modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
husnu/xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6
|
husnu
| 2022-01-14T20:57:15Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6
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. -->
# xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2578
## 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: 48
- eval_batch_size: 48
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3828 | 1.0 | 1845 | 1.7946 |
| 1.5827 | 2.0 | 3690 | 1.4123 |
| 1.404 | 3.0 | 5535 | 1.3142 |
| 1.346 | 4.0 | 7380 | 1.2819 |
| 1.2871 | 5.0 | 9225 | 1.2630 |
| 1.2538 | 6.0 | 11070 | 1.2578 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
addy88/eli5-all-mpnet-base-v2
|
addy88
| 2022-01-14T13:24:40Z | 14 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"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
---
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on [ELI5](https://huggingface.co/datasets/eli5)
<!--- Describe your model here -->
## Usage (Sentence-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('addy88/eli5-all-mpnet-base-v2')
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('addy88/eli5-all-mpnet-base-v2')
model = AutoModel.from_pretrained('addy88/eli5-all-mpnet-base-v2')
# 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=addy88/eli5-all-mpnet-base-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 14393 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"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": 1439,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(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
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
vachonni/wav2vec2-large-xls-r-300m-da-colab
|
vachonni
| 2022-01-14T12:14:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-da-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-xls-r-300m-da-colab
This model is a fine-tuned version of [Alvenir/wav2vec2-base-da](https://huggingface.co/Alvenir/wav2vec2-base-da) 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: 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
anirudh21/xlnet-base-cased-finetuned-rte
|
anirudh21
| 2022-01-14T07:04:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: xlnet-base-cased-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6895306859205776
---
<!-- 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. -->
# xlnet-base-cased-finetuned-rte
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0656
- Accuracy: 0.6895
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.7007 | 0.4874 |
| No log | 2.0 | 312 | 0.6289 | 0.6751 |
| No log | 3.0 | 468 | 0.7020 | 0.6606 |
| 0.6146 | 4.0 | 624 | 1.0573 | 0.6570 |
| 0.6146 | 5.0 | 780 | 1.0656 | 0.6895 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
zhichao158/wav2vec2-xls-r-common_voice-tr-ft
|
zhichao158
| 2022-01-14T07:03:32Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-common_voice-tr-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. -->
# wav2vec2-xls-r-common_voice-tr-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3736
- Wer: 0.2930
- Cer: 0.0708
## 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: 0.0005
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 96
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.5462 | 13.51 | 500 | 0.4423 | 0.4807 | 0.1188 |
| 0.342 | 27.03 | 1000 | 0.3781 | 0.3954 | 0.0967 |
| 0.2272 | 40.54 | 1500 | 0.3816 | 0.3595 | 0.0893 |
| 0.1805 | 54.05 | 2000 | 0.3943 | 0.3487 | 0.0854 |
| 0.1318 | 67.57 | 2500 | 0.3818 | 0.3262 | 0.0801 |
| 0.1213 | 81.08 | 3000 | 0.3777 | 0.3113 | 0.0758 |
| 0.0639 | 94.59 | 3500 | 0.3788 | 0.2953 | 0.0716 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.8.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
LACAI/DialoGPT-large-PFG
|
LACAI
| 2022-01-14T05:18:30Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
Base model: [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker control token to prompt system responses
The following Dialogues were excluded:
- Those with donation amounts outside of the task range of [$0, $2].
- Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue.
- Those with more than 800 words.
Stats:
- Training set: 519 dialogues
- Validation set: 58 dialogues
- ~20 utterances per dialogue
|
LACAI/DialoGPT-small-PFG
|
LACAI
| 2022-01-14T01:36:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker control token to prompt system responses
The following Dialogues were excluded:
- Those with donation amounts outside of the task range of [$0, $2].
- Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue.
- Those with more than 800 words.
Stats:
- Training set: 519 dialogues
- Validation set: 58 dialogues
- ~20 utterances per dialogue
|
bob80333/speechbrain_ja2en_st_63M_yt600h
|
bob80333
| 2022-01-14T00:45:47Z | 18 | 1 |
speechbrain
|
[
"speechbrain",
"speech-translation",
"CTC",
"Attention",
"Transformer",
"pytorch",
"automatic-speech-recognition",
"en",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: "en"
thumbnail:
tags:
- speech-translation
- CTC
- Attention
- Transformer
- pytorch
- speechbrain
- automatic-speech-recognition
metrics:
- BLEU
---
# Conformer Encoder/Decoder for Speech Translation
This model was trained with [SpeechBrain](https://speechbrain.github.io), and is based on the Fisher Callhome recipie.
The performance of the model is the following:
| Release | CoVoSTv2 JA->EN Test BLEU | Custom Dataset Validation BLEU | Custom Dataset Test BLEU | GPUs |
|:-------------:|:--------------:|:--------------:|:--------------:|:--------:|
| 01-13-21 | 9.73 | 8.38 | 12.01 | 1xRTX 3090 |
This model was trained on subtitled audio downloaded from YouTube, and was not fine-tuned on the CoVoSTv2 training set.
When calculating the BLEU score for CoVoSTv2, the utterances were first preprocessed by the same pipeline that preprocessed the original data for the model, which includes removing all punctuation outside of apostrophes, and removing capitalization, similar to the data preprocessing done for the Fisher Callhome dataset in the speechbrain recipe.
## Pipeline description
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, install SpeechBrain with the following command:
```
pip install speechbrain
```
### Transcribing your own audio files (Spoken Japanese, to written English)
```python
from speechbrain.pretrained import EncoderDecoderASR
st_model = EncoderDecoderASR.from_hparams(source="bob80333/speechbrain_ja2en_st_63M_yt600h")
st_model.transcribe_file("your_file_here.wav")
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Limitations:
The model is likely to get caught in repetitions. The model is not very good at translation, which is reflected by its low BLEU scores.
The outputs of this model are unlikely to be correct, do not rely on it for any serious purpose.
This model was trained on data from Youtube, and has inherited whatever biases can be found in Youtube audio/subtitles.
The creator of this model doesn't actually know Japanese.
|
flax-community/pino-bigbird-roberta-base
|
flax-community
| 2022-01-13T15:29:26Z | 34 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"big_bird",
"fill-mask",
"nl",
"dataset:mC4",
"dataset:Dutch_news",
"arxiv:2007.14062",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: nl
datasets:
- mC4
- Dutch_news
---
# Pino (Dutch BigBird) base model
Created by [Dat Nguyen](https://www.linkedin.com/in/dat-nguyen-49a641138/) & [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/) during the [Hugging Face community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104)
(Not finished yet)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model on Dutch language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird).
## Model description
BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts.
## How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BigBirdModel
# by default its in `block_sparse` mode with num_random_blocks=3, block_size=64
model = BigBirdModel.from_pretrained("flax-community/pino-bigbird-roberta-base")
# you can change `attention_type` to full attention like this:
model = BigBirdModel.from_pretrained("flax-community/pino-bigbird-roberta-base", attention_type="original_full")
# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdModel.from_pretrained("flax-community/pino-bigbird-roberta-base", block_size=16, num_random_blocks=2)
```
## Training Data
This model is pre-trained on four publicly available datasets: **mC4**, and scraped **Dutch news** from NRC en Nu.nl. It uses the the fast universal Byte-level BPE (BBPE) in contrast to the sentence piece tokenizer and vocabulary as RoBERTa (which is in turn borrowed from GPT2).
## Training Procedure
The data is cleaned as follows:
Remove texts containing HTML codes / javascript codes / loremipsum / policies
Remove lines without end mark.
Remove too short texts, words
Remove too long texts, words
Remove bad words
## BibTeX entry and citation info
```tex
@misc{zaheer2021big,
title={Big Bird: Transformers for Longer Sequences},
author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed},
year={2021},
eprint={2007.14062},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
keras-io/deep-dream
|
keras-io
| 2022-01-13T14:53:54Z | 10 | 3 |
tf-keras
|
[
"tf-keras",
"gan",
"generative adversarial networks",
"deep dream",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- gan
- generative adversarial networks
- deep dream
license:
- cc0-1.0
---
## Keras Implementation of Deep Dream 🦚🌌
This repo contains the model and the notebook [for this Deep Dream implementation of Keras](https://keras.io/examples/generative/deep_dream/).
Full credits to: [François Chollet](https://twitter.com/fchollet)
## Background Information
"Deep dream" is an image-filtering technique which consists of taking an image classification model, and running gradient ascent over an input image to try to maximize the activations of specific layers (and sometimes, specific units in specific layers) for this input. It produces hallucination-like visuals.
It was first introduced by Alexander Mordvintsev from Google in July 2015.
Process:
- Load the original image.
- Define a number of processing scales ("octaves"), from smallest to largest.
- Resize the original image to the smallest scale.
- For every scale, starting with the smallest (i.e. current one): - Run gradient ascent - Upscale image to the next scale - Re-inject the detail that was lost at upscaling time
- Stop when we are back to the original size. To obtain the detail lost during upscaling, we simply take the original image, shrink it down, upscale it, and compare the result to the (resized) original image.
|
keras-io/deep-deterministic-policy-gradient
|
keras-io
| 2022-01-13T14:53:44Z | 7 | 0 |
tf-keras
|
[
"tf-keras",
"reinforcement learning",
"cartpole",
"deep deterministic policy gradient",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- reinforcement learning
- cartpole
- deep deterministic policy gradient
license:
- cc0-1.0
---
## Keras Implementation of Deep Deterministic Policy Gradient ⏱🤖
This repo contains the model and the notebook [to this Keras example on Deep Deterministic Policy Gradient on pendulum](https://keras.io/examples/rl/ddpg_pendulum/).
Full credits to: [Hemant Singh](https://github.com/amifunny)
## Background Information
Deep Deterministic Policy Gradient (DDPG) is a model-free off-policy algorithm for learning continous actions.
It combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). It uses Experience Replay and slow-learning target networks from DQN, and it is based on DPG, which can operate over continuous action spaces.
This tutorial closely follow this paper - Continuous control with deep reinforcement learning
We are trying to solve the classic Inverted Pendulum control problem. In this setting, we can take only two actions: swing left or swing right.
What make this problem challenging for Q-Learning Algorithms is that actions are continuous instead of being discrete. That is, instead of using two discrete actions like -1 or +1, we have to select from infinite actions ranging from -2 to +2.
Just like the Actor-Critic method, we have two networks:
Actor - It proposes an action given a state.
Critic - It predicts if the action is good (positive value) or bad (negative value) given a state and an action.
DDPG uses two more techniques not present in the original DQN:
First, it uses two Target networks.
Why? Because it add stability to training. In short, we are learning from estimated targets and Target networks are updated slowly, hence keeping our estimated targets stable.
Conceptually, this is like saying, "I have an idea of how to play this well, I'm going to try it out for a bit until I find something better", as opposed to saying "I'm going to re-learn how to play this entire game after every move". See this StackOverflow answer.
Second, it uses Experience Replay.
We store list of tuples (state, action, reward, next_state), and instead of learning only from recent experience, we learn from sampling all of our experience accumulated so far.
|
keras-io/ppo-cartpole
|
keras-io
| 2022-01-13T14:53:36Z | 4 | 0 |
tf-keras
|
[
"tf-keras",
"reinforcement learning",
"proximal policy optimization",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- reinforcement learning
- proximal policy optimization
license:
- cc0-1.0
---
## Keras Implementation of Proximal Policy Optimization on Cartpole Environment 🔨🤖
This repo contains the model and the notebook [to this Keras example on PPO for Cartpole](https://keras.io/examples/rl/ppo_cartpole/).
Full credits to: Ilias Chrysovergis
## Background Information
### CartPole-v0
A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center. After 200 steps the episode ends. Thus, the highest return we can get is equal to 200.
### Proximal Policy Optimization
PPO is a policy gradient method and can be used for environments with either discrete or continuous action spaces. It trains a stochastic policy in an on-policy way. Also, it utilizes the actor critic method. The actor maps the observation to an action and the critic gives an expectation of the rewards of the agent for the observation given. Firstly, it collects a set of trajectories for each epoch by sampling from the latest version of the stochastic policy. Then, the rewards-to-go and the advantage estimates are computed in order to update the policy and fit the value function. The policy is updated via a stochastic gradient ascent optimizer, while the value function is fitted via some gradient descent algorithm. This procedure is applied for many epochs until the environment is solved.
|
keras-io/simple-mnist-convnet
|
keras-io
| 2022-01-13T14:52:44Z | 2 | 0 |
tf-keras
|
[
"tf-keras",
"lstm",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- lstm
license:
- cc0-1.0
---
## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣
This repo contains the model and the notebook [on Simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/).
Full credits to: [François Chollet](https://github.com/fchollet)
|
mahaamami/distilroberta-base-model-transcript
|
mahaamami
| 2022-01-13T13:28:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-model-transcript
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. -->
# distilroberta-base-model-transcript
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8922
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1193 | 1.0 | 5570 | 1.9873 |
| 2.0502 | 2.0 | 11140 | 1.9304 |
| 1.9718 | 3.0 | 16710 | 1.8922 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/h_ototake-hirox246-ochyai
|
huggingtweets
| 2022-01-13T07:45:50Z | 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: http://www.huggingtweets.com/h_ototake-hirox246-ochyai/1642059945521/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/646595746905620480/oeKI14gB_400x400.png')">
</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/1072419376668782597/hhmhNVER_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/1481142443068198912/NCrXoLUB_400x400.jpg')">
</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">ひろゆき, Hiroyuki Nishimura & 落合陽一 Yoichi OCHIAI & 乙武 洋匡</div>
<div style="text-align: center; font-size: 14px;">@h_ototake-hirox246-ochyai</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 ひろゆき, Hiroyuki Nishimura & 落合陽一 Yoichi OCHIAI & 乙武 洋匡.
| Data | ひろゆき, Hiroyuki Nishimura | 落合陽一 Yoichi OCHIAI | 乙武 洋匡 |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3240 | 3238 |
| Retweets | 281 | 2238 | 1259 |
| Short tweets | 1980 | 574 | 1437 |
| Tweets kept | 987 | 428 | 542 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3k39l31f/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 @h_ototake-hirox246-ochyai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1d9okxed) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1d9okxed/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/h_ototake-hirox246-ochyai')
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)
|
ptaszynski/yacis-electra-small-japanese
|
ptaszynski
| 2022-01-13T01:43:17Z | 28 | 7 |
transformers
|
[
"transformers",
"pytorch",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ja
license: cc-by-sa-4.0
datasets:
- YACIS corpus
---
# yacis-electra-small
This is [ELECTRA](https://github.com/google-research/electra) Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of [YACIS](https://github.com/ptaszynski/yacis-corpus) blog corpus.
The corpus was tokenized for pretraining with [MeCab](https://taku910.github.io/mecab/). Subword tokenization was done with WordPiece.
## Model architecture
This model uses ELECTRA Small model settings, 12 layers, 128 dimensions of hidden states, and 12 attention heads.
Vocabulary size was set to 32,000 tokens.
## Training data and libraries
YACIS-ELECTRA is trained on the whole of [YACIS](https://github.com/ptaszynski/yacis-corpus) blog corpus, which is a Japanese blog corpus containing 5.6 billion words in 354 million sentences.
The corpus was originally split into sentences using custom rules, and each sentence was tokenized using [MeCab](https://taku910.github.io/mecab/). Subword tokenization for pretraining was done with WordPiece.
We used original [ELECTRA](https://github.com/google-research/electra) repository for pretraining. The pretrainig process took 7 days and 6 hours under the following environment: CPU: Intel Core i9-7920X, RAM: 132 GB, GPU: GeForce GTX 1080 Ti x1.
## Licenses
The pretrained model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License.
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
## Citations
Please, cite the model using the following citation.
```
@inproceedings{shibata2022yacis-electra,
title={日本語大規模ブログコーパスYACISに基づいたELECTRA事前学習済み言語モデルの作成及び性能評価},
% title={Development and performance evaluation of ELECTRA pretrained language model based on YACIS large-scale Japanese blog corpus [in Japanese]}, %% for English citations
author={柴田 祥伍 and プタシンスキ ミハウ and エロネン ユーソ and ノヴァコフスキ カロル and 桝井 文人},
% author={Shibata, Shogo and Ptaszynski, Michal and Eronen, Juuso and Nowakowski, Karol and Masui, Fumito}, %% for English citations
booktitle={言語処理学会第28回年次大会(NLP2022) (予定)},
% booktitle={Proceedings of The 28th Annual Meeting of The Association for Natural Language Processing (NLP2022)}, %% for English citations
pages={1--4},
year={2022}
}
```
The model was build using sentences from YACIS corpus, which should be cited using at least one of the following refrences.
```
@inproceedings{ptaszynski2012yacis,
title={YACIS: A five-billion-word corpus of Japanese blogs fully annotated with syntactic and affective information},
author={Ptaszynski, Michal and Dybala, Pawel and Rzepka, Rafal and Araki, Kenji and Momouchi, Yoshio},
booktitle={Proceedings of the AISB/IACAP world congress},
pages={40--49},
year={2012},
howpublished = "\url{https://github.com/ptaszynski/yacis-corpus}"
}
```
```
@article{ptaszynski2014automatically,
title={Automatically annotating a five-billion-word corpus of Japanese blogs for sentiment and affect analysis},
author={Ptaszynski, Michal and Rzepka, Rafal and Araki, Kenji and Momouchi, Yoshio},
journal={Computer Speech \& Language},
volume={28},
number={1},
pages={38--55},
year={2014},
publisher={Elsevier},
howpublished = "\url{https://github.com/ptaszynski/yacis-corpus}"
}
```
|
flboehm/youtube-bert
|
flboehm
| 2022-01-12T21:29:46Z | 10 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: youtube-bert
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. -->
# youtube-bert
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: 2.4771
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.691 | 1.0 | 1077 | 2.5445 |
| 2.5768 | 2.0 | 2154 | 2.5226 |
| 2.5227 | 3.0 | 3231 | 2.5027 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ju-bezdek/slovakbert-conll2003-sk-ner
|
ju-bezdek
| 2022-01-12T20:37:34Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"generated_from_trainer",
"dataset:ju-bezdek/conll2003-SK-NER",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- ju-bezdek/conll2003-SK-NER
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: outputs
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ju-bezdek/conll2003-SK-NER
type: ju-bezdek/conll2003-SK-NER
args: conll2003-SK-NER
metrics:
- name: Precision
type: precision
value: 0.8189727994593682
- name: Recall
type: recall
value: 0.8389581169955002
- name: F1
type: f1
value: 0.8288450029922203
- name: Accuracy
type: accuracy
value: 0.9526157920337243
---
<!-- 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. -->
# outputs
This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset.
It achieves the following results on the evaluation (validation) set:
- Loss: 0.1752
- Precision: 0.8190
- Recall: 0.8390
- F1: 0.8288
- Accuracy: 0.9526
## Model description
More information needed
## Code example
```python:
from transformers import pipeline, AutoModel, AutoTokenizer
from spacy import displacy
import os
model_path="ju-bezdek/slovakbert-conll2003-sk-ner"
aggregation_strategy="max"
ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy)
input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax"
ner_ents = ner_pipeline(input_sentence)
print(ner_ents)
ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0]
options = {"ents":ent_group_labels}
dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents]
displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True)
```
### Result:
<div>
<span class="tex2jax_ignore"><div class="entities" style="line-height: 2.5; direction: ltr">
<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Ruský
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">MISC</span>
</mark>
premiér
<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Viktor Černomyrdin
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
</mark>
v piatok povedal, že prezident
<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Boris Jeľcin,
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
</mark>
, ktorý je na dovolenke mimo
<mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Moskvy
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span>
</mark>
, podporil mierový plán šéfa bezpečnosti
<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Alexandra Lebedu
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
</mark>
pre
<mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Čečensko,
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span>
</mark>
uviedla tlačová agentúra
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Interfax
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORG</span>
</mark>
</div></span>
</div>
## 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 |
| 0.1663 | 2.0 | 1756 | 0.2370 | 0.7775 | 0.8090 | 0.7929 | 0.9394 |
| 0.1251 | 3.0 | 2634 | 0.2289 | 0.7732 | 0.8029 | 0.7878 | 0.9385 |
| 0.0984 | 4.0 | 3512 | 0.2818 | 0.7294 | 0.8189 | 0.7715 | 0.9294 |
| 0.0808 | 5.0 | 4390 | 0.3138 | 0.7615 | 0.7900 | 0.7755 | 0.9326 |
| 0.0578 | 6.0 | 5268 | 0.3072 | 0.7548 | 0.8222 | 0.7871 | 0.9370 |
| 0.0481 | 7.0 | 6146 | 0.2778 | 0.7897 | 0.8156 | 0.8025 | 0.9408 |
| 0.0414 | 8.0 | 7024 | 0.3336 | 0.7695 | 0.8201 | 0.7940 | 0.9389 |
| 0.0268 | 9.0 | 7902 | 0.3294 | 0.7868 | 0.8140 | 0.8002 | 0.9409 |
| 0.0204 | 10.0 | 8780 | 0.3693 | 0.7657 | 0.8239 | 0.7938 | 0.9376 |
| 0.016 | 11.0 | 9658 | 0.3816 | 0.7932 | 0.8242 | 0.8084 | 0.9425 |
| 0.0108 | 12.0 | 10536 | 0.3607 | 0.7929 | 0.8256 | 0.8089 | 0.9431 |
| 0.0078 | 13.0 | 11414 | 0.3980 | 0.7915 | 0.8240 | 0.8074 | 0.9423 |
| 0.0062 | 14.0 | 12292 | 0.4096 | 0.7995 | 0.8247 | 0.8119 | 0.9436 |
| 0.0035 | 15.0 | 13170 | 0.4177 | 0.8006 | 0.8251 | 0.8127 | 0.9438 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
hogger32/xlmRoberta-for-VietnameseQA
|
hogger32
| 2022-01-12T14:43:00Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: xlmRoberta-for-VietnameseQA
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. -->
# xlmRoberta-for-VietnameseQA
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8315
## Model description
Fine-tuned by Honganh Nguyen (FPTU AI Club).
## Intended uses & limitations
More information needed
## Training and evaluation data
Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.
Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json
Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5701 | 1.0 | 2534 | 1.2220 |
| 1.2942 | 2.0 | 5068 | 0.9698 |
| 1.0693 | 3.0 | 7602 | 0.8315 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
uw-madison/yoso-4096
|
uw-madison
| 2022-01-12T13:36:04Z | 1,918 | 0 |
transformers
|
[
"transformers",
"pytorch",
"yoso",
"fill-mask",
"arxiv:2111.09714",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# YOSO
YOSO model for masked language modeling (MLM) for sequence length 4096.
## About YOSO
The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
The abstract from the paper is the following:
Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at this https URL
## Usage
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='uw-madison/yoso-4096')
>>> unmasker("Paris is the [MASK] of France.")
[{'score': 0.024274500086903572,
'token': 812,
'token_str': ' capital',
'sequence': 'Paris is the capital of France.'},
{'score': 0.022863076999783516,
'token': 3497,
'token_str': ' Republic',
'sequence': 'Paris is the Republic of France.'},
{'score': 0.01383623294532299,
'token': 1515,
'token_str': ' French',
'sequence': 'Paris is the French of France.'},
{'score': 0.013550693169236183,
'token': 2201,
'token_str': ' Paris',
'sequence': 'Paris is the Paris of France.'},
{'score': 0.011591030284762383,
'token': 270,
'token_str': ' President',
'sequence': 'Paris is the President of France.'}]
```
|
mahaamami/distilroberta-base-finetuned-wikitext2
|
mahaamami
| 2022-01-12T13:25:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8833
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1026 | 1.0 | 5835 | 1.9705 |
| 2.0088 | 2.0 | 11670 | 1.9090 |
| 1.9766 | 3.0 | 17505 | 1.8833 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ibraheemmoosa/xlmindic-base-uniscript-soham
|
ibraheemmoosa
| 2022-01-12T12:28:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"albert",
"text-classification",
"multilingual",
"xlmindic",
"nlp",
"indoaryan",
"indicnlp",
"iso15919",
"transliteration",
"as",
"bn",
"gu",
"hi",
"mr",
"ne",
"or",
"pa",
"si",
"sa",
"bpy",
"mai",
"bh",
"gom",
"dataset:oscar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- as
- bn
- gu
- hi
- mr
- ne
- or
- pa
- si
- sa
- bpy
- mai
- bh
- gom
license: apache-2.0
datasets:
- oscar
tags:
- multilingual
- albert
- xlmindic
- nlp
- indoaryan
- indicnlp
- iso15919
- transliteration
- text-classification
widget:
- text : 'cīnēra madhyāñcalē āraō ēkaṭi śaharēra bāsindārā ābāra gharabandī haẏē paṛēchēna. āja maṅgalabāra natuna karē lakaḍāuna–saṁkrānta bidhiniṣēdha jāri haōẏāra para gharē āṭakā paṛēchēna tām̐rā. karōnāra ati saṁkrāmaka natuna dharana amikranēra bistāra ṭhēkātē ēmana padakṣēpa niẏēchē kartr̥pakṣa. khabara bārtā saṁsthā ēēphapira.'
co2_eq_emissions:
emissions: "0.21 in grams of CO2"
source: "calculated using this webstie https://mlco2.github.io/impact/#compute"
training_type: "fine-tuning"
geographical_location: "NA"
hardware_used: "P100 for about 1.5 hours"
---
# XLMIndic Base Uniscript
This model is finetuned from [this model](https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript) on Soham Bangla News Classification task which is part of the IndicGLUE benchmark. **Before pretraining this model we transliterate the text to [ISO-15919](https://en.wikipedia.org/wiki/ISO_15919) format using the [Aksharamukha](https://pypi.org/project/aksharamukha/)
library.** A demo of Aksharamukha library is hosted [here](https://aksharamukha.appspot.com/converter)
where you can transliterate your text and use it on our model on the inference widget.
## Model description
This model has the same configuration as the [ALBERT Base v2 model](https://huggingface.co/albert-base-v2/). Specifically, this model has the following configuration:
- 12 repeating layers
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 11M parameters
- 512 sequence length
## Training data
This model was fine-tuned on Soham dataset that is part of the IndicGLUE benchmark.
## Transliteration
*The unique component of this model is that it takes in ISO-15919 transliterated text.*
The motivation behind this is this. When two languages share vocabularies, a machine learning model can exploit that to learn good cross-lingual representations. However if these two languages use different writing scripts it is difficult for a model to make the connection. Thus if if we can write the two languages in a single script then it is easier for the model to learn good cross-lingual representation.
For many of the scripts currently in use, there are standard transliteration schemes to convert to the Latin script. In particular, for the Indic scripts the ISO-15919 transliteration scheme is designed to consistently transliterate texts written in different Indic scripts to the Latin script.
An example of ISO-15919 transliteration for a piece of **Bangla** text is the following:
**Original:** "রবীন্দ্রনাথ ঠাকুর এফআরএএস (৭ মে ১৮৬১ - ৭ আগস্ট ১৯৪১; ২৫ বৈশাখ ১২৬৮ - ২২ শ্রাবণ ১৩৪৮ বঙ্গাব্দ) ছিলেন অগ্রণী বাঙালি কবি, ঔপন্যাসিক, সংগীতস্রষ্টা, নাট্যকার, চিত্রকর, ছোটগল্পকার, প্রাবন্ধিক, অভিনেতা, কণ্ঠশিল্পী ও দার্শনিক।"
**Transliterated:** 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kabi, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika.'
Another example for a piece of **Hindi** text is the following:
**Original:** "चूंकि मानव परिवार के सभी सदस्यों के जन्मजात गौरव और समान तथा अविच्छिन्न अधिकार की स्वीकृति ही विश्व-शान्ति, न्याय और स्वतन्त्रता की बुनियाद है"
**Transliterated:** "cūṁki mānava parivāra kē sabhī sadasyōṁ kē janmajāta gaurava aura samāna tathā avicchinna adhikāra kī svīkr̥ti hī viśva-śānti, nyāya aura svatantratā kī buniyāda hai"
## Training procedure
### Preprocessing
The texts are transliterated to ISO-15919 format using the Aksharamukha library. Then these are tokenized using SentencePiece and a vocabulary size of 50,000.
### Training
The model was trained for 8 epochs with a batch size of 16 and a learning rate of *2e-5*.
## Evaluation results
See results specific to Soham in the following table.
### IndicGLUE
Task | mBERT | XLM-R | IndicBERT-Base | XLMIndic-Base-Uniscript (This Model) | XLMIndic-Base-Multiscript (Ablation Model)
-----| ----- | ----- | ------ | ------- | --------
Wikipedia Section Title Prediction | 71.90 | 65.45 | 69.40 | **81.78 ± 0.60** | 77.17 ± 0.76
Article Genre Classification | 88.64 | 96.61 | 97.72 | **98.70 ± 0.29** | 98.30 ± 0.26
Named Entity Recognition (F1-score) | 71.29 | 62.18 | 56.69 | **89.85 ± 1.14** | 83.19 ± 1.58
BBC Hindi News Article Classification | 60.55 | 75.52 | 74.60 | **79.14 ± 0.60** | 77.28 ± 1.50
Soham Bangla News Article Classification | 80.23 | 87.6 | 78.45 | **93.89 ± 0.48** | 93.22 ± 0.49
INLTK Gujarati Headlines Genre Classification | - | - | **92.91** | 90.73 ± 0.75 | 90.41 ± 0.69
INLTK Marathi Headlines Genre Classification | - | - | **94.30** | 92.04 ± 0.47 | 92.21 ± 0.23
IITP Hindi Product Reviews Sentiment Classification | 74.57 | **78.97** | 71.32 | 77.18 ± 0.77 | 76.33 ± 0.84
IITP Hindi Movie Reviews Sentiment Classification | 56.77 | 61.61 | 59.03 | **66.34 ± 0.16** | 65.91 ± 2.20
MIDAS Hindi Discourse Type Classification | 71.20 | **79.94** | 78.44 | 78.54 ± 0.91 | 78.39 ± 0.33
Cloze Style Question Answering (Fill-mask task) | - | - | 37.16 | **41.54** | 38.21
## Intended uses & limitations
This model is pretrained on Indo-Aryan languages. Thus it is intended to be used for downstream tasks on these languages. However, since Dravidian languages such as Malayalam, Telegu, Kannada etc share a lot of vocabulary with the Indo-Aryan languages, this model can potentially be used on those languages too (after transliterating the text to ISO-15919).
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=xlmindic) 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
To use this model you will need to first install the [Aksharamukha](https://pypi.org/project/aksharamukha/) library.
```bash
pip install aksharamukha
```
Using this library you can transliterate any text wriiten in Indic scripts in the following way:
```python
>>> from aksharamukha import transliterate
>>> text = "चूंकि मानव परिवार के सभी सदस्यों के जन्मजात गौरव और समान तथा अविच्छिन्न अधिकार की स्वीकृति ही विश्व-शान्ति, न्याय और स्वतन्त्रता की बुनियाद है"
>>> transliterated_text = transliterate.process('autodetect', 'ISO', text)
>>> transliterated_text
"cūṁki mānava parivāra kē sabhī sadasyōṁ kē janmajāta gaurava aura samāna tathā avicchinna adhikāra kī svīkr̥ti hī viśva-śānti, nyāya aura svatantratā kī buniyāda hai"
```
Then you can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> from aksharamukha import transliterate
>>> unmasker = pipeline('fill-mask', model='ibraheemmoosa/xlmindic-base-uniscript')
>>> text = "রবীন্দ্রনাথ ঠাকুর এফআরএএস (৭ মে ১৮৬১ - ৭ আগস্ট ১৯৪১; ২৫ বৈশাখ ১২৬৮ - ২২ শ্রাবণ ১৩৪৮ বঙ্গাব্দ) ছিলেন অগ্রণী বাঙালি [MASK], ঔপন্যাসিক, সংগীতস্রষ্টা, নাট্যকার, চিত্রকর, ছোটগল্পকার, প্রাবন্ধিক, অভিনেতা, কণ্ঠশিল্পী ও দার্শনিক। ১৯১৩ সালে গীতাঞ্জলি কাব্যগ্রন্থের ইংরেজি অনুবাদের জন্য তিনি এশীয়দের মধ্যে সাহিত্যে প্রথম নোবেল পুরস্কার লাভ করেন।"
>>> transliterated_text = transliterate.process('Bengali', 'ISO', text)
>>> transliterated_text
'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli [MASK], aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama [MASK] puraskāra lābha karēna.'
>>> unmasker(transliterated_text)
[{'score': 0.39705055952072144,
'token': 1500,
'token_str': 'abhinētā',
'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli abhinētā, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'},
{'score': 0.20499080419540405,
'token': 3585,
'token_str': 'kabi',
'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kabi, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'},
{'score': 0.1314290314912796,
'token': 15402,
'token_str': 'rājanētā',
'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli rājanētā, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'},
{'score': 0.060830358415842056,
'token': 3212,
'token_str': 'kalākāra',
'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kalākāra, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'},
{'score': 0.035522934049367905,
'token': 11586,
'token_str': 'sāhityakāra',
'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli sāhityakāra, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}]
```
### Limitations and bias
Even though we pretrain on a comparatively large multilingual corpus the model may exhibit harmful gender, ethnic and political bias. If you fine-tune this model on a task where these issues are important you should take special care when relying on the model to make decisions.
## Contact
Feel free to contact us if you have any ideas or if you want to know more about our models.
- Ibraheem Muhammad Moosa ([email protected])
- Mahmud Elahi Akhter ([email protected])
- Ashfia Binte Habib
## BibTeX entry and citation info
Coming soon!
|
huggingtweets/prof_preobr
|
huggingtweets
| 2022-01-12T10:06:59Z | 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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/853613144832446464/VrGXs0NZ_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">Проф. Преображенский</div>
<div style="text-align: center; font-size: 14px;">@prof_preobr</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 Проф. Преображенский.
| Data | Проф. Преображенский |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 567 |
| Short tweets | 61 |
| Tweets kept | 2596 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12xdr90k/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 @prof_preobr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vqtap5s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vqtap5s/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/prof_preobr')
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)
|
gpssohi/distilbart-qgen-3-3
|
gpssohi
| 2022-01-12T08:29:26Z | 14 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question-generation",
"summarization",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- question-generation
- summarization
license: apache-2.0
datasets:
- squad
---
# Introduction
This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. [GitHub Link for training scripts.](https://github.com/darth-c0d3r/bart-question-generation)
# Usage
The input format is as follows: `[answer] <s> [passage]`. The model will predict the question that corresponds to the answer from the passage.
# Plot
# Dataset
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
## [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. We use the SQuAD 1.1 variant which does not have unanswerable questions. So, every question will have a corresponding answer and vice-versa.
### Preprocessing
The first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
### Stats
**Original Dataset**
| Split | Num Docs | Num Contexts | Ques w/ Ans | Ques w/o Ans | Num Unique Ans |
| ----- | -------- | ------------ | ----------- | ------------ | -------------- |
| Train | 442 | 19035 | 86821 | 43498 | 86821 |
| Dev | 35 | 1204 | 5928 | 5945 | 10279 |
**After Preprocessing**
| Split | Num Rows | Context | Answer | Question |
| ----- | -------- | ---------- | ------ | -------- |
| Train | 80995 | 653,120,20 | 43,3,1 | 40,10,1 |
| Eval | 5826 | 445,123,67 | 28,3,1 | 29,10,3 |
| Test | 10297 | 629,129,25 | 29,4,1 | 31,10,3 |
The numbers in the columns indicate max, avg, min number of words.
|
anirudh21/distilbert-base-uncased-finetuned-cola
|
anirudh21
| 2022-01-12T07:24:56Z | 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
| 2022-03-02T23:29:05Z |
---
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: 0.5224154837835395
---
<!-- 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: 0.8623
- Matthews Correlation: 0.5224
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5278 | 1.0 | 535 | 0.5223 | 0.4007 |
| 0.3515 | 2.0 | 1070 | 0.5150 | 0.4993 |
| 0.2391 | 3.0 | 1605 | 0.6471 | 0.5103 |
| 0.1841 | 4.0 | 2140 | 0.7640 | 0.5153 |
| 0.1312 | 5.0 | 2675 | 0.8623 | 0.5224 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Jinhwan/krelectra-base-mecab
|
Jinhwan
| 2022-01-12T03:18:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language: ko
license: apache-2.0
tags:
- korean
---
# KrELECTRA-base-mecab
Korean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)
## Usage
### Load model and tokenizer
```python
>>> from transformers import AutoTokenizer, AutoModelForPreTraining
>>> model = AutoModelForPreTraining.from_pretrained("Jinhwan/krelectra-base-mecab")
>>> tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab")
```
### Tokenizer example
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]'])
[2, 7214, 24023, 24663, 26580, 3195, 7086, 3746, 5500, 17, 3]
|
habiba/egy-slang-model
|
habiba
| 2022-01-12T01:27:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: egy-slang-model
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. -->
# egy-slang-model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9273
- Wer: 1.0000
## 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: 0.001
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.64 | 200 | 2.9735 | 1.0 |
| 3.8098 | 3.28 | 400 | 2.9765 | 1.0 |
| 3.8098 | 4.91 | 600 | 2.9662 | 1.0 |
| 2.9531 | 6.56 | 800 | 2.9708 | 1.0 |
| 2.9531 | 8.2 | 1000 | 2.9673 | 1.0 |
| 2.9259 | 9.83 | 1200 | 2.9989 | 1.0 |
| 2.9259 | 11.47 | 1400 | 2.9889 | 1.0 |
| 2.9023 | 13.11 | 1600 | 2.9739 | 1.0 |
| 2.9023 | 14.75 | 1800 | 3.0040 | 1.0000 |
| 2.8832 | 16.39 | 2000 | 3.0170 | 1.0 |
| 2.8832 | 18.03 | 2200 | 2.9963 | 0.9999 |
| 2.8691 | 19.67 | 2400 | 2.9273 | 1.0000 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ThePixOne/retBERT
|
ThePixOne
| 2022-01-11T18:24:24Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
BERT finetuned on wallstreetbets subreddit
|
avichr/heBERT_NER
|
avichr
| 2022-01-11T17:00:46Z | 4,122 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"arxiv:1810.04805",
"arxiv:2102.01909",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HeBERT is a Hebrew pretrained language model. It is based on [Google's BERT](https://arxiv.org/abs/1810.04805) architecture and it is BERT-Base config. <br>
HeBert was trained on three dataset:
1. A Hebrew version of [OSCAR](https://oscar-corpus.com/): ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences.
2. A Hebrew dump of [Wikipedia](https://dumps.wikimedia.org/): ~650 MB of data, including over 63 millions words and 3.8 millions sentences
3. Emotion User Generated Content (UGC) data that was collected for the purpose of this study (described below).
## Named-entity recognition (NER)
The ability of the model to classify named entities in text, such as persons' names, organizations, and locations; tested on a labeled dataset from [Ben Mordecai and M Elhadad (2005)](https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/), and evaluated with F1-score.
### How to use
```
from transformers import pipeline
# how to use?
NER = pipeline(
"token-classification",
model="avichr/heBERT_NER",
tokenizer="avichr/heBERT_NER",
)
NER('דויד לומד באוניברסיטה העברית שבירושלים')
```
## Other tasks
[**Emotion Recognition Model**](https://huggingface.co/avichr/hebEMO_trust).
An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing)
<br>
[**Sentiment Analysis**](https://huggingface.co/avichr/heBERT_sentiment_analysis).
<br>
[**masked-LM model**](https://huggingface.co/avichr/heBERT) (can be fine-tunned to any down-stream task).
## Contact us
[Avichay Chriqui](mailto:[email protected]) <br>
[Inbal yahav](mailto:[email protected]) <br>
The Coller Semitic Languages AI Lab <br>
Thank you, תודה, شكرا <br>
## If you used this model please cite us as :
Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909.
```
@article{chriqui2021hebert,
title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition},
author={Chriqui, Avihay and Yahav, Inbal},
journal={arXiv preprint arXiv:2102.01909},
year={2021}
}
```
[git](https://github.com/avichaychriqui/HeBERT)
|
rbhushan/distilgpt2-finetuned-wikitext2
|
rbhushan
| 2022-01-11T16:55:00Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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: 5.2872
## 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 | 73 | 5.4169 |
| No log | 2.0 | 146 | 5.3145 |
| No log | 3.0 | 219 | 5.2872 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
alaggung/bart-r3f
|
alaggung
| 2022-01-11T16:18:32Z | 123 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"summarization",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language:
- ko
tags:
- summarization
widget:
- text: "[BOS]밥 ㄱ?[SEP]고고고고 뭐 먹을까?[SEP]어제 김치찌개 먹어서 한식말고 딴 거[SEP]그럼 돈까스 어때?[SEP]오 좋다 1시 학관 앞으로 오셈[SEP]ㅇㅋ[EOS]"
inference:
parameters:
max_length: 64
top_k: 5
---
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[bart-pretrained](https://huggingface.co/alaggung/bart-pretrained) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약](https://aihub.or.kr/aidata/30714) 데이터를 사용하였습니다.
|
alaggung/bart-pretrained
|
alaggung
| 2022-01-11T16:07:39Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- ko
widget:
- text: "[BOS]뭐 해?[SEP][MASK]하다가 이제 [MASK]려고[EOS]"
inference:
parameters:
max_length: 64
---
# BART Pretrained
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 BART Pretrain 단계를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약](https://aihub.or.kr/aidata/30714) 데이터를 사용하였습니다.
|
Humair/all-mpnet-base-v2-finetuned-v2
|
Humair
| 2022-01-11T12:26:56Z | 13 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Humair/all-mpnet-base-v2-finetuned-v2
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('Humair/all-mpnet-base-v2-finetuned-v2')
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('Humair/all-mpnet-base-v2-finetuned-v2')
model = AutoModel.from_pretrained('Humair/all-mpnet-base-v2-finetuned-v2')
# 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=Humair/all-mpnet-base-v2-finetuned-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"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": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: MPNetModel
(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 -->
|
flax-community/t5-base-dutch
|
flax-community
| 2022-01-11T12:10:22Z | 32 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"dataset:yhavinga/mc4_nl_cleaned",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- dutch
tags:
- seq2seq
- lm-head
datasets:
- yhavinga/mc4_nl_cleaned
license: apache-2.0
inference: false
---
# t5-base-dutch
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
& [Dat Nguyen](https://www.linkedin.com/in/dat-nguyen-49a641138/) during the [Hugging Face community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google, for the project [Pre-train T5 from scratch in Dutch](https://discuss.huggingface.co/t/pretrain-t5-from-scratch-in-dutch/8109).
See also the fine-tuned [t5-base-dutch-demo](https://huggingface.co/flax-community/t5-base-dutch-demo) model,
and the demo application **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)**,
that are based on this model.
**5 jan 2022: Model updated. Evaluation accuracy increased from 0.64 to 0.70.**
**11 jan 2022: See also [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) with eval acc 0.78**
## Model
* Configuration based on `google/t5-base`
* 12 layers, 12 heads
* Dropout set to 0.1
## Dataset
This model was trained on the `full` configuration of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Tokenization
A SentencePiece tokenizer was trained from scratch on this dataset.
The total tokens of the `full` configuration is 34B
## Training
The model was trained on the `full` mc4_nl_cleaned dataset configuration for 1 epoch, consisting of 34B tokens,
for 528 482 steps with a batch size of 128 and took 57 hours.
A triangle learning rate schedule was used, with peak learning rate 0.005.
## Evaluation
* Loss: 1.38
* Accuracy: 0.70
|
huang0624/Taiwan_House_Prediction
|
huang0624
| 2022-01-11T11:12:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
Hi, this is Taiwan_House_Prediction.
|
moumeneb1/testing
|
moumeneb1
| 2022-01-11T09:16:45Z | 5 | 0 |
speechbrain
|
[
"speechbrain",
"wav2vec2",
"CTC",
"Attention",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"rw",
"dataset:commonvoice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: "rw"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- Attention
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (Kinyarwanda Language) within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
The performance of the model is the following:
| Release | Test WER | GPUs |
|:--------------:|:--------------:| :--------:|
| 03-06-21 | 18.91 | 2xV100 32GB |
## Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions (train.tsv) of CommonVoice (RW).
- Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on CommonVoice En.
The obtained final acoustic representation is given to the CTC and attention decoders.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files (in Kinyarwanda)
```python
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-rw", savedir="pretrained_models/asr-wav2vec2-commonvoice-rw")
asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-rw/example.mp3")
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
## Parallel Inference on a Batch
Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```bash
cd recipes/CommonVoice/ASR/seq2seq
python train_with_wav2vec.py hparams/train_rw_with_wav2vec.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
kleinay/nominalization-candidate-classifier
|
kleinay
| 2022-01-11T04:12:39Z | 1,135 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"nominalizations",
"en",
"dataset:kleinay/qanom",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- pytorch
- token-classification
- nominalizations
datasets:
- kleinay/qanom
---
# Nominalization Detector
This model identifies "predicative nominalizations", that is, nominalizations that carry an eventive (or "verbal") meaning in context. It is a `bert-base-cased` pretrained model, fine-tuned for token classification on top of the "nominalization detection" task as defined and annotated by the QANom project [(Klein et. al., COLING 2020)](https://www.aclweb.org/anthology/2020.coling-main.274/).
## Task Description
The model is trained as a binary classifier, classifying candidate nominalizations.
The candidates are extracted using a POS tagger (filtering common nouns) and additionally lexical resources (e.g. WordNet and CatVar), filtering nouns that have (at least one) derivationally-related verb. In the QANom annotation project, these candidates are given to annotators to decide whether they carry a "verbal" meaning in the context of the sentence. The current model reproduces this binary classification.
## Demo
Check out our cool [demo](https://huggingface.co/spaces/kleinay/nominalization-detection-demo)!
## Usage
The candidate extraction algorithm is implemented inside the `qanom` package - see the README in the [QANom github repo](https://github.com/kleinay/QANom) for full documentation. The `qanom` package is also available via `pip install qanom`.
For ease of use, we encapsulated the full nominalization detection pipeline (i.e. candidate extraction + predicate classification) in the `qanom.nominalization_detector.NominalizationDetector` class, which internally utilize this `nominalization-candidate-classifier`:
```python
from qanom.nominalization_detector import NominalizationDetector
detector = NominalizationDetector()
raw_sentences = ["The construction of the officer 's building finished right after the beginning of the destruction of the previous construction ."]
print(detector(raw_sentences, return_all_candidates=True))
print(detector(raw_sentences, threshold=0.75, return_probability=False))
```
Outputs:
```json
[[{'predicate_idx': 1,
'predicate': 'construction',
'predicate_detector_prediction': True,
'predicate_detector_probability': 0.7626778483390808,
'verb_form': 'construct'},
{'predicate_idx': 4,
'predicate': 'officer',
'predicate_detector_prediction': False,
'predicate_detector_probability': 0.19832570850849152,
'verb_form': 'officer'},
{'predicate_idx': 6,
'predicate': 'building',
'predicate_detector_prediction': True,
'predicate_detector_probability': 0.5794129371643066,
'verb_form': 'build'},
{'predicate_idx': 11,
'predicate': 'beginning',
'predicate_detector_prediction': True,
'predicate_detector_probability': 0.8937646150588989,
'verb_form': 'begin'},
{'predicate_idx': 14,
'predicate': 'destruction',
'predicate_detector_prediction': True,
'predicate_detector_probability': 0.8501205444335938,
'verb_form': 'destruct'},
{'predicate_idx': 18,
'predicate': 'construction',
'predicate_detector_prediction': True,
'predicate_detector_probability': 0.7022264003753662,
'verb_form': 'construct'}]]
```
```json
[[{'predicate_idx': 1, 'predicate': 'construction', 'verb_form': 'construct'},
{'predicate_idx': 11, 'predicate': 'beginning', 'verb_form': 'begin'},
{'predicate_idx': 14, 'predicate': 'destruction', 'verb_form': 'destruct'}]]
```
## Cite
```latex
@inproceedings{klein2020qanom,
title={QANom: Question-Answer driven SRL for Nominalizations},
author={Klein, Ayal and Mamou, Jonathan and Pyatkin, Valentina and Stepanov, Daniela and He, Hangfeng and Roth, Dan and Zettlemoyer, Luke and Dagan, Ido},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={3069--3083},
year={2020}
}
```
|
0x7o/keyt5-base
|
0x7o
| 2022-01-11T03:52:53Z | 28 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ru",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language:
- ru
license: mit
inference:
parameters:
top_p: 0.9
widget:
- text: "В России может появиться новый штамм коронавируса «омикрон», что может привести к подъему заболеваемости в январе, заявил доцент кафедры инфекционных болезней РУДН Сергей Вознесенский. Он отметил, что вариант «дельта» вызывал больше летальных случаев, чем омикрон, именно на фоне «дельты» была максимальная летальность."
example_title: "Коронавирус"
- text: "Начальника штаба обороны Великобритании адмирала Тони Радакина заставили имитировать активность во время визита в ангар с тяжелым вооружением, сообщила британская пресса. В приказе говорилось, что военнослужащим было велено подбегать к автомобилям, открывать все люки, затворы, листать руководство по эксплуатации и осматриваться машины, будто проводится функциональный тест для обеспечения правильной работы оборудования."
example_title: "Британия"
- text: "Для воспроизведения музыки достаточно нажимать на кнопки клавиатуры. Каждой клавише соответствует определенный семпл — есть маракасы и футуристичные звуки, напоминающие выстрелы бластеров. Из всего многообразия можно формировать собственные паттерны и наблюдать за визуализацией с анимированными геометрическими фигурами. Что интересно, нажатием клавиши пробел можно полностью переменить оформление, цвета на экране и звучание семплов."
example_title: "Технологии"
---
## keyT5. Base (small) version
[](https://github.com/0x7o/text2keywords "Go to GitHub repo")
[](https://github.com/0x7o/text2keywords)
[](https://github.com/0x7o/text2keywords)
Supported languages: ru
Github - [text2keywords](https://github.com/0x7o/text2keywords)
[Pretraining Large version](https://huggingface.co/0x7194633/keyt5-large)
|
[Pretraining Base version](https://huggingface.co/0x7194633/keyt5-base)
# Usage
Example usage (the code returns a list with keywords. duplicates are possible):
[](https://colab.research.google.com/github/0x7o/text2keywords/blob/main/example/keyT5_use.ipynb)
```
pip install transformers sentencepiece
```
```python
from itertools import groupby
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
model_name = "0x7194633/keyt5-large" # or 0x7194633/keyt5-base
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
def generate(text, **kwargs):
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(**inputs, num_beams=5, **kwargs)
s = tokenizer.decode(hypotheses[0], skip_special_tokens=True)
s = s.replace('; ', ';').replace(' ;', ';').lower().split(';')[:-1]
s = [el for el, _ in groupby(s)]
return s
article = """Reuters сообщил об отмене 3,6 тыс. авиарейсов из-за «омикрона» и погоды
Наибольшее число отмен авиарейсов 2 января пришлось на американские авиакомпании
SkyWest и Southwest, у каждой — более 400 отмененных рейсов. При этом среди
отмененных 2 января авиарейсов — более 2,1 тыс. рейсов в США. Также свыше 6400
рейсов были задержаны."""
print(generate(article, top_p=1.0, max_length=64))
# ['авиаперевозки', 'отмена авиарейсов', 'отмена рейсов', 'отмена авиарейсов', 'отмена рейсов', 'отмена авиарейсов']
```
# Training
Go to the training notebook and learn more about it:
[](https://colab.research.google.com/github/0x7o/text2keywords/blob/main/example/keyT5_train.ipynb)
|
ai-forever/ruclip-vit-base-patch16-384
|
ai-forever
| 2022-01-11T02:29:57Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# ruclip-vit-base-patch16-384
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and
multimodal learning.
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text ranking`; `image ranking`; `zero-shot image classification`;
* Type: `encoder`
* Num Parameters: `150M`
* Training Data Volume: `240 million text-image pairs`
* Language: `Russian`
* Context Length: `77`
* Transformer Layers: `12`
* Transformer Width: `512`
* Transformer Heads: `8`
* Image Size: `384`
* Vision Layers: `12`
* Vision Width: `768`
* Vision Patch Size: `16`
## Usage [Github](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
clip, processor = ruclip.load("ruclip-vit-base-patch16-384", device="cuda")
```
## Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|:--------------|:---------------|:--------------------|
| Food101 | acc | 0.689 |
| CIFAR10 | acc | 0.845 |
| CIFAR100 | acc | 0.569 |
| Birdsnap | acc | 0.195 |
| SUN397 | acc | 0.521 |
| Stanford Cars | acc | 0.626 |
| DTD | acc | 0.421 |
| MNIST | acc | 0.478 |
| STL10 | acc | 0.964 |
| PCam | acc | 0.501 |
| CLEVR | acc | 0.132 |
| Rendered SST2 | acc | 0.525 |
| ImageNet | acc | 0.482 |
| FGVC Aircraft | mean-per-class | 0.046 |
| Oxford Pets | mean-per-class | 0.635 |
| Caltech101 | mean-per-class | 0.835 |
| Flowers102 | mean-per-class | 0.452 |
| HatefulMemes | roc-auc | 0.543 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
|
ai-forever/rudalle-Malevich
|
ai-forever
| 2022-01-11T02:20:10Z | 0 | 34 | null |
[
"pytorch",
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"region:us"
] |
text-to-image
| 2022-03-02T23:29:05Z |
---
language:
- ru
- en
pipeline_tag: text-to-image
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/ru-dalle"
---
# ruDALL-E Malevich (XL)
## Generate images from text
<img style="text-align:center; display:block;" src="https://huggingface.co/sberbank-ai/rudalle-Malevich/resolve/main/dalle-malevich.jpg" width="200">
"Avocado painting in the style of Malevich"
* [Technical Report (Russian)](https://habr.com/ru/company/sberbank/blog/586926)
* [Demo](https://rudalle.ru)
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text2image generation`
* Type: `encoder-decoder`
* Num Parameters: `1.3 B`
* Training Data Volume: `120 million text-image pairs`
### Model Description
This is a 1.3 billion parameter model for Russian, recreating OpenAI's [DALL·E](https://openai.com/blog/dall-e/), a model capable of generating arbitrary images from a text prompt that describes the desired result.
The generation pipeline includes ruDALL-E, ruCLIP for ranging results, and a superresolution model.
You can use automatic translation into Russian to create desired images with ruDALL-E.
### How to Use
The easiest way to get familiar with the code and the models is to follow the inference notebook we provide in our [github repo](https://github.com/sberbank-ai/ru-dalle).
## Motivation
One might say that “investigate, master, and train” is our engineering motto. Well, we caught the scent, and today we can say that we created from scratch a complete pipeline for generating images from descriptive textual input written in Russian.
Teams at SberAI, SberDevices, Samara University, AIRI and SberCloud all actively contributed.
We trained two versions of the model, each a different size, and named them after Russia’s great abstractionists: Vasily Kandinsky and Kazimir Malevich.
* ruDALL-E Kandinsky (XXL), with 12 billion parameters
* ruDALL-E Malevich (XL), having 1.3 billion parameters
Some of our models are already freely available:
* ruDALL-E Malevich (XL) [[GitHub](https://github.com/sberbank-ai/ru-dalle), [HuggingFace](https://huggingface.co/sberbank-ai/rudalle-Malevich)]
* Sber VQ-GAN [[GitHub](https://github.com/sberbank-ai/sber-vq-gan), [HuggingFace](https://huggingface.co/sberbank-ai/Sber-VQGAN)]
* ruCLIP Small [[GitHub](https://github.com/sberbank-ai/ru-clip), [HuggingFace](https://huggingface.co/sberbank-ai/ru-clip)]
* Super Resolution (Real ESRGAN) [[GitHub](https://github.com/sberbank-ai/Real-ESRGAN), [HuggingFace](https://huggingface.co/sberbank-ai/Real-ESRGAN)]
The latter two models are included in the pipeline for generating images from text (as you’ll see later on).
The models ruDALL-E Malevich (XL), ruDALL-E Kandinsky (XXL), ruCLIP Small, ruCLIP Large, and Super Resolution (Real ESRGAN) will also soon be available on [DataHub](https://mlspace.aicloud.sbercloud.ru/mlspace/datahub).
Training the ruDALL-E neural networks on the Christofari cluster has become the largest calculation task in Russia:
* ruDALL-E Kandinsky (XXL) was trained for 37 days on the 512 GPU TESLA V100, and then also for 11 more days on the 128 GPU TESLA V100, for a total of 20,352 GPU-days;
* ruDALL-E Malevich (XL) was trained for 8 days on the 128 GPU TESLA V100, and then also for 15 more days on the 192 GPU TESLA V100, for a total of 3,904 GPU-days.
Accordingly, training for both models totalled 24,256 GPU-days.
## Model capabilities
The long term goal of this research is the creation of multimodal neural networks. They will be able to pull on concepts from a variety of mediums---from text and visuals at first---in order to better understand the world as a whole.
Image generation might seem like the wrong rabbit hole in our century of big data and search engines. But it actually addresses two important requirements that search is currently unable to cope with:
1. Being able to describe in writing exactly what you’re looking for and getting a completely new image created personally for you.
2. Being able to create at any time as many license-free illustrations as you could possibly want
"Grand Canyon"
<img style="text-align:center; display:block;" src="https://habrastorage.org/webt/kb/sv/ih/kbsvihfsmz3fx5mvitii0seimi0.jpeg" width="800">
"Salvador Dali picture"
<img style="text-align:center; display:block;" src="https://habrastorage.org/webt/r8/nl/oi/r8nloiq-l8j2ckg6pzh2pufsklm.jpeg" width="800">
"An eagle sits in a tree, looking to the side"
<img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/10a/19c/fa2/10a19cfa2cc84aa7c8b99820890e908d.png" width="800">
"Elegant living room with green stuffed chairs"
<img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/6fe/e69/d7c/6fee69d7c392239d587725799e0e41e4.png" width="800">
“Raccoon with a gun”
<img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/3bb/1b8/7c4/3bb1b87c45bf9305cd342ae9900ac245.png" width="800">
“Pretty lake at sunset”
<img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/241/781/fe9/241781fe99da510d4d5fea03af635e88.png" width="800">
|
tscholak/2jrayxos
|
tscholak
| 2022-01-10T21:50:53Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:cosql",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "And the concert named Auditions? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : sing er_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name ( Super bootcamp, Auditions ), theme, stadium_id, year | singer_in_concert : concert_id, singer_id || Which year did the concert Super bootcamp happen in? | Find the name and location of the stadiums which some concerts happened in the years of both 2014 and 2015."
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name, theme, stadium_id, year | singer_in_concert : concert_id, singer_id"
license: "apache-2.0"
datasets:
- cosql
- spider
metrics:
- cosql
---
## tscholak/2jrayxos
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.large](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k).
### Training Data
The model has been fine-tuned on the 2,164 training dialogues in the [CoSQL SQL-grounded dialogue state tracking dataset](https://yale-lily.github.io/cosql) and the 7,000 training examples in the [Spider text-to-SQL dataset](https://yale-lily.github.io/spider). The model solves both, CoSQL's zero-shot text-to-SQL dialogue state tracking task and Spider's zero-shot text-to-SQL translation task. Zero-shot means that the model can generalize to unseen SQL databases.
### Training Objective
This model was initialized with [t5.1.1.lm100k.large](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) and fine-tuned with the text-to-text generation objective.
A question is always grounded in both, a database schema and the preceiding questions in the dialogue. The model is trained to predict the SQL query that would be used to answer the user's current natural language question. The input to the model is composed of the user's current question, the database identifier, a list of tables and their columns, and a sequence of previous questions in reverse chronological order.
```
[current question] | [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ... || [previous question] | ... | [first question]
```
The sequence of previous questions is separated by `||` from the linearized schema. In the absence of previous questions (for example, for the first question in a dialogue or for Spider questions), this separator is omitted.
The model outputs the database identifier and the SQL query that will be executed on the database to answer the user's current question in the dialog.
```
[db_id] | [sql]
```
### Performance
Out of the box, this model achieves 52.5 % question match accuracy on the CoSQL development set.
Using the PICARD constrained decoding method (see [the official PICARD implementation](https://github.com/ElementAI/picard)), the model's performance can be improved to **54.2 %** question match accuracy on the CoSQL development set.
### Usage
Please see [the official repository](https://github.com/ElementAI/picard) for scripts and docker images that support evaluation and serving of this model.
### References
1. [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093)
2. [Official PICARD code](https://github.com/ElementAI/picard)
### Citation
```bibtex
@inproceedings{Scholak2021:PICARD,
author = {Torsten Scholak and Nathan Schucher and Dzmitry Bahdanau},
title = "{PICARD}: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.779",
pages = "9895--9901",
}
```
|
tscholak/2e826ioa
|
tscholak
| 2022-01-10T21:50:39Z | 9 | 7 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:cosql",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "And the concert named Auditions? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : sing er_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name ( Super bootcamp, Auditions ), theme, stadium_id, year | singer_in_concert : concert_id, singer_id || Which year did the concert Super bootcamp happen in? | Find the name and location of the stadiums which some concerts happened in the years of both 2014 and 2015."
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name, theme, stadium_id, year | singer_in_concert : concert_id, singer_id"
license: "apache-2.0"
datasets:
- cosql
- spider
metrics:
- cosql
---
## tscholak/2e826ioa
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-3B](https://huggingface.co/t5-3b).
### Training Data
The model has been fine-tuned on the 2,164 training dialogues in the [CoSQL SQL-grounded dialogue state tracking dataset](https://yale-lily.github.io/cosql) and the 7,000 training examples in the [Spider text-to-SQL dataset](https://yale-lily.github.io/spider). The model solves both, CoSQL's zero-shot text-to-SQL dialogue state tracking task and Spider's zero-shot text-to-SQL translation task. Zero-shot means that the model can generalize to unseen SQL databases.
### Training Objective
This model was initialized with [T5-3B](https://huggingface.co/t5-3b) and fine-tuned with the text-to-text generation objective.
A question is always grounded in both, a database schema and the preceiding questions in the dialogue. The model is trained to predict the SQL query that would be used to answer the user's current natural language question. The input to the model is composed of the user's current question, the database identifier, a list of tables and their columns, and a sequence of previous questions in reverse chronological order.
```
[current question] | [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ... || [previous question] | ... | [first question]
```
The sequence of previous questions is separated by `||` from the linearized schema. In the absence of previous questions (for example, for the first question in a dialogue or for Spider questions), this separator is omitted.
The model outputs the database identifier and the SQL query that will be executed on the database to answer the user's current question in the dialog.
```
[db_id] | [sql]
```
### Performance
Out of the box, this model achieves 53.8 % question match accuracy and 21.8 % interaction match accuracy on the CoSQL development set. On the CoSQL test set, the model achieves 51.4 % question match accuracy and 21.7 % interaction match accuracy.
Using the PICARD constrained decoding method (see [the official PICARD implementation](https://github.com/ElementAI/picard)), the model's performance can be improved to **56.9 %** question match accuracy and **24.2 %** interaction match accuracy on the CoSQL development set. On the CoSQL test set and with PICARD, the model achieves **54.6 %** question match accuracy and **23.7 %** interaction match accuracy.
### Usage
Please see [the official repository](https://github.com/ElementAI/picard) for scripts and docker images that support evaluation and serving of this model.
### References
1. [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093)
2. [Official PICARD code](https://github.com/ElementAI/picard)
### Citation
```bibtex
@inproceedings{Scholak2021:PICARD,
author = {Torsten Scholak and Nathan Schucher and Dzmitry Bahdanau},
title = "{PICARD}: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.779",
pages = "9895--9901",
}
```
|
tscholak/cxmefzzi
|
tscholak
| 2022-01-10T21:49:50Z | 675 | 30 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name, theme, stadium_id, year | singer_in_concert : concert_id, singer_id"
license: "apache-2.0"
datasets:
- spider
metrics:
- spider
---
## tscholak/cxmefzzi
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-3B](https://huggingface.co/t5-3b).
### Training Data
The model has been fine-tuned on the 7000 training examples in the [Spider text-to-SQL dataset](https://yale-lily.github.io/spider). The model solves Spider's zero-shot text-to-SQL translation task, and that means that it can generalize to unseen SQL databases.
### Training Objective
This model was initialized with [T5-3B](https://huggingface.co/t5-3b) and fine-tuned with the text-to-text generation objective.
Questions are always grounded in a database schema, and the model is trained to predict the SQL query that would be used to answer the question. The input to the model is composed of the user's natural language question, the database identifier, and a list of tables and their columns:
```
[question] | [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ...
```
The model outputs the database identifier and the SQL query that will be executed on the database to answer the user's question:
```
[db_id] | [sql]
```
### Performance
Out of the box, this model achieves 71.5 % exact-set match accuracy and 74.4 % execution accuracy on the Spider development set. On the test set, the model achieves 68.0 % exact-set match accuracy and 70.1 % execution accuracy.
Using the PICARD constrained decoding method (see [the official PICARD implementation](https://github.com/ElementAI/picard)), the model's performance can be improved to **75.5 %** exact-set match accuracy and **79.3 %** execution accuracy on the Spider development set. On the test set and with PICARD, the model achieves **71.9 %** exact-set match accuracy and **75.1 %** execution accuracy.
### Usage
Please see [the official repository](https://github.com/ElementAI/picard) for scripts and docker images that support evaluation and serving of this model.
### References
1. [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093)
2. [Official PICARD code](https://github.com/ElementAI/picard)
### Citation
```bibtex
@inproceedings{Scholak2021:PICARD,
author = {Torsten Scholak and Nathan Schucher and Dzmitry Bahdanau},
title = "{PICARD}: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.779",
pages = "9895--9901",
}
```
|
repro-rights-amicus-briefs/bert-base-uncased-finetuned-RRamicus
|
repro-rights-amicus-briefs
| 2022-01-10T21:19:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: reprorights-amicus-bert
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. -->
# reprorights-amicus-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5428
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7763 | 1.0 | 1479 | 1.6789 |
| 1.76 | 2.0 | 2958 | 1.6199 |
| 1.6881 | 3.0 | 4437 | 1.5683 |
| 1.6424 | 4.0 | 5916 | 1.5432 |
| 1.6131 | 5.0 | 7395 | 1.5269 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
SaulLu/markuplm-base
|
SaulLu
| 2022-01-10T19:17:34Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"markuplm",
"arxiv:2110.08518",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
# MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei
|
fhamborg/roberta-targeted-sentiment-classification-newsarticles
|
fhamborg
| 2022-01-10T16:16:01Z | 15 | 15 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment-analysis",
"sentiment-classification",
"targeted-sentiment-classification",
"target-depentent-sentiment-classification",
"en",
"dataset:fhamborg/news_sentiment_newsmtsc",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- text-classification
- sentiment-analysis
- sentiment-classification
- targeted-sentiment-classification
- target-depentent-sentiment-classification
license: "apache-2.0"
datasets: "fhamborg/news_sentiment_newsmtsc"
---
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: [use our PyPI package](https://pypi.org/project/NewsSentiment/) instead of this model on the Hub
The Huggingface Hub architecture currently [does not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use [PyPI package NewsSentiment](https://pypi.org/project/NewsSentiment/).
## Description
This model is the currently [best performing](https://aclanthology.org/2021.eacl-main.142.pdf)
targeted sentiment classifier for news articles. In contrast to regular sentiment
classification, targeted sentiment classification allows you to provide a target in a sentence.
Only for this target, the sentiment is then predicted. This is more reliable in many
cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."
This model is also available as an easy-to-use PyPI package named [`NewsSentiment`](https://pypi.org/project/NewsSentiment/) and
in its original GitHub repository named [`NewsMTSC`](https://github.com/fhamborg/NewsMTSC), where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each
labeled and agreed upon by at least 5 annotators) can be found in our [EACL paper](https://aclanthology.org/2021.eacl-main.142.pdf). The
dataset, the model, and its source code can be viewed in our [GitHub repository](https://github.com/fhamborg/NewsMTSC).
We recommend to use our [PyPI package](https://pypi.org/project/NewsSentiment/) for sentiment classification since the Huggingface Hub platform seems to [not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification.
# How to cite
If you use the dataset or model, please cite our [paper](https://www.aclweb.org/anthology/2021.eacl-main.142/) ([PDF](https://www.aclweb.org/anthology/2021.eacl-main.142.pdf)):
```
@InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
}
```
|
ibm-research/tslm-discourse-markers
|
ibm-research
| 2022-01-10T14:42:41Z | 0 | 0 | null |
[
"arxiv:2201.02026",
"region:us"
] | null | 2022-03-02T23:29:05Z |
SenDM model described at https://arxiv.org/pdf/2201.02026
---
language:
- en
tags:
- discourse-markers
license: apache-2.0
---
|
khanglam7012/t5-small
|
khanglam7012
| 2022-01-10T13:32:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: Keywords to Sentences
tags:
- keytotext
- k2t
- Keywords to Sentences
license: mit
datasets:
- WebNLG
- Dart
metrics:
- NLG
---
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
|
flboehm/youtube-bert_10
|
flboehm
| 2022-01-10T11:39:26Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: youtube-bert_10
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. -->
# youtube-bert_10
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: 2.4456
- Perplexity: 11.54
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6799 | 1.0 | 1899 | 2.5135 |
| 2.5736 | 2.0 | 3798 | 2.4612 |
| 2.5172 | 3.0 | 5697 | 2.4363 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/dril-hostagekiller-suicidepussy
|
huggingtweets
| 2022-01-10T10:25:29Z | 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: http://www.huggingtweets.com/dril-hostagekiller-suicidepussy/1641810324627/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/1473236995497500675/FtwXDZld_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/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/1322637724470358022/ccOsLDPE_400x400.jpg')">
</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">HUSSY2K. & wint & I have 400 diseases</div>
<div style="text-align: center; font-size: 14px;">@dril-hostagekiller-suicidepussy</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 HUSSY2K. & wint & I have 400 diseases.
| Data | HUSSY2K. | wint | I have 400 diseases |
| --- | --- | --- | --- |
| Tweets downloaded | 3186 | 3226 | 3237 |
| Retweets | 819 | 480 | 121 |
| Short tweets | 395 | 304 | 1125 |
| Tweets kept | 1972 | 2442 | 1991 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bqo2ddu/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-hostagekiller-suicidepussy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/o4ya0wuw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/o4ya0wuw/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-hostagekiller-suicidepussy')
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)
|
doc2query/msmarco-t5-base-v1
|
doc2query
| 2022-01-10T10:22:10Z | 1,411 | 5 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/embedding-training-data",
"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:
- sentence-transformers/embedding-training-data
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/msmarco-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/msmarco-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 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). 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 (query, passage) from the [MS MARCO Passage-Ranking dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking).
|
doc2query/msmarco-t5-small-v1
|
doc2query
| 2022-01-10T10:19:24Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/embedding-training-data",
"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:
- sentence-transformers/embedding-training-data
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/msmarco-t5-small-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/msmarco-t5-small-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-small](https://huggingface.co/google/t5-v1_1-small) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). 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 (query, passage) from the [MS MARCO Passage-Ranking dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking).
|
huggingtweets/hostagekiller
|
huggingtweets
| 2022-01-10T10:05:54Z | 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: http://www.huggingtweets.com/hostagekiller/1641809138009/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/1473236995497500675/FtwXDZld_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">HUSSY2K.</div>
<div style="text-align: center; font-size: 14px;">@hostagekiller</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 HUSSY2K..
| Data | HUSSY2K. |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 819 |
| Short tweets | 395 |
| Tweets kept | 1972 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/u2hpg02v/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 @hostagekiller's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tx11pqs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tx11pqs/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/hostagekiller')
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)
|
vinhood/wineberto-italian-cased
|
vinhood
| 2022-01-10T08:26:52Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: it
license: mit
widget:
- text: "Con del pesce bisogna bere un bicchiere di vino [MASK]."
- text: "Con la carne c'è bisogno del vino [MASK]."
- text: "A tavola non può mancare del buon [MASK]."
---
# WineBERTo 🍷🥂
**wineberto-italian-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on Italian drink recipes and wine descriptions, approximately 77k sentences (3.3M words).
**Author:** Cristiano De Nobili ([@denocris](https://twitter.com/denocris) on Twitter, [LinkedIn](https://www.linkedin.com/in/cristiano-de-nobili/)) for [VINHOOD](https://www.vinhood.com/en/).
<p>
<img src="https://drive.google.com/uc?export=view&id=1dco9I9uzevP2V6oku1salIYcovUAeqWE" width="400"> </br>
</p>
# Perplexity
Test set: 14k sentences about wine.
| Model | Perplexity |
| ------ | ------ |
| wineberto-italian-cased | **2.29** |
| bert-base-italian-xxl-cased | 4.60 |
# Usage
```python
from transformers import AutoModel, AutoTokenizer
model_name = "vinhood/wineberto-italian-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
```
|
celtics1863/env-bert-chinese
|
celtics1863
| 2022-01-10T07:16:25Z | 62 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"pretrain",
"environment",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: zh
widget:
- text: "总[MASK]是水环境中的重要污染物。"
- text: "气[MASK]变化是重要的全球环境问题。"
tags:
- pretrain
- pytorch
- environment
---
环境领域的中文预训练Bert模型，在hlf/chinese-bert-wwm-ext的基础上进行训练，旨在学习到中文表达后进一步学习到环境领域的专业知识。
1.5G的预训练语料包括水环境、大气环境、土壤环境、气候变化、中文期刊、国家政策等内容。
项目正在进行中，后续会陆续更新相关内容。
清华大学环境学院课题组
有相关需求、建议，联系[email protected]
|
ai-forever/ruclip-vit-base-patch32-384
|
ai-forever
| 2022-01-10T00:21:50Z | 3,104 | 3 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# ruclip-vit-base-patch32-384
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and
multimodal learning.
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text ranking`; `image ranking`; `zero-shot image classification`;
* Type: `encoder`
* Num Parameters: `150M`
* Training Data Volume: `240 million text-image pairs`
* Language: `Russian`
* Context Length: `77`
* Transformer Layers: `12`
* Transformer Width: `512`
* Transformer Heads: `8`
* Image Size: `384`
* Vision Layers: `12`
* Vision Width: `768`
* Vision Patch Size: `32`
## Usage [Github](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
clip, processor = ruclip.load("ruclip-vit-base-patch32-384", device="cuda")
```
## Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|:--------------|:---------------|:----------------------------|
| Food101 | acc | 0.642 |
| CIFAR10 | acc | 0.862 |
| CIFAR100 | acc | 0.529 |
| Birdsnap | acc | 0.161 |
| SUN397 | acc | 0.510 |
| Stanford Cars | acc | 0.572 |
| DTD | acc | 0.390 |
| MNIST | acc | 0.404 |
| STL10 | acc | 0.946 |
| PCam | acc | 0.506 |
| CLEVR | acc | 0.188 |
| Rendered SST2 | acc | 0.508 |
| ImageNet | acc | 0.451 |
| FGVC Aircraft | mean-per-class | 0.053 |
| Oxford Pets | mean-per-class | 0.587 |
| Caltech101 | mean-per-class | 0.834 |
| Flowers102 | mean-per-class | 0.449 |
| HatefulMemes | roc-auc | 0.537 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
|
ai-forever/ruclip-vit-large-patch14-336
|
ai-forever
| 2022-01-09T22:25:33Z | 834 | 2 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# ruclip-vit-large-patch14-336
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and
multimodal learning.
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text ranking`; `image ranking`; `zero-shot image classification`;
* Type: `encoder`
* Num Parameters: `430M`
* Training Data Volume: `240 million text-image pairs`
* Language: `Russian`
* Context Length: `77`
* Transformer Layers: `12`
* Transformer Width: `768`
* Transformer Heads: `12`
* Image Size: `336`
* Vision Layers: `24`
* Vision Width: `1024`
* Vision Patch Size: `14`
## Usage [Github](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
clip, processor = ruclip.load("ruclip-vit-large-patch14-336", device="cuda")
```
## Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|:--------------|:---------------|:--------------------|
| Food101 | acc | 0.712 |
| CIFAR10 | acc | 0.906 |
| CIFAR100 | acc | 0.591 |
| Birdsnap | acc | 0.213 |
| SUN397 | acc | 0.523 |
| Stanford Cars | acc | 0.659 |
| DTD | acc | 0.408 |
| MNIST | acc | 0.242 |
| STL10 | acc | 0.956 |
| PCam | acc | 0.554 |
| CLEVR | acc | 0.142 |
| Rendered SST2 | acc | 0.539 |
| ImageNet | acc | 0.488 |
| FGVC Aircraft | mean-per-class | 0.075 |
| Oxford Pets | mean-per-class | 0.546 |
| Caltech101 | mean-per-class | 0.835 |
| Flowers102 | mean-per-class | 0.517 |
| HatefulMemes | roc-auc | 0.519 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
|
Firat/roberta-base-finetuned-squad
|
Firat
| 2022-01-09T22:12:48Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-squad
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-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8953
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8926 | 1.0 | 5536 | 0.8694 |
| 0.6821 | 2.0 | 11072 | 0.8428 |
| 0.5335 | 3.0 | 16608 | 0.8953 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ai-forever/ruclip-vit-large-patch14-224
|
ai-forever
| 2022-01-09T21:43:58Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# ruclip-vit-large-patch14-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and
multimodal learning.
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text ranking`; `image ranking`; `zero-shot image classification`;
* Type: `encoder`
* Num Parameters: `430M`
* Training Data Volume: `240 million text-image pairs`
* Language: `Russian`
* Context Length: `77`
* Transformer Layers: `12`
* Transformer Width: `768`
* Transformer Heads: `12`
* Image Size: `224`
* Vision Layers: `24`
* Vision Width: `1024`
* Vision Patch Size: `14`
## Usage [Github](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
clip, processor = ruclip.load("ruclip-vit-large-patch14-224", device="cuda")
```
## Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|:--------------|:---------------|:--------------------|
| Food101 | acc | 0.597 |
| CIFAR10 | acc | 0.878 |
| CIFAR100 | acc | 0.511 |
| Birdsnap | acc | 0.172 |
| SUN397 | acc | 0.484 |
| Stanford Cars | acc | 0.559 |
| DTD | acc | 0.370 |
| MNIST | acc | 0.337 |
| STL10 | acc | 0.934 |
| PCam | acc | 0.520 |
| CLEVR | acc | 0.152 |
| Rendered SST2 | acc | 0.529 |
| ImageNet | acc | 0.426 |
| FGVC Aircraft | mean-per-class | 0.046 |
| Oxford Pets | mean-per-class | 0.604 |
| Caltech101 | mean-per-class | 0.777 |
| Flowers102 | mean-per-class | 0.455 |
| HatefulMemes | roc-auc | 0.530 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
|
ai-forever/ruclip-vit-base-patch32-224
|
ai-forever
| 2022-01-09T21:34:27Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# ruclip-vit-base-patch32-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and
multimodal learning.
Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
* Task: `text ranking`; `image ranking`; `zero-shot image classification`;
* Type: `encoder`
* Num Parameters: `150M`
* Training Data Volume: `240 million text-image pairs`
* Language: `Russian`
* Context Length: `77`
* Transformer Layers: `12`
* Transformer Width: `512`
* Transformer Heads: `8`
* Image Size: `224`
* Vision Layers: `12`
* Vision Width: `768`
* Vision Patch Size: `32`
## Usage [Github](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
clip, processor = ruclip.load("ruclip-vit-base-patch32-224", device="cuda")
```
## Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|:--------------|:---------------|:--------------------|
| Food101 | acc | 0.505 |
| CIFAR10 | acc | 0.818 |
| CIFAR100 | acc | 0.504 |
| Birdsnap | acc | 0.115 |
| SUN397 | acc | 0.452 |
| Stanford Cars | acc | 0.433 |
| DTD | acc | 0.380 |
| MNIST | acc | 0.447 |
| STL10 | acc | 0.932 |
| PCam | acc | 0.501 |
| CLEVR | acc | 0.148 |
| Rendered SST2 | acc | 0.489 |
| ImageNet | acc | 0.375 |
| FGVC Aircraft | mean-per-class | 0.033 |
| Oxford Pets | mean-per-class | 0.560 |
| Caltech101 | mean-per-class | 0.786 |
| Flowers102 | mean-per-class | 0.401 |
| HatefulMemes | roc-auc | 0.564 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
|
huggingtweets/elxokas-evilafm-ibaillanos
|
huggingtweets
| 2022-01-09T19:38:49Z | 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: http://www.huggingtweets.com/elxokas-evilafm-ibaillanos/1641757124234/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/1476303212672131074/kuPm3Cvp_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/1473427376696705024/mzWRw3ML_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/1402480040877699075/LShUbbef_400x400.jpg')">
</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">Ibai & Alexelcapo & XOKAS</div>
<div style="text-align: center; font-size: 14px;">@elxokas-evilafm-ibaillanos</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 Ibai & Alexelcapo & XOKAS.
| Data | Ibai | Alexelcapo | XOKAS |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3207 | 3245 |
| Retweets | 28 | 12 | 187 |
| Short tweets | 669 | 231 | 421 |
| Tweets kept | 2553 | 2964 | 2637 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ed2k4vcn/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 @elxokas-evilafm-ibaillanos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/169fwvwo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/169fwvwo/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/elxokas-evilafm-ibaillanos')
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)
|
huggingtweets/ibaillanos
|
huggingtweets
| 2022-01-09T18:36:11Z | 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: http://www.huggingtweets.com/ibaillanos/1641753367000/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/1476303212672131074/kuPm3Cvp_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">Ibai</div>
<div style="text-align: center; font-size: 14px;">@ibaillanos</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 Ibai.
| Data | Ibai |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 28 |
| Short tweets | 669 |
| Tweets kept | 2553 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3qyv6lsf/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 @ibaillanos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cxnkmkg6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cxnkmkg6/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/ibaillanos')
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)
|
tonyalves/wav2vec2-large-xls-r-300m-pt-colab
|
tonyalves
| 2022-01-09T17:40:58Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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-xls-r-300m-pt-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-xls-r-300m-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3637
- Wer: 0.2982
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.591 | 1.15 | 400 | 0.9128 | 0.6517 |
| 0.5049 | 2.31 | 800 | 0.4596 | 0.4437 |
| 0.2871 | 3.46 | 1200 | 0.3964 | 0.3905 |
| 0.2077 | 4.61 | 1600 | 0.3958 | 0.3744 |
| 0.1695 | 5.76 | 2000 | 0.4040 | 0.3720 |
| 0.1478 | 6.92 | 2400 | 0.3866 | 0.3651 |
| 0.1282 | 8.07 | 2800 | 0.3987 | 0.3674 |
| 0.1134 | 9.22 | 3200 | 0.4128 | 0.3688 |
| 0.1048 | 10.37 | 3600 | 0.3928 | 0.3561 |
| 0.0938 | 11.53 | 4000 | 0.4048 | 0.3619 |
| 0.0848 | 12.68 | 4400 | 0.4229 | 0.3555 |
| 0.0798 | 13.83 | 4800 | 0.3974 | 0.3468 |
| 0.0688 | 14.98 | 5200 | 0.3870 | 0.3503 |
| 0.0658 | 16.14 | 5600 | 0.3875 | 0.3351 |
| 0.061 | 17.29 | 6000 | 0.4133 | 0.3417 |
| 0.0569 | 18.44 | 6400 | 0.3915 | 0.3414 |
| 0.0526 | 19.6 | 6800 | 0.3957 | 0.3231 |
| 0.0468 | 20.75 | 7200 | 0.4110 | 0.3301 |
| 0.0407 | 21.9 | 7600 | 0.3866 | 0.3186 |
| 0.0384 | 23.05 | 8000 | 0.3976 | 0.3193 |
| 0.0363 | 24.21 | 8400 | 0.3910 | 0.3177 |
| 0.0313 | 25.36 | 8800 | 0.3656 | 0.3109 |
| 0.0293 | 26.51 | 9200 | 0.3712 | 0.3092 |
| 0.0277 | 27.66 | 9600 | 0.3613 | 0.3054 |
| 0.0249 | 28.82 | 10000 | 0.3783 | 0.3015 |
| 0.0234 | 29.97 | 10400 | 0.3637 | 0.2982 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
tonyalves/wav2vec2-300M-teste2
|
tonyalves
| 2022-01-09T17:16:10Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-300M-teste2
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. -->
# wav2vec2-300M-teste2
This model was trained from scratch on the common_voice 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Littlemilk/autobiography-generator
|
Littlemilk
| 2022-01-09T17:15:14Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- zh
license: gpl-3.0
tags:
- generated_from_trainer
model-index:
- name: clm-total
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. -->
# clm-total
This model is a fine-tuned version of [ckiplab/gpt2-base-chinese](https://huggingface.co/ckiplab/gpt2-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8586
## 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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Vassilis/distilbert-base-uncased-finetuned-emotion
|
Vassilis
| 2022-01-09T16:41:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
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: 0.1628
- Accuracy: 0.9345
- F1: 0.9348
## 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: 64
- eval_batch_size: 64
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1674 | 1.0 | 250 | 0.1718 | 0.9265 | 0.9266 |
| 0.1091 | 2.0 | 500 | 0.1628 | 0.9345 | 0.9348 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0
- Tokenizers 0.10.3
|
ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop
|
ying-tina
| 2022-01-09T12:13:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-32-epochs50-earlystop
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-timit-demo-colab-32-epochs50-earlystop
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5208
- Wer: 0.3561
## 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: 8
- 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4294 | 4.0 | 500 | 1.3397 | 0.8966 |
| 0.5848 | 8.0 | 1000 | 0.4931 | 0.4585 |
| 0.2323 | 12.0 | 1500 | 0.4781 | 0.4008 |
| 0.14 | 16.0 | 2000 | 0.4294 | 0.3806 |
| 0.1026 | 20.0 | 2500 | 0.5098 | 0.3663 |
| 0.0725 | 24.0 | 3000 | 0.4527 | 0.3568 |
| 0.058 | 28.0 | 3500 | 0.5208 | 0.3561 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
dead69/GPT-small-yoda
|
dead69
| 2022-01-09T11:24:39Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dead69/GTP-small-yoda")
model = AutoModelWithLMHead.from_pretrained("dead69/GTP-small-yoda")
# Let's chat for 4 lines
for step in range(10):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Master YODA: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
NahedAbdelgaber/evaluating-student-writing-distibert-ner-with-metric
|
NahedAbdelgaber
| 2022-01-09T06:45:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: evaluating-student-writing-distibert-ner-with-metric
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. -->
# evaluating-student-writing-distibert-ner-with-metric
This model is a fine-tuned version of [NahedAbdelgaber/evaluating-student-writing-distibert-ner](https://huggingface.co/NahedAbdelgaber/evaluating-student-writing-distibert-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7535
- Precision: 0.0614
- Recall: 0.2590
- F1: 0.0993
- Accuracy: 0.6188
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7145 | 1.0 | 1755 | 0.7683 | 0.0546 | 0.2194 | 0.0875 | 0.6191 |
| 0.6608 | 2.0 | 3510 | 0.7504 | 0.0570 | 0.2583 | 0.0934 | 0.6136 |
| 0.5912 | 3.0 | 5265 | 0.7535 | 0.0614 | 0.2590 | 0.0993 | 0.6188 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
RenZHU/t5-small-finetuned-xsum-original
|
RenZHU
| 2022-01-09T06:04:38Z | 5 | 1 |
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
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-original
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.8838
---
<!-- 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-original
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: 2.4436
- Rouge1: 28.8838
- Rouge2: 8.1114
- Rougel: 22.8318
- Rougelsum: 22.8318
- Gen Len: 18.8141
## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6754 | 1.0 | 51012 | 2.4436 | 28.8838 | 8.1114 | 22.8318 | 22.8318 | 18.8141 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
haji2438/bertweet-base-finetuned-SNS-brand-personality
|
haji2438
| 2022-01-09T03:24:39Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: bertweet-base-finetuned-SNS-brand-personality
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. -->
# bertweet-base-finetuned-SNS-brand-personality
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0498
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0757 | 1.0 | 1549 | 0.0723 |
| 0.0605 | 2.0 | 3098 | 0.0573 |
| 0.0498 | 3.0 | 4647 | 0.0498 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt
|
vuiseng9
| 2022-01-09T03:14:14Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 60% upon initialization. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and ffnn are targeted.
2. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
3. Custom distillation with large model ```bert-large-uncased-whole-word-masking-finetuned-squad```
```
eval_exact_match = 80.3122
eval_f1 = 87.6162
eval_samples = 10784
```
# Setup
```bash
# OpenVINO/NNCF
git clone https://github.com/vuiseng9/nncf && cd nncf
git checkout tld-poc
git reset --hard 1dec7afe7a4b567c059fcf287ea2c234980fded2
python setup.py develop
pip install -r examples/torch/requirements.txt
# Huggingface nn_pruning
git clone https://github.com/vuiseng9/nn_pruning && cd nn_pruning
git checkout reproduce-evaluation
git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446
pip install -e ".[dev]"
# Huggingface Transformers
git clone https://github.com/vuiseng9/transformers && cd transformers
git checkout tld-poc
git reset --hard 10a1e29d84484e48fd106f58957d9ffc89dc43c5
pip install -e .
head -n 1 examples/pytorch/question-answering/requirements.txt | xargs -i pip install {}
# Additional dependencies
pip install onnx
```
# Train
```bash
git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt
BASE_MODEL=/path/to/cloned_repo_above #to-revise
wget https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt/raw/main/nncf_bert_squad_sparsity.json
NNCF_CFG=/path/to/downloaded_nncf_cfg_above #to-revise
OUTROOT=/path/to/train_output_root #to-revise
WORKDIR=transformers/examples/pytorch/question-answering #to-revise
RUNID=bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt
cd $WORKDIR
OUTDIR=$OUTROOT/$RUNID
mkdir -p $OUTDIR
export CUDA_VISIBLE_DEVICES=0
NEPOCH=5
python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
--optimize_model_before_eval \
--optimized_checkpoint $BASE_MODEL \
--dataset_name squad \
--do_eval \
--do_train \
--evaluation_strategy steps \
--eval_steps 250 \
--learning_rate 3e-5 \
--lr_scheduler_type cosine_with_restarts \
--warmup_ratio 0.25 \
--cosine_cycles 1 \
--teacher bert-large-uncased-whole-word-masking-finetuned-squad \
--teacher_ratio 0.9 \
--num_train_epochs $NEPOCH \
--per_device_eval_batch_size 128 \
--per_device_train_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 250 \
--nncf_config $NNCF_CFG \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR
```
# Eval
This repo must be cloned locally.
```bash
git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt
MODELROOT=/path/to/cloned_repo_above #to-revise
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt
WORKDIR=transformers/examples/pytorch/question-answering #to-revise
cd $WORKDIR
mkdir $OUTDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
--dataset_name squad \
--optimize_model_before_eval \
--qat_checkpoint $MODELROOT/checkpoint-22000 \
--nncf_config $MODELROOT/nncf_bert_squad_sparsity.json \
--to_onnx $OUTDIR/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt.onnx \
--do_eval \
--per_device_eval_batch_size 128 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
|
RenZHU/t5-small-finetuned-xsum
|
RenZHU
| 2022-01-09T03:09:55Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5310
- Rouge1: 27.9232
- Rouge2: 7.5324
- Rougel: 22.035
- Rougelsum: 22.0304
- Gen Len: 18.8116
## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.7564 | 1.0 | 51012 | 2.5310 | 27.9232 | 7.5324 | 22.035 | 22.0304 | 18.8116 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
vuiseng9/bert-base-uncased-squad
|
vuiseng9
| 2022-01-08T18:08:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-base-uncased-squad
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--do_eval \
--do_train \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 16 \
--doc_stride 128 \
--max_seq_length 384 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--eval_steps 250 \
--save_steps 2500 \
--logging_steps 1 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-uncased-squad
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-uncased-squad \
--dataset_name squad \
--do_eval \
--per_device_eval_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
|
LeverageX/scibert-wechsel-korean
|
LeverageX
| 2022-01-08T12:14:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
# scibert-wechsel-korean
Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.
### Description
- SciBERT is trained on papers from the corpus of semanticscholar.org. Corpus size is 1.14M papers, 3.1B tokens.
- Wechsel is converting embedding layer's subword tokens from source language to target language.
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance.
### Reference
- [Scibert](https://github.com/allenai/scibert)
- [WECHSEL](https://github.com/CPJKU/wechsel)
- [Korean Language Understanding Evaluation](https://github.com/KLUE-benchmark/KLUE)
|
LanceaKing/spkrec-ecapa-cnceleb
|
LanceaKing
| 2022-01-08T09:27:18Z | 12 | 4 |
speechbrain
|
[
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"zh",
"dataset:cnceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language: "zh"
thumbnail:
tags:
- speechbrain
- embeddings
- Speaker
- Verification
- Identification
- pytorch
- ECAPA
- TDNN
license: "apache-2.0"
datasets:
- cnceleb
metrics:
- EER
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on cnceleb
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as well.
It is trained on cnceleb 1+ cnceleb2 training data.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The model performance on cnceleb1-test set(Cleaned) is:
| Release | EER(%) | minDCF |
|:-------------:|:--------------:|:--------------:|
## Pipeline description
This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Compute your speaker embeddings
```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="LanceaKing/spkrec-ecapa-cnceleb")
signal, fs =torchaudio.load('samples/audio_samples/example1.wav')
embeddings = classifier.encode_batch(signal)
```
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
### Perform Speaker Verification
```python
from speechbrain.pretrained import SpeakerRecognition
verification = SpeakerRecognition.from_hparams(source="LanceaKing/spkrec-ecapa-cnceleb", savedir="pretrained_models/spkrec-ecapa-cnceleb")
score, prediction = verification.verify_files("speechbrain/spkrec-ecapa-cnceleb/example1.wav", "speechbrain/spkrec-ecapa-cnceleb/example2.flac")
```
The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain (aa018540).
To train it from scratch follows these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/LanceaKing/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```
cd recipes/CNCeleb/SpeakerRec
python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Referencing ECAPA-TDNN
```
@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
author = {Brecht Desplanques and
Jenthe Thienpondt and
Kris Demuynck},
editor = {Helen Meng and
Bo Xu and
Thomas Fang Zheng},
title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
in {TDNN} Based Speaker Verification},
booktitle = {Interspeech 2020},
pages = {3830--3834},
publisher = {{ISCA}},
year = {2020},
}
```
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and Fran莽ois Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
|
tai-dang11/test2
|
tai-dang11
| 2022-01-08T04:34:31Z | 64 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: test2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test2
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:
- Train Loss: 0.2510
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.2510 | 0 |
### Framework versions
- Transformers 4.16.0.dev0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
espnet/Karthik_sinhala_asr_train_asr_transformer
|
espnet
| 2022-01-08T03:24:39Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- sinhala
license: cc-by-4.0
---
## ESPnet2 ASR pretrained model
### `espnet/Karthik_sinhala_asr_train_asr_transformer`
This model was trained by Karthik using sinhala/asr1 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}
}
```
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}
}
```
|
eliwill/rare-puppers
|
eliwill
| 2022-01-08T01:40:43Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.4895833432674408
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### algebra
#### arithmetic
#### calculus
#### geometry
#### trigonometry
|
Peter/medium
|
Peter
| 2022-01-08T01:14:45Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: medium
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. -->
# medium
This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_paraphraser_on_T5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6025
- Rouge1: 81.6007
- Rouge2: 75.1196
- Rougel: 81.4213
- Rougelsum: 81.4956
- Gen Len: 32.4286
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 63 | 0.5775 | 65.0748 | 58.8985 | 64.5731 | 63.6249 | 19.0 |
| No log | 2.0 | 126 | 0.5806 | 74.3055 | 69.2025 | 73.4922 | 73.0941 | 17.8571 |
| No log | 3.0 | 189 | 0.6025 | 71.3808 | 66.0359 | 70.1235 | 69.4614 | 18.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
lincoln/2021twitchfr-conv-bert-small-mlm-simcse
|
lincoln
| 2022-01-07T18:00:43Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"convbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"twitch",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: mit
pipeline_tag: sentence-similarity
widget:
- source_sentence: "Bonsoir"
sentences:
- "Salut !"
- "Hello"
- "Bonsoir!"
- "Bonsouar!"
- "Bonsouar !"
- "De rien"
- "LUL LUL"
example_title: "Coucou"
- source_sentence: "elle s'en sort bien"
sentences:
- "elle a raison"
- "elle a tellement raison"
- "Elle a pas tort"
- "C'est bien ce qu'elle dit là"
- "Hello"
example_title: "Raison or not"
- source_sentence: "et la question énergétique n'est pas politique ?"
sentences:
- "C'est le nucléaire militaire qui a entaché le nucléaire pour l'énergie."
- "La fusion nucléaire c'est pas pour maintenant malheureusement"
- "le pro nucléaire redevient acceptable à gauche j'ai l'impression"
- "La mer à Nantes?"
- "c'est bien un olivier pour l'upr"
- "Moi je vois juste sa lavallière"
example_title: "Nucléaire"
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- twitch
- convbert
---
## Modèle de représentation d'un message Twitch à l'aide de ConvBERT
Modèle [sentence-transformers](https://www.SBERT.net): cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique.
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire internet que cela implique (fautes, vocabulaire communautaires, abréviations, anglicisme, emotes, ...).
Après avoir entrainé un modèle `ConvBert` puis `MLM` (cf section smodèles), nous avons entrainé un modèle _sentence-transformers_ à l'aide du framework d'apprentissage [SimCSE](https://www.sbert.net/examples/unsupervised_learning/SimCSE/README.html) en non supervisée.
L'objectif est de spécialiser la moyenne des tokens _CLS_ de chaque token de la séquence pour représenter un vecteur numérique cohérent avec l'ensemble du corpus. _SimCSE_ crée fictivement des exemples positifs et négatifs supervisées à l'aide du dropout pour revenir à une tâche classique.
_Nous garantissons pas la stabilité du modèle sur le long terme. Modèle réalisé dans le cadre d'un POC._
## 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('2021twitchfr-conv-bert-small-mlm-simcse')
embeddings = model.encode(sentences)
print(embeddings)
```
## Semantic Textual Similarity
```python
from sentence_transformers import SentenceTransformer, models, util
# Two lists of sentences
sentences1 = ['zackFCZack',
'Team bons petits plats',
'sa commence a quelle heure de base popcorn ?',
'BibleThump']
sentences2 = ['zack titulaire',
'salade de pates c une dinguerie',
'ça commence à être long la',
'NotLikeThis']
# Compute embedding for both lists
embeddings1 = model.encode(sentences1, convert_to_tensor=True)
embeddings2 = model.encode(sentences2, convert_to_tensor=True)
# Compute cosine-similarits
cosine_scores = util.cos_sim(embeddings1, embeddings2)
# Output the pairs with their score
for i in range(len(sentences1)):
print("Score: {:.4f} | \"{}\" -vs- \"{}\" ".format(cosine_scores[i][i], sentences1[i], sentences2[i]))
# Score: 0.5783 | "zackFCZack" -vs- "zack titulaire"
# Score: 0.2881 | "Team bons petits plats" -vs- "salade de pates c une dinguerie"
# Score: 0.4529 | "sa commence a quelle heure de base popcorn ?" -vs- "ça commence à être long la"
# Score: 0.5805 | "BibleThump" -vs- "NotLikeThis"
```
## Entrainement
* 500 000 messages twitchs échantillonnés (cf description données des modèles de bases)
* Batch size: 24
* Epochs: 24
* Loss: MultipleNegativesRankingLoss
_A noter:_
* _ConvBert a été entrainé avec un longueur de 128 tokens max, mais est utilisé pour 512 dans ce modèle. Pas de problème._
* _La loss d'apprentissage n'est pas encore disponible: peu de visibilité sur les performances._
L'ensemble du code d'entrainement sur le github public [lincoln/twitchatds](https://github.com/Lincoln-France/twitchatds).
## Application:
Nous avons utilisé une approche détournée de [BERTopic](https://maartengr.github.io/BERTopic/) pour réaliser un clustering d'un stream en prenant en compte la dimension temporelle: i.e. le nombre de seconde écoulée depuis le début du stream.
Globalement, l'approche donnes des résultats satisfaisant pour identifier des messages dit "similaires" récurrents. L'approche en revanche est fortement influencée par la ponctuation et la structure d'un message. Cela est largement explicable par le manque d'entrainement de l'ensemble des modèles et une volumétrie faible.
### Clustering émission "Backseat":
Entre 19h30 et 20h00:
🎞️ en vidéo: [youtu.be/EcjvlE9aTls](https://youtu.be/EcjvlE9aTls)
### Exemple regroupement émission "PopCorn":
```txt
-------------------- LABEL 106 --------------------
circus (0.88)/sulli (0.23)/connu (0.19)/jure (0.12)/aime (0.11)
silouhette moyenne: 0.04
-------------------- LABEL 106 --------------------
2021-03-30 20:10:22 0.01: les gosse c est des animaux
2021-03-30 20:12:11 -0.03: oue c connu
2021-03-30 20:14:15 0.03: oh le circus !! <3
2021-03-30 20:14:19 0.12: le circus l'anciennnee
2021-03-30 20:14:22 0.06: jure le circus !
2021-03-30 20:14:27 -0.03: le sulli
2021-03-30 20:14:31 0.09: le circus??? j'aime po
2021-03-30 20:14:34 0.11: le Circus, hors de prix !
2021-03-30 20:14:35 -0.09: le Paddock a Rignac en Aveyron
2021-03-30 20:14:39 0.11: le circus ><
2021-03-30 20:14:39 0.04: le Titty Twister de Besançon
-------------------- LABEL 17 --------------------
pates (0.12)/riz (0.09)/pâtes (0.09)/salade (0.07)/emission (0.07)
silouhette moyenne: -0.05
-------------------- LABEL 17 --------------------
2021-03-30 20:11:18 -0.03: Des nanimaux trop beaux !
2021-03-30 20:13:11 -0.01: episode des simpsons ça...
2021-03-30 20:13:41 -0.01: des le debut d'emission ca tue mdrrrrr
2021-03-30 20:13:50 0.03: des "lasagnes"
2021-03-30 20:14:37 -0.18: poubelle la vie
2021-03-30 20:15:13 0.03: Une omelette
2021-03-30 20:15:35 -0.19: salade de bite
2021-03-30 20:15:36 -0.00: hahaha ce gastronome
2021-03-30 20:15:43 -0.08: salade de pates c une dinguerie
2021-03-30 20:17:00 -0.11: Une bonne femme !
2021-03-30 20:17:06 -0.05: bouffe des graines
2021-03-30 20:17:08 -0.06: des pokeball ?
2021-03-30 20:17:11 -0.12: le choux fleur cru
2021-03-30 20:17:15 0.05: des pockeball ?
2021-03-30 20:17:27 -0.00: du chou fleur crue
2021-03-30 20:17:36 -0.09: un râgout de Meynia !!!!
2021-03-30 20:17:43 -0.07: une line up Sa rd o ch Zack Ponce my dream
2021-03-30 20:17:59 -0.10: Pâtes/10
2021-03-30 20:18:09 -0.05: Team bons petits plats
2021-03-30 20:18:13 -0.10: pate level
2021-03-30 20:18:19 -0.03: que des trucs très basiques
2021-03-30 20:18:24 0.03: des pates et du jambon c'est de la cuisine?
2021-03-30 20:18:30 0.05: Des pates et du riz ouai
2021-03-30 20:18:37 -0.02: des gnocchis à la poele c'est cuisiner ?
2021-03-30 20:18:50 -0.03: Pâtes à pizzas, pulled pork, carbonade flamande, etc..
2021-03-30 20:19:01 -0.11: Des pâtes ou du riz ça compte ?
2021-03-30 20:19:22 -0.21: le noob
2021-03-30 20:19:47 -0.02: Une bonne escalope de milanaise les gars
2021-03-30 20:20:05 -0.04: faites des gratins et des quiches
-------------------- LABEL 67 --------------------
1 1 (0.25)/1 (0.19)/ (0.0)/ (0.0)/ (0.0)
silouhette moyenne: 0.96
-------------------- LABEL 67 --------------------
2021-03-30 20:24:17 0.94: +1
2021-03-30 20:24:37 0.97: +1
2021-03-30 20:24:37 0.97: +1
2021-03-30 20:24:38 0.97: +1
2021-03-30 20:24:39 0.97: +1
2021-03-30 20:24:43 0.97: +1
2021-03-30 20:24:44 0.97: +1
2021-03-30 20:24:47 0.97: +1
2021-03-30 20:24:49 0.97: +1
2021-03-30 20:25:00 0.97: +1
2021-03-30 20:25:21 0.95: +1
2021-03-30 20:25:25 0.95: +1
2021-03-30 20:25:28 0.94: +1
2021-03-30 20:25:30 0.94: +1
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ConvBertModel
(1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Modèles:
* [2021twitchfr-conv-bert-small](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small)
* [2021twitchfr-conv-bert-small-mlm](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm)
* [2021twitchfr-conv-bert-small-mlm-simcse](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm-simcse)
|
lincoln/2021twitchfr-conv-bert-small
|
lincoln
| 2022-01-07T15:25:20Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"convbert",
"feature-extraction",
"twitch",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: mit
pipeline_tag: "feature-extraction"
widget:
- text: LUL +1 xD La Fronce !
tags:
- feature-extraction
- convbert
- twitch
---
## Modèle de langue sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire internet que cela implique (fautes, vocabulaire communautaires, abréviations, anglicisme, emotes, ...).
Nos contraintes sont celles d’une entreprise n’ayant pas une volumétrie excessive de données et une puissance infinie de calcul.
Il a été nécessaire de construire un nouveau tokenizer afin de mieux correspondre à notre corpus plutôt qu’un tokenizer français existant.
Note corpus étant faible en volumétrie par rapport aux données habituelles pour entrainer un modèle BERT, nous avons opté pour l’entrainement d’un modèle dit « small ». Et il a été montré dans la littérature qu’un corpus de quelques giga octets peut donner de bons résultats, c’est pourquoi nous avons continué avec notre corpus.
La limite de la puissance de calcul a été contourné à l’aide d’une nouvelle architecture d’apprentissage basée sur un double modèle générateur / discriminateur.
Ceci nous a permis d’entrainer un modèle de langue ConvBERT sur nos données, ainsi qu’un modèle de masking en quelques heures sur une carte GPU V100.
_Nous garantissons pas la stabilité du modèle sur le long terme. Modèle réalisé dans le cadre d'un POC._
## Données
| Streamer | Nbr de messages | Categories notables en 2021 |
| --------------------------------------------- | --------------- | ---------------------------------- |
| Ponce | 2 604 935 | Chatting/Mario Kart/FIFA |
| Domingo | 1 209 703 | Chatting/talk-shows/FM2O21 |
| Mistermv | 1 205 882 | Isaac/Special events/TFT |
| Zerator | 900 894 | New World/WOW/Valorant |
| Blitzstream | 821 585 | Chess |
| Squeezie | 602 148 | Chatting / Minecraft |
| Antoinedaniellive | 548 497 | Geoguessr |
| Jeanmassietaccropolis/jeanmassiet | 301 387 | Talk-shows/chatting/special events |
| Samueletienne | 215 956 | chatting |
Sur la période du 12/03/2021 au 22/07/2021. La totalité des messages comptent 9 410 987 messages sur ces neufs streamers. Ces messages sont issus du canal IRC, donc n’ont pas subi de modération
Les données d'entrainement sont basé sur le format d'entrainement du modèle ELECTRA. Cela nécessite de formater les données en paragraphe, séparés par phrase. Nous avons choisi de regrouper les messages dans une fenêtre de 60 secondes, faisant office de paragraphe, avec les conditions suivantes :
* Longueur supérieure à 170 (ce qui représente en moyenne 50 tokens) afin de ne pas créer des instances ayant pas d’information car majoritairement vide : un padding sera nécessaire et pénalise la vitesse d’apprentissage.
* 128 tokens maximums (défaut)
Si la longueur maximale est atteinte, une deuxième instance est créée. Au final, la volumétrie d'instance d'entrainement est de 554 974.
## Application
Voir github public [lincoln/twitchatds](https://github.com/Lincoln-France/twitchatds) pour les détails d'implémentation et les résultats.
## Remarques
* Expérimentation ponctuelle
* Les métriques d'entrainement sont disponibles dans l'onglet _Training metrics_
* Pour une meilleure stabilité, les données doivent être plus hétérogènes et volumineuse. Le modèle doit être entrainé + de 24h.
## Usage
```python
from transformers import AutoTokenizer, ConvBertModel
from transformers import FeatureExtractionPipeline
model_name = 'lincoln/2021twitchfr-conv-bert-small'
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
loaded_model = ConvBertModel.from_pretrained(model_name)
nlp = FeatureExtractionPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
nlp("<3 <3 les modos")
```
## Modèles:
* [2021twitchfr-conv-bert-small](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small)
* [2021twitchfr-conv-bert-small-mlm](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm)
* [2021twitchfr-conv-bert-small-mlm-simcse](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm-simcse)
|
lincoln/2021twitchfr-conv-bert-small-mlm
|
lincoln
| 2022-01-07T15:23:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convbert",
"fill-mask",
"twitch",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: mit
pipeline_tag: "fill-mask"
widget:
- text: <mask> tt le monde !
- text: cc<mask> va?
- text: <mask> la Fronce !
tags:
- fill-mask
- convbert
- twitch
---
## Modèle de Masking sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire internet que cela implique (fautes, vocabulaire communautaires, abréviations, anglicisme, emotes, ...).
Nos contraintes sont celles d’une entreprise n’ayant pas une volumétrie excessive de données et une puissance infinie de calcul.
Il a été nécessaire de construire un nouveau tokenizer afin de mieux correspondre à notre corpus plutôt qu’un tokenizer français existant.
Note corpus étant faible en volumétrie par rapport aux données habituelles pour entrainer un modèle BERT, nous avons opté pour l’entrainement d’un modèle dit « small ». Et il a été montré dans la littérature qu’un corpus de quelques giga octets peut donner de bons résultats, c’est pourquoi nous avons continué avec notre corpus.
La limite de la puissance de calcul a été contourné à l’aide d’une nouvelle architecture d’apprentissage basée sur un double modèle générateur / discriminateur.
Ceci nous a permis d’entrainer un modèle de langue ConvBERT sur nos données, ainsi qu’un modèle de masking en quelques heures sur une carte GPU V100.
_Nous garantissons pas la stabilité du modèle sur le long terme. Modèle réalisé dans le cadre d'un POC._
## Données
| Streamer | Nbr de messages | Categories notables en 2021 |
| --------------------------------------------- | --------------- | ---------------------------------- |
| Ponce | 2 604 935 | Chatting/Mario Kart/FIFA |
| Domingo | 1 209 703 | Chatting/talk-shows/FM2O21 |
| Mistermv | 1 205 882 | Isaac/Special events/TFT |
| Zerator | 900 894 | New World/WOW/Valorant |
| Blitzstream | 821 585 | Chess |
| Squeezie | 602 148 | Chatting / Minecraft |
| Antoinedaniellive | 548 497 | Geoguessr |
| Jeanmassietaccropolis/jeanmassiet | 301 387 | Talk-shows/chatting/special events |
| Samueletienne | 215 956 | chatting |
Sur la période du 12/03/2021 au 22/07/2021. La totalité des messages comptent 9 410 987 messages sur ces neufs streamers. Ces messages sont issus du canal IRC, donc n’ont pas subi de modération
Les données d'entrainement du modèle de masking contient 899 652 instances de train et 99 962 instances de test. Les données ont été formaté en concaténant les messages sur une fenêtre de 10s. Cette fenêtre correspond à une fenêtre courte qui regroupe des messages très « proches » temporellement.
* 512 tokens max
* Probabilité du « mask » : 15%
## Application
Voir github public [lincoln/twitchatds](https://github.com/Lincoln-France/twitchatds) pour les détails d'implémentation et les résultats.
## Remarques
* Expérimentation ponctuelle
* Les métriques d'entrainement sont disponibles dans l'onglet _Training metrics_
* Pour une meilleure stabilité, les données doivent être plus hétérogènes et volumineuse. Le modèle doit être entrainé + de 24h.
* Le token `<mask>` fonctionne probablement mieux sans laisser d'espace à gauche. Cela est dû au fait que `lstrip=False` pour ce token spécial.
## Usage
```python
from transformers import AutoTokenizer, ConvBertForMaskedLM
from transformers import pipeline
model_name = 'lincoln/2021twitchfr-conv-bert-small-mlm'
tokenizer_name = 'lincoln/2021twitchfr-conv-bert-small'
loaded_tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
loaded_model = ConvBertForMaskedLM.from_pretrained(model_name)
nlp = pipeline('fill-mask', model=loaded_model, tokenizer=loaded_tokenizer)
nlp('<mask> les gens !')
```
## Modèles:
* [2021twitchfr-conv-bert-small](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small)
* [2021twitchfr-conv-bert-small-mlm](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm)
* [2021twitchfr-conv-bert-small-mlm-simcse](https://huggingface.co/lincoln/2021twitchfr-conv-bert-small-mlm-simcse)
|
Kien/distilbert-base-uncased-finetuned-cola
|
Kien
| 2022-01-07T15:00:42Z | 105 | 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: 0.5232819075279987
---
<!-- 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: 0.5327
- Matthews Correlation: 0.5233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5314 | 1.0 | 535 | 0.4955 | 0.4270 |
| 0.3545 | 2.0 | 1070 | 0.5327 | 0.5233 |
| 0.2418 | 3.0 | 1605 | 0.6180 | 0.5132 |
| 0.1722 | 4.0 | 2140 | 0.7344 | 0.5158 |
| 0.1243 | 5.0 | 2675 | 0.8581 | 0.5196 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
hs788/wav2vec2-base-timit-demo-colab
|
hs788
| 2022-01-07T13:34:11Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4125
- Wer: 0.3607
## 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: 64
- 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_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2018 | 7.94 | 500 | 1.3144 | 0.8508 |
| 0.4671 | 15.87 | 1000 | 0.4737 | 0.4160 |
| 0.1375 | 23.81 | 1500 | 0.4125 | 0.3607 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ietz/distilroberta-base-finetuned-jira-qt-issue-title
|
ietz
| 2022-01-07T12:27:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"jira",
"code",
"issue",
"development",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- jira
- code
- issue
- development
license: mit
---
`distilroberta-base` finetuned for masked language modeling on 126213 Qt jira issue titles for up to 50 epochs.
|
doc2query/reddit-t5-small-v1
|
doc2query
| 2022-01-07T08:55:11Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"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:
- datasets/sentence-transformers/reddit-title-body
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/reddit-t5-small-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/reddit-t5-small-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=384, 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-small](https://huggingface.co/google/t5-v1_1-small) for 547k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (title, body) from Reddit.
|
doc2query/stackexchange-title-body-t5-base-v1
|
doc2query
| 2022-01-07T08:48:22Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:flax-sentence-embeddings/stackexchange_title_body_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_body_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-title-body-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-title-body-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 550k 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, question_body) from StackExchange.
|
Shenyancheng/distilbert-base-uncased-finetuned-ner
|
Shenyancheng
| 2022-01-07T04:37:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9266592920353982
- name: Recall
type: recall
value: 0.9371294328224634
- name: F1
type: f1
value: 0.9318649535569274
- name: Accuracy
type: accuracy
value: 0.9838117781625813
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
- Precision: 0.9267
- Recall: 0.9371
- F1: 0.9319
- Accuracy: 0.9838
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2462 | 1.0 | 878 | 0.0714 | 0.9052 | 0.9223 | 0.9137 | 0.9803 |
| 0.0535 | 2.0 | 1756 | 0.0615 | 0.9188 | 0.9331 | 0.9259 | 0.9827 |
| 0.0315 | 3.0 | 2634 | 0.0620 | 0.9267 | 0.9371 | 0.9319 | 0.9838 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/shegotadankwa
|
huggingtweets
| 2022-01-07T04:37:33Z | 98 | 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: http://www.huggingtweets.com/shegotadankwa/1641530248419/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/1466974207313649667/8zoSbNnW_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">blizzy b 🏄🏾♀️</div>
<div style="text-align: center; font-size: 14px;">@shegotadankwa</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 blizzy b 🏄🏾♀️.
| Data | blizzy b 🏄🏾♀️ |
| --- | --- |
| Tweets downloaded | 3164 |
| Retweets | 916 |
| Short tweets | 667 |
| Tweets kept | 1581 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ayiomb1h/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 @shegotadankwa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ezr5ck3t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ezr5ck3t/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/shegotadankwa')
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)
|
huggingartists/obladaet
|
huggingartists
| 2022-01-07T01:09:32Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/obladaet",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/obladaet
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/4411ffc50a3cd07d303d09a5db3b7cf5.1000x1000x1.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">OBLADAET</div>
<a href="https://genius.com/artists/obladaet">
<div style="text-align: center; font-size: 14px;">@obladaet</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 OBLADAET.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/obladaet).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/obladaet")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1mtsuuwr/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 OBLADAET's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1s9epb35) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1s9epb35/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/obladaet')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/obladaet")
model = AutoModelWithLMHead.from_pretrained("huggingartists/obladaet")
```
## 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)
|
BigSalmon/InformalToFormalLincoln18
|
BigSalmon
| 2022-01-06T22:00:50Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln18")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln18")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```
```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```
```
https://huggingface.co/spaces/BigSalmon/GPT2Space (The model for this space changes over time)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
````
|
Waynehillsdev/Waynehills-STT-doogie-server
|
Waynehillsdev
| 2022-01-06T17:18:49Z | 87 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
name: Waynehills-STT-doogie-server
---
<!-- 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. -->
# Waynehills-STT-doogie-server
This model is a fine-tuned version of [Doogie/Waynehills-STT-doogie-server](https://huggingface.co/Doogie/Waynehills-STT-doogie-server) 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: 0.0001
- train_batch_size: 16
- 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_steps: 1000
- num_epochs: 60
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
shaina/covid_qa_distillBert
|
shaina
| 2022-01-06T15:41:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
widget:
- text: "What is COVID-19?"
context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic."
- text: "Where was COVID-19 first discovered?"
context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event."
- text: "What is Post-COVID syndrome?"
context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction."
model-index:
- name: CoQUAD_DistilBERT_v1
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. -->
# covid_qa_distillBert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the covid_qa_deepset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0971
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2537 | 1.0 | 3880 | 0.1871 |
| 0.2005 | 2.0 | 7760 | 0.1257 |
| 0.1395 | 3.0 | 11640 | 0.0971 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
unicamp-dl/ptt5-base-pt-msmarco-100k-v2
|
unicamp-dl
| 2022-01-06T13:44:21Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: pt
license: mit
tags:
- msmarco
- t5
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- msmarco
widget:
- text: "Texto de exemplo em português"
inference: false
---
# PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps.
Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository.
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'unicamp-dl/ptt5-base-msmarco-pt-100k-v2'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
```
# Citation
If you use ptt5-base-msmarco-pt-100k-v2, please cite:
@misc{bonifacio2021mmarco,
title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
eprint={2108.13897},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
mimi/Waynehills-NLP-doogie
|
mimi
| 2022-01-06T08:02:38Z | 3 | 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: Waynehills-NLP-doogie
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. -->
# Waynehills-NLP-doogie
This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9188
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 28.2167 | 0.06 | 1000 | 9.7030 |
| 10.4479 | 0.12 | 2000 | 7.5450 |
| 8.0306 | 0.19 | 3000 | 6.1969 |
| 6.503 | 0.25 | 4000 | 5.3015 |
| 5.5406 | 0.31 | 5000 | 4.6363 |
| 4.7299 | 0.38 | 6000 | 4.0431 |
| 3.9263 | 0.44 | 7000 | 3.6313 |
| 3.4111 | 0.5 | 8000 | 3.4830 |
| 3.0517 | 0.56 | 9000 | 3.3294 |
| 2.7524 | 0.62 | 10000 | 3.2077 |
| 2.5402 | 0.69 | 11000 | 3.1094 |
| 2.3228 | 0.75 | 12000 | 3.1099 |
| 2.1513 | 0.81 | 13000 | 3.0284 |
| 2.0418 | 0.88 | 14000 | 3.0155 |
| 1.8875 | 0.94 | 15000 | 3.0241 |
| 1.756 | 1.0 | 16000 | 3.0165 |
| 1.6489 | 1.06 | 17000 | 2.9849 |
| 1.5788 | 1.12 | 18000 | 2.9496 |
| 1.5368 | 1.19 | 19000 | 2.9500 |
| 1.4467 | 1.25 | 20000 | 3.0133 |
| 1.381 | 1.31 | 21000 | 2.9631 |
| 1.3451 | 1.38 | 22000 | 3.0159 |
| 1.2917 | 1.44 | 23000 | 2.9906 |
| 1.2605 | 1.5 | 24000 | 3.0006 |
| 1.2003 | 1.56 | 25000 | 2.9797 |
| 1.1987 | 1.62 | 26000 | 2.9253 |
| 1.1703 | 1.69 | 27000 | 3.0044 |
| 1.1474 | 1.75 | 28000 | 2.9216 |
| 1.0816 | 1.81 | 29000 | 2.9645 |
| 1.0709 | 1.88 | 30000 | 3.0439 |
| 1.0476 | 1.94 | 31000 | 3.0844 |
| 1.0645 | 2.0 | 32000 | 2.9434 |
| 1.0204 | 2.06 | 33000 | 2.9386 |
| 0.9901 | 2.12 | 34000 | 3.0452 |
| 0.9911 | 2.19 | 35000 | 2.9798 |
| 0.9706 | 2.25 | 36000 | 2.9919 |
| 0.9461 | 2.31 | 37000 | 3.0279 |
| 0.9577 | 2.38 | 38000 | 2.9615 |
| 0.9466 | 2.44 | 39000 | 2.9988 |
| 0.9486 | 2.5 | 40000 | 2.9133 |
| 0.9201 | 2.56 | 41000 | 3.0004 |
| 0.896 | 2.62 | 42000 | 2.9626 |
| 0.8893 | 2.69 | 43000 | 2.9667 |
| 0.9028 | 2.75 | 44000 | 2.9543 |
| 0.897 | 2.81 | 45000 | 2.8760 |
| 0.8664 | 2.88 | 46000 | 2.9894 |
| 0.8719 | 2.94 | 47000 | 2.8456 |
| 0.8491 | 3.0 | 48000 | 2.9713 |
| 0.8402 | 3.06 | 49000 | 2.9738 |
| 0.8484 | 3.12 | 50000 | 2.9361 |
| 0.8304 | 3.19 | 51000 | 2.8945 |
| 0.8208 | 3.25 | 52000 | 2.9625 |
| 0.8074 | 3.31 | 53000 | 3.0054 |
| 0.8226 | 3.38 | 54000 | 2.9405 |
| 0.8185 | 3.44 | 55000 | 2.9047 |
| 0.8352 | 3.5 | 56000 | 2.9016 |
| 0.8289 | 3.56 | 57000 | 2.9490 |
| 0.7918 | 3.62 | 58000 | 2.9621 |
| 0.8212 | 3.69 | 59000 | 2.9341 |
| 0.7955 | 3.75 | 60000 | 2.9167 |
| 0.7724 | 3.81 | 61000 | 2.9409 |
| 0.8169 | 3.88 | 62000 | 2.8925 |
| 0.7862 | 3.94 | 63000 | 2.9314 |
| 0.803 | 4.0 | 64000 | 2.9271 |
| 0.7595 | 4.06 | 65000 | 2.9263 |
| 0.7931 | 4.12 | 66000 | 2.9400 |
| 0.7759 | 4.19 | 67000 | 2.9501 |
| 0.7859 | 4.25 | 68000 | 2.9133 |
| 0.805 | 4.31 | 69000 | 2.8785 |
| 0.7649 | 4.38 | 70000 | 2.9060 |
| 0.7692 | 4.44 | 71000 | 2.8868 |
| 0.7692 | 4.5 | 72000 | 2.9045 |
| 0.7798 | 4.56 | 73000 | 2.8951 |
| 0.7812 | 4.62 | 74000 | 2.9068 |
| 0.7533 | 4.69 | 75000 | 2.9129 |
| 0.7527 | 4.75 | 76000 | 2.9157 |
| 0.7652 | 4.81 | 77000 | 2.9053 |
| 0.7633 | 4.88 | 78000 | 2.9190 |
| 0.7437 | 4.94 | 79000 | 2.9251 |
| 0.7653 | 5.0 | 80000 | 2.9188 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3
|
XYHY/autonlp-123-478412765
|
XYHY
| 2022-01-06T06:22:38Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:XYHY/autonlp-data-123",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- XYHY/autonlp-data-123
co2_eq_emissions: 69.86520391863117
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.9549699799866577
## 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/XYHY/autonlp-123-478412765
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
ncduy/phobert-large-finetuned-vietnamese_students_feedback
|
ncduy
| 2022-01-06T05:55:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:vietnamese_students_feedback",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- vietnamese_students_feedback
metrics:
- accuracy
model-index:
- name: phobert-large-finetuned-vietnamese_students_feedback
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: vietnamese_students_feedback
type: vietnamese_students_feedback
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9463044851547694
---
<!-- 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. -->
# phobert-large-finetuned-vietnamese_students_feedback
This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on the vietnamese_students_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2285
- Accuracy: 0.9463
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 477 | 0.2088 | 0.9375 |
| 0.3231 | 2.0 | 954 | 0.2463 | 0.9444 |
| 0.1805 | 3.0 | 1431 | 0.2285 | 0.9463 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
NahedAbdelgaber/evaluating-student-writing-distibert-ner
|
NahedAbdelgaber
| 2022-01-06T05:49:02Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: evaluating-student-writing-distibert-ner
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. -->
# evaluating-student-writing-distibert-ner
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: 0.7688
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.871 | 1.0 | 1755 | 0.8158 |
| 0.7476 | 2.0 | 3510 | 0.7688 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
unicamp-dl/mt5-base-en-pt-msmarco-v2
|
unicamp-dl
| 2022-01-05T23:16:47Z | 22 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: pt
license: mit
tags:
- msmarco
- t5
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- msmarco
widget:
- text: "Texto de exemplo em português"
inference: false
---
# mt5-base Reranker finetuned on mMARCO
## Introduction
mT5-base-en-pt-msmarco-v2 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the v2 version, the Portuguese dataset was translated using Google Translate.
Further information about the dataset or the translation method can be found on our paper [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository.
## Usage
```python
from transformers import T5Tokenizer, MT5ForConditionalGeneration
model_name = 'unicamp-dl/mt5-base-en-pt-msmarco-v2'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = MT5ForConditionalGeneration.from_pretrained(model_name)
```
# Citation
If you use mt5-base-en-pt-msmarco-v2, please cite:
@misc{bonifacio2021mmarco,
title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
eprint={2108.13897},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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