diarization",
"dataset:mini_librispeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- diarization
language:
datasets:
- mini_librispeech
license: cc-by-4.0
---
## ESPnet2 DIAR model
### `YushiUeda/test`
This model was trained by Yushi Ueda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4
pip install -e .
cd egs2/mini_librispeech/diar1
./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test
```
<!-- Generated by scripts/utils/show_diar_result.sh -->
# RESULTS
## Environments
- date: `Wed Aug 25 23:29:07 EDT 2021`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.2a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92`
- Commit date: `Tue Aug 24 19:50:44 2021 -0400`
## `diar_train_diar_raw_max_epoch20`
### DER
`dev_clean_2_ns2_beta2_500`
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med1_collar0.0|32.42|
|result_th0.3_med11_collar0.0|32.03|
|result_th0.4_med1_collar0.0|30.96|
|result_th0.4_med11_collar0.0|30.26|
|result_th0.5_med1_collar0.0|30.35|
|result_th0.5_med11_collar0.0|29.37|
|result_th0.6_med1_collar0.0|30.77|
|result_th0.6_med11_collar0.0|29.52|
|result_th0.7_med1_collar0.0|32.60|
|result_th0.7_med11_collar0.0|31.03|
## DIAR config
<details><summary>expand</summary>
```
config: conf/train_diar.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_train_diar_raw_max_epoch20
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 20
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 3
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_stats_8k/train/speech_shape
- exp/diar_stats_8k/train/spk_labels_shape
valid_shape_file:
- exp/diar_stats_8k/valid/speech_shape
- exp/diar_stats_8k/valid/spk_labels_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 200000
chunk_shift_ratio: 0.5
num_cache_chunks: 64
train_data_path_and_name_and_type:
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
valid_data_path_and_name_and_type:
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.01
scheduler: noamlr
scheduler_conf:
warmup_steps: 1000
num_spk: 2
init: xavier_uniform
input_size: null
model_conf:
loss_type: pit
use_preprocessor: true
frontend: default
frontend_conf:
fs: 8k
hop_length: 128
normalize: global_mvn
normalize_conf:
stats_file: exp/diar_stats_8k/train/feats_stats.npz
encoder: transformer
encoder_conf:
input_layer: linear
num_blocks: 2
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
decoder: linear
decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf: {}
required:
- output_dir
version: 0.10.2a1
distributed: false
```
</details>
|
Monsia/autonlp-tweets-classification-23044997
|
Monsia
| 2021-10-20T14:38:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:Monsia/autonlp-data-tweets-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Monsia/autonlp-data-tweets-classification
co2_eq_emissions: 4.819872182577655
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 23044997
- CO2 Emissions (in grams): 4.819872182577655
## Validation Metrics
- Loss: 0.001594889909029007
- Accuracy: 0.9997478885667465
- Macro F1: 0.9991190902836993
- Micro F1: 0.9997478885667465
- Weighted F1: 0.9997476735518704
- Macro Precision: 0.9998014460161265
- Micro Precision: 0.9997478885667465
- Weighted Precision: 0.9997479944069787
- Macro Recall: 0.9984426545713851
- Micro Recall: 0.9997478885667465
- Weighted Recall: 0.9997478885667465
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Monsia/autonlp-tweets-classification-23044997
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/dril-linaarabii
|
huggingtweets
| 2021-10-20T11:36:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/dril-linaarabii/1634729786636/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1423543147305619456/9RT-Ji0Z_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Lina Arabi</div>
<div style="text-align: center; font-size: 14px;">@dril-linaarabii</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Lina Arabi.
| Data | wint | Lina Arabi |
| --- | --- | --- |
| Tweets downloaded | 3227 | 3130 |
| Retweets | 473 | 896 |
| Short tweets | 317 | 322 |
| Tweets kept | 2437 | 1912 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yq3shwo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-linaarabii's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21rpwe17) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21rpwe17/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dril-linaarabii')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
aditeyabaral/sentencetransformer-distilbert-hinglish-small
|
aditeyabaral
| 2021-10-20T09:04:04Z | 173 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-distilbert-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-distilbert-hinglish-small')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-distilbert-hinglish-small')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-distilbert-hinglish-small')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-distilbert-hinglish-small)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
lapcameraatp/cameragiamsat
|
lapcameraatp
| 2021-10-20T08:53:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z | ERROR: type should be string, got "https://camerasaigon24h.com\nhttps://cameragiamsat360.com\nhttps://lapdatcameracongty.vn\nhttps://lapdatcamerawifi.vn\nhttps://lapcamerawifi.com\nhttps://giacameraquansat.com\nhttps://cameraquansatre.com\nhttps://cameraanninhwifi.com\n\nhttps://camerawifigiadinh.com/\nhttps://lapcameratanphu.com\nhttp://camerathehemoi.com\nhttp://lapcameratanbinh.com\nhttp://lapcamerabinhtan.com\nhttp://lapcameraquan2giare.com\nhttp://cameraquan12.com\nhttp://cameraquan3giare.com\nhttp://lapdatcameraquan4.com\nhttp://lapdatcameraquan10.com\nhttp://lapdatcameraquan7.com\nhttp://camerabinhthanh.com\nhttp://lapcameraquan9giare.com\nhttp://lapdatcameraquan11.com\nhttp://lapcameragiarethuduc.com\nhttp://lapdatcameraquan6.com\nhttp://lapdatcameraquan5.com\nhttp://lapcameraquan1.com\nhttp://cameraquan8.com\nhttp://cameranhatranggiare.com\nhttp://lapcamerahocmon.com\nhttp://lapcameragiaregovap.com\nhttp://lapcameraphunhuan.com\nhttp://cameragiarebinhduong.com\nhttp://phanphoicameragiare.com\nhttp://camerawifigiadinh.com/\nhttp://cameraphanthietgiare.com/" |
mrm8488/t5-base-finetuned-break_data
|
mrm8488
| 2021-10-20T08:31:28Z | 962 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:break_data",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- break_data
widget:
- text: "paraphrase: The composer of Sands Theme plays what type of guitar?"
---
# T5-base fine-tuned on break_data / QDMR-high-level ❓➡️📋
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [break_data](https://huggingface.co/nlp/viewer/?dataset=break_data&config=QDMR-high-level) dataset for **QDMRs**.
## Details of T5 📜 ➡️ 📜
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (QDMRs) - Dataset 📚
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases. This repository contains the Break dataset along with information on the exact data format.
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| break_data | train | 17503 |
| break_data | valid | 3130 |
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The main change is at preprocessing ```inputs``` and ```targets``` we feed to the model. We do it as a *paraphrasing task*.
## Model in Action 🚀
```python
# Tip: By now, install transformers from source
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-break_data")
model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-break_data")
def get_decomposition(question):
input_text = "paraphrase: %s </s>" % question
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=32)
return tokenizer.decode(output[0])
question = "The composer of Sands Theme plays what type of guitar?"
get_decomposition(question)
# output: 'return Sands Theme ;return composer of #1 ;return guitar that #2 plays'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
aditeyabaral/sentencetransformer-bert-hinglish-small
|
aditeyabaral
| 2021-10-20T06:28:16Z | 9 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-bert-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-bert-hinglish-small')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-bert-hinglish-small')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-bert-hinglish-small')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-bert-hinglish-small)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
chrisjay/masakhane_benchmarks
|
chrisjay
| 2021-10-20T05:55:51Z | 0 | 0 | null |
[
"african-languages",
"machine-translation",
"text",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: african-languages
tags:
- african-languages
- machine-translation
- text
license: apache-2.0
model-index:
- name: Masakhane Benchmark Models
results:
- task:
name: Machine Translation
type: machine-translation
dataset:
name: masakhane benchmarks
args: african-languages
---
# Interacting with the Masakhane Benchmark Models
I created this demo for very easy interaction with the [benchmark models on Masakhane](https://github.com/masakhane-io/masakhane-mt/tree/master/benchmarks) which were trained with [JoeyNMT](https://github.com/chrisemezue/joeynmt)(my forked version).
To access the space click [here](https://huggingface.co/spaces/chrisjay/masakhane-benchmarks).
To include your language, all you need to do is:
1. Create a folder in the format *src-tgt/main* for your language pair, if it does not exist.
2. Inside the *main* folder put the following files:
1. model checkpoint. Rename it to `best.ckpt`.
2. `config.yaml` file. This is the JoeyNMT config file which loads the model an pre-processing parameters.
3. `src_vocab.txt` file.
4. `trg_vocab.txt` file.
The space currently supports these languages:
| source language | target language |
|:---------------:|:---------------:|
| English | Swahili |
| English | Afrikaans |
| English | Arabic |
| English | Urhobo |
| English | Ẹ̀dó |
| Efik | English |
| English | Hausa |
| English | Igbo |
| English | Fon |
| English | Twi |
| English | Dendi |
| English | Ẹ̀sán |
| English | Isoko |
| English | Kamba |
| English | Luo |
| English | Southern Ndebele |
| English | Tshivenda |
| Shona | English |
| Swahili | English |
| Yoruba | English |
TO DO:
1. Include more languages from the benchmark.
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition
|
Bagus
| 2021-10-20T05:38:41Z | 37 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio",
"audio-classification",
"speech",
"el",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:04Z |
---
language: el
datasets:
- aesdd
tags:
- audio
- audio-classification
- speech
license: apache-2.0
---
~~~
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!git clone https://github.com/m3hrdadfi/soxan
cd soxan
~~~
# prediction
~~~
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
~~~
~~~
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
~~~
~~~
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torchaudio.load(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
~~~
# prediction
~~~
# path for a sample
path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'
outputs = predict(path, sampling_rate)
~~~
~~~
[{'Emotion': 'anger', 'Score': '98.3%'},
{'Emotion': 'disgust', 'Score': '0.0%'},
{'Emotion': 'fear', 'Score': '0.4%'},
{'Emotion': 'happiness', 'Score': '0.7%'},
{'Emotion': 'sadness', 'Score': '0.5%'}]
~~~
|
huggingartists/adele
|
huggingartists
| 2021-10-20T04:50:21Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/adele",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/adele
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4c3ac1f1d845d251671a892309b5f9b5.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Adele</div>
<a href="https://genius.com/artists/adele">
<div style="text-align: center; font-size: 14px;">@adele</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Adele.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/adele).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/adele")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1yyqw6ss/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Adele's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/adele')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/adele")
model = AutoModelWithLMHead.from_pretrained("huggingartists/adele")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
aditeyabaral/sentencetransformer-distilbert-hinglish-big
|
aditeyabaral
| 2021-10-20T01:24:00Z | 153 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-distilbert-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-distilbert-hinglish-big')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-distilbert-hinglish-big')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-distilbert-hinglish-big')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-distilbert-hinglish-big)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
yazdipour/text-to-sparql-t5-base-qald9
|
yazdipour
| 2021-10-19T23:25:20Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-base-2021-10-19_23-02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sparql-qald9-t5-base-2021-10-19_23-02
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_lastDS](https://huggingface.co/yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_lastDS) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:-----------------------------------------------------------------------------:|:-------:|
| No log | 1.0 | 51 | 1.8300 | 19.0 | 0.3640 | 0.0346 | 0.1943 | 10.0358 | [72.88988261598658, 50.27455765710799, 35.93015446608462, 28.454070201643017] | 0.2281 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/iamdevloper
|
huggingtweets
| 2021-10-19T20:59:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/iamdevloper/1634677176847/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1178631635606151168/yIlrcg4o_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">I Am Devloper</div>
<div style="text-align: center; font-size: 14px;">@iamdevloper</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from I Am Devloper.
| Data | I Am Devloper |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 190 |
| Short tweets | 233 |
| Tweets kept | 2821 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2k1120ro/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iamdevloper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wr63mia) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wr63mia/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iamdevloper')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
aditeyabaral/sentencetransformer-bert-hinglish-big
|
aditeyabaral
| 2021-10-19T19:38:38Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-bert-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-bert-hinglish-big')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-bert-hinglish-big')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-bert-hinglish-big')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-bert-hinglish-big)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hugggof/ConvTasNet-DAMP-Vocals
|
hugggof
| 2021-10-19T19:28:08Z | 0 | 2 | null |
[
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- audacity
inference: false
sample_rate: 8000
---
This is an Audacity wrapper for the model, forked from the repository `groadabike/ConvTasNet_DAMP-VSEP_enhboth`,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied directly from `groadabike/ConvTasNet_DAMP-VSEP_enhboth`:
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
```yaml
data:
channels: 1
n_src: 2
root_path: data
sample_rate: 16000
samples_per_track: 10
segment: 3.0
task: enh_both
filterbank:
kernel_size: 20
n_filters: 256
stride: 10
main_args:
exp_dir: exp/train_convtasnet
help: None
masknet:
bn_chan: 256
conv_kernel_size: 3
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 4
n_src: 2
norm_type: gLN
skip_chan: 256
optim:
lr: 0.0003
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 12
early_stop: True
epochs: 50
half_lr: True
num_workers: 12
```
### Results:
```yaml
si_sdr: 14.018196157142519
si_sdr_imp: 14.017103133809577
sdr: 14.498517291333885
sdr_imp: 14.463389151567865
sir: 24.149634529133372
sir_imp: 24.11450638936735
sar: 15.338597389045935
sar_imp: -137.30634122401517
stoi: 0.7639416744417206
stoi_imp: 0.1843383526963759
```
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
hugggof/ConvTasNet_WHAM_sepclean
|
hugggof
| 2021-10-19T19:25:37Z | 0 | 0 | null |
[
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- audacity
inference: false
---
This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied from `mpariente/ConvTasNet_WHAM_sepclean`:
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.
### Training config:
```yaml
data:
n_src: 2
mode: min
nondefault_nsrc: None
sample_rate: 8000
segment: 3
task: sep_clean
train_dir: data/wav8k/min/tr/
valid_dir: data/wav8k/min/cv/
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/wham
gpus: -1
help: None
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 2
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
num_workers: 4
```
### Results:
```yaml
si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015
```
### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.
|
maxspaziani/bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
|
maxspaziani
| 2021-10-19T17:58:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6717 | 1.0 | 1014 | 2.6913 |
| 2.4869 | 2.0 | 2028 | 2.5843 |
| 2.3411 | 3.0 | 3042 | 2.5095 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab
|
patrickvonplaten
| 2021-10-19T17:18:47Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-turkish-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4055
- Wer: 0.4800
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0179 | 4.21 | 400 | 1.4935 | 1.0249 |
| 0.7075 | 8.42 | 800 | 0.4546 | 0.6071 |
| 0.3072 | 12.63 | 1200 | 0.3947 | 0.5401 |
| 0.2145 | 16.84 | 1600 | 0.4049 | 0.5194 |
| 0.1647 | 21.05 | 2000 | 0.4199 | 0.5003 |
| 0.1338 | 25.26 | 2400 | 0.4144 | 0.4859 |
| 0.116 | 29.47 | 2800 | 0.4055 | 0.4800 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
doc2query/stackexchange-t5-base-v1
|
doc2query
| 2021-10-19T16:26:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl
widget:
- text: "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
license: apache-2.0
---
# doc2query/stackexchange-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/stackexchange-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt')
outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
num_return_sequences=5)
print("Text:")
print(text)
print("\nGenerated Queries:")
for i in range(len(outputs)):
query = tokenizer.decode(outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
```
**Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
## Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 449k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (title, best_answer_pairs) from StackExchange.
|
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