modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 06:34:03
| downloads
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| likes
int64 0
11.7k
| library_name
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listlengths 1
4.05k
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DMetaSoul/sbert-chinese-general-v2-distill
|
DMetaSoul
| 2022-04-02T09:58:33Z | 15 | 6 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-04-02T09:58:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好且编码速度更快**。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 6% 左右（具体结果详见下文评估小节）。
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2-distill')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill')
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比：
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 38ms | 20ms | -47% |
| Throughput | 418 sentence/s | 791 sentence/s | 1.9x |
*精度：*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 77.19% | 72.59% | 36.79% | 76.91% | 49.62% | 16.24% | 63.15% | 56.07% |
| **Student** | 76.49% | 73.33% | 26.46% | 64.26% | 46.02% | 11.83% | 52.45% | 50.12% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -5.95% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
|
junnyu/flash_small_wwm_cluecorpussmall
|
junnyu
| 2022-04-02T09:46:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"flash",
"fill-mask",
"license:mit",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T02:59:48Z |
---
license: mit
inference: False
---
# training logs
- https://wandb.ai/junyu/huggingface/runs/1jg2jlgt
# install
- https://github.com/JunnYu/FLASHQuad_pytorch
# usage
```python
import torch
from flash import FLASHForMaskedLM
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model = FLASHForMaskedLM.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model.eval()
text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512, return_token_type_ids=False) #这里必须是512，不然结果可能不对。
with torch.no_grad():
pt_outputs = model(**inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val,idx = pt_outputs[i].softmax(-1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v,t in zip(val.cpu(),tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pt_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
print(pt_outputs_sentence)
# pytorch: 天气预报说今天的天[气+0.994||天+0.0015||空+0.0014||晴+0.0005||阳+0.0003]很好，那么我[们+0.9563||就+0.0381||也+0.0032||俩+0.0004||来+0.0002]一起去公园玩吧！
```
|
Chikashi/t5-small-finetuned-wikihow_3epoch
|
Chikashi
| 2022-04-02T07:42:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T21:20:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 25.5784
---
<!-- 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-wikihow_3epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5163
- Rouge1: 25.5784
- Rouge2: 8.9929
- Rougel: 21.5345
- Rougelsum: 24.9382
- Gen Len: 18.384
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9421 | 0.25 | 5000 | 2.6545 | 23.2336 | 7.5502 | 19.5899 | 22.5521 | 18.4076 |
| 2.8411 | 0.51 | 10000 | 2.6103 | 24.3524 | 8.2068 | 20.5238 | 23.6679 | 18.2606 |
| 2.7983 | 0.76 | 15000 | 2.5836 | 24.8169 | 8.4826 | 20.8765 | 24.1686 | 18.3211 |
| 2.7743 | 1.02 | 20000 | 2.5627 | 24.9904 | 8.5625 | 21.0344 | 24.3416 | 18.3786 |
| 2.7452 | 1.27 | 25000 | 2.5508 | 25.1497 | 8.6872 | 21.152 | 24.4751 | 18.3524 |
| 2.7353 | 1.53 | 30000 | 2.5384 | 25.2909 | 8.7408 | 21.2344 | 24.629 | 18.4453 |
| 2.7261 | 1.78 | 35000 | 2.5322 | 25.3748 | 8.7802 | 21.312 | 24.7191 | 18.3754 |
| 2.7266 | 2.03 | 40000 | 2.5265 | 25.4095 | 8.8915 | 21.3871 | 24.7685 | 18.4013 |
| 2.706 | 2.29 | 45000 | 2.5211 | 25.4372 | 8.8926 | 21.4124 | 24.7902 | 18.3776 |
| 2.7073 | 2.54 | 50000 | 2.5176 | 25.4925 | 8.9668 | 21.5103 | 24.8608 | 18.4303 |
| 2.703 | 2.8 | 55000 | 2.5163 | 25.5784 | 8.9929 | 21.5345 | 24.9382 | 18.384 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Suman123/upside-down-detector
|
Suman123
| 2022-04-02T07:33:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-01T12:56:45Z |
TASK 1 of Faltima Fellowship- UpsideDown detector
|
nikhil6041/wav2vec2-large-xls-r-300m-hindi-colab
|
nikhil6041
| 2022-04-02T06:04:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-02T03:35:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-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-hindi-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.
## 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 1.18.3
- Tokenizers 0.10.3
|
satoshiz01/Flipped_CIFAR10_vision
|
satoshiz01
| 2022-04-02T05:09:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-02T03:30:55Z |
**Google Colab Notebook link:**
https://colab.research.google.com/drive/1iA8nvb93VLcrDfIt17AOIHnkVdLSNcW_?usp=sharing
This repo contains files for defining and creating a simple convolutional network for
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees).
The following files are in this repo:
Coding_Challenge_for_Fatima_Fellowship.ipynb -- a copy of the Google Collab notebook with the code/output/writeup
best_model.pth -- dictionary of best model stats/weights found during training
cifar10flip_trn.pt -- saved training dataset of ~50% flipped CIFAR10 images
cifar10flip_tst.pt -- saved training dataset of ~50% flipped CIFAR10 images
image_examples.png -- an array of example imags from flipped CIFAR10 dataset
write-up -- write up of data processing, model results, and potential improvements (also in Google Colab)
wrong_predictions.zip -- a zip file of PNG images that were incorrectly classified by my model
(each file name provide information on the image's prediction, true label, and its class)
|
huggingtweets/clortown
|
huggingtweets
| 2022-04-02T04:51:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T02:36:56Z |
---
language: en
thumbnail: http://www.huggingtweets.com/clortown/1648875085007/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/1488574779351187458/RlIQNUFG_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">yeosang elf agenda</div>
<div style="text-align: center; font-size: 14px;">@clortown</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 yeosang elf agenda.
| Data | yeosang elf agenda |
| --- | --- |
| Tweets downloaded | 3140 |
| Retweets | 538 |
| Short tweets | 463 |
| Tweets kept | 2139 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cupnlna/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 @clortown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uii743r9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uii743r9/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/clortown')
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)
|
BigSalmon/Points4
|
BigSalmon
| 2022-04-02T03:04:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T02:57:31Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Points4")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points4")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27
Keywords to sentences or sentence.
|
TheJarmanitor/fatima-fellowship-model
|
TheJarmanitor
| 2022-04-02T03:03:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-02T03:01:06Z |
model and notebook for the Fatima Fellowship 2022 coding Challenge
|
youssefadarrab/TP_NLP_SNLI_Adarrab_Baziz_Malige
|
youssefadarrab
| 2022-04-02T00:40:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T21:11:05Z |
# CentraleSupelec - Natural language processing
# Practical session n°7
## Natural Language Inferencing (NLI):
(NLI) is a classical NLP (Natural Language Processing) problem that involves taking two sentences (the premise and the hypothesis ), and deciding how they are related (if the premise *entails* the hypothesis, *contradicts* it, or *neither*).
Ex:
| Premise | Label | Hypothesis |
| --- | --- | --- |
| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
| An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. |
| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |
### Stanford NLI (SNLI) corpus
In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( https://nlp.stanford.edu/projects/snli/ ), available in the *Datasets* library by Huggingface.
from datasets import load_dataset
snli = load_dataset("snli")
#Removing sentence pairs with no label (-1)
snli = snli.filter(lambda example: example['label'] != -1)
## Quick summary of the model
This is the model from : Youssef Adarrab, Othmane Baziz and Alain Malige
- Fist we import the corpus and do some visualization
- Second we apply DistilBert for sequence classification
- We illustrate through our work the code used for training, to obtain better results, one should run the training on more epochs
|
JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en
|
JustAdvanceTechonology
| 2022-04-02T00:07:29Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-31T10:16:30Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en
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. -->
# JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6429
- Validation Loss: 0.8071
- Epoch: 2
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.6423 | 0.8071 | 0 |
| 0.6424 | 0.8071 | 1 |
| 0.6429 | 0.8071 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.5.0
- Datasets 2.0.0
- Tokenizers 0.10.1
|
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese
|
lgris
| 2022-04-01T20:32:58Z | 268 | 9 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: Lucas Gris XLSR Wav2Vec2 Large 53 Brazilian Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER
type: wer
value: 12.905054857823264%
---
# Wav2vec 2.0 With Open Brazilian Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.
- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt) (_only train_): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages to train ASR models. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). The set in Portuguese (mostly Brazilian variant) used in this work is the 6.1 version (pt_63h_2020-12-11) that contains about 50 validated hours and 1,120 unique speakers.
- [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively.
The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/drive/folders/1XTKIUB4kp3oYOavwH97wq8IPFsxP5sNz?usp=sharing).
This model was trained in 80k updates.
#### Datasets in number of instances and number of frames
The following image shows the overall distribution of the dataset:
#### Transcription examples
| Text | Transcription |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| É comum os usuários confundirem software livre com software livre | É comum os __usuares__ __confunder em__ __softwerlivr__ com __softwerlivre__ |
| Ele fez tanto ghostwriting que ele começa a se sentir como um fantasma também | Ele fez tanto __golstraitn__ que ele __começou__ a se sentir como um fantasma também |
| Arnold apresentou um gráfico mostrando quantas cegonhas ele havia contado nos últimos dez anos | Arnold apresentou um gráfico mostrando quantas __segonhas__ ele havia contado nos últimos dez anos |
| Mais cedo ou mais tarde eles descobrirão como ler esses hieróglifos | Mais __sedo__ ou mais tarde eles descobriram como __de__ esses __ierogrôficos__ |
| Viver juntos compartilhar objetivos e ter um bom relacionamento | __E ver__ juntos __signafica__ viver juntos ou __fartlhar__ objetivos ter um bom __relacionamentoo__ |
| Da mesma forma uma patente pode impedir que concorrentes desenvolvam produtos similares | Da mesma forma uma patente pode impedir que concorrentes __desenvolva__ produtos similares |
| Duas mulheres e uma menina levantam com troféus | Duas mulheres e uma menina levantam com __trofés__ |
| Esse acrobata de circo deve ter um sistema vestibular bem treinado pensou o espectador | Esse acrobata de __cirko__ deve ter um sistema vestibular __bemtreinado__ pensou o espectador |
| Durante a exposição o tribunal pode fazer quaisquer perguntas ou esclarecimentos que considere apropriados | Durante a exposição o tribunal pode fazer quaisquer perguntas ou esclarecimentos que considere __apropriado__ |
## Imports and dependencies
```python
%%capture
!pip install datasets
!pip install jiwer
!pip install torchaudio
!pip install transformers
!pip install soundfile
```
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
```
## Preparation
```python
chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605
wer = load_metric("wer")
device = "cuda"
```
```python
model_name = 'lgris/wav2vec2-large-xlsr-open-brazilian-portuguese'
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2Processor.from_pretrained(model_name)
```
```python
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
batch["predicted"] = [pred.lower() for pred in batch["predicted"]]
batch["target"] = batch["sentence"]
return batch
```
## Tests
### Test against Common Voice (In-domain)
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
for pred, target in zip(result["predicted"][:10], result["target"][:10]):
print(pred, "|", target)
```
0.12905054857823264
nem o varanin os altros influmindo os de teterno um bombederster | nem o radar nem os outros instrumentos detectaram o bombardeiro stealth
pedir dinheiro é emprestado das pessoas do aldeia | pedir dinheiro emprestado às pessoas da aldeia
oito | oito
teno calcos | trancá-los
realizaram a investigação para resolver o problema | realizar uma investigação para resolver o problema
iotube ainda é a melhor plataforma de vídeos | o youtube ainda é a melhor plataforma de vídeos
menina e menino beijando nas sombras | menina e menino beijando nas sombras
eu sou o senhor | eu sou o senhor
duas metcas sentam-se para baixo randes jornais | duas mulheres que sentam-se para baixo lendo jornais
eu originalmente esperava | eu originalmente esperava
**Result**: 12.90%
### Test against [TEDx](http://www.openslr.org/100/) (Out-of-domain)
```python
!gdown --id 1HJEnvthaGYwcV_whHEywgH2daIN4bQna
!tar -xf tedx.tar.gz
```
```python
dataset = load_dataset('csv', data_files={'test': 'tedx/test.csv'})['test']
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = speech.squeeze(0).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
for pred, target in zip(result["predicted"][:10], result["target"][:10]):
print(pred, "|", target)
```
0.35215851987208774
com isso a gente vê que essa rede de pactuação de de deparcerias nos remete a um raciocínio lógico que ao que a gente crê que é a prevenção | com isso a gente vê que essa rede de pactuação de parcerias nos remete a um raciocínio lógico que é o que a gente crê que é a prevenção
ente vai para o resultado | e aí a gente vai pro resultado
curiosidade hé o que eu descobri desde que comecei a fazer pesquisa lá no ensino médio | e a curiosidade é algo que descobri desde que comecei a fazer pesquisa lá no ensino médio
val des quemesho | há vários caminhos
que é uma opcissão por comer soldado | que é uma obsessão por comer saudável
isso é tão é forte algoltão universal que existem dados que mostram que setenta e cinco por cento das reuniões são dominadas pela voz masculina | e isso é tão forte é algo tão universal que existem dados que mostram que das reuniões são dominadas pela voz masculina
não era exatamente isso não estávamos deveto | e não era exatamente isso que nós estávamos a ver
durante meci do médio ofiz pesquisa estudei numa escola que chamam a fundação liberate ficava relativamente próximo daqui | durante o ensino médio eu fiz pesquisa estudei numa escola que se chama fundação liberato que fica relativamente próxima daqui
oito anos atrás eu fui apresentado por uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçamos | oito anos atrás fui apresentado para uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçamos
o terceiro é o museu do ripiopeco | o terceiro é o museu do hip hop
**Result**: 35.21%
|
lgris/bp400-xlsr
|
lgris
| 2022-04-01T20:31:02Z | 91 | 3 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"arxiv:2107.11414",
"arxiv:2012.03411",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: bp400-xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 14.0
license: apache-2.0
---
# bp400-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
**Paper:** https://arxiv.org/abs/2107.11414
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.
- [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt).
- [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
- [Multilingual TEDx](http://www.openslr.org/100): a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech.
- [Sidney](https://igormq.github.io/datasets/) (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation;
- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets.
| Dataset | Train | Valid | Test |
|--------------------------------|-------:|------:|------:|
| CETUC | 93.9h | -- | 5.4h |
| Common Voice | 37.6h | 8.9h | 9.5h |
| LaPS BM | 0.8h | -- | 0.1h |
| MLS | 161.0h | -- | 3.7h |
| Multilingual TEDx (Portuguese) | 144.2h | -- | 1.8h |
| SID | 5.0h | -- | 1.0h |
| VoxForge | 2.8h | -- | 0.1h |
| Total | 437.2h | 8.9h | 21.6h |
The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/drive/folders/1eRUExXRF2XK8JxUjIzbLBkLa5wuR3nig?usp=sharing).
#### Summary
| | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG |
|----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|
| bp\_400 (demonstration below) | 0.052 | 0.140 | 0.074 | 0.117 | 0.121 | 0.245 | 0.118 | 0.124 |
| bp\_400 + 3-gram | 0.033 | 0.095 | 0.046 | 0.123 | 0.112 | 0.212 | 0.123 | 0.106 |
| bp\_400 + 4-gram (demonstration below) | **0.030** | 0.096 | 0.043 | **0.106** | 0.118 | 0.229 | **0.117** | **0.105** |
| bp\_400 + 5-gram | 0.033 | 0.094 | 0.043 | 0.123 | **0.111** | **0.210** | 0.123 | **0.105** |
| bp\_400 + Transf. | 0.032 | **0.092** | **0.036** | 0.130 | 0.115 | 0.215 | 0.125 | 0.106 |
#### Transcription examples
| Text | Transcription |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
|alguém sabe a que horas começa o jantar | alguém sabe a que horas **começo** jantar |
|lila covas ainda não sabe o que vai fazer no fundo|**lilacovas** ainda não sabe o que vai fazer no fundo|
|que tal um pouco desse bom spaghetti|**quetá** um pouco **deste** bom **ispaguete**|
|hong kong em cantonês significa porto perfumado|**rongkong** **en** **cantones** significa porto perfumado|
|vamos hackear esse problema|vamos **rackar** esse problema|
|apenas a poucos metros há uma estação de ônibus|apenas **ha** poucos metros **á** uma estação de ônibus|
|relâmpago e trovão sempre andam juntos|**relampagotrevão** sempre andam juntos|
## Demonstration
```python
MODEL_NAME = "lgris/bp400-xlsr"
```
### Imports and dependencies
```python
%%capture
!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install datasets
!pip install jiwer
!pip install transformers
!pip install soundfile
!pip install pyctcdecode
!pip install https://github.com/kpu/kenlm/archive/master.zip
```
```python
import jiwer
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
from pyctcdecode import build_ctcdecoder
import torch
import re
import sys
```
### Helpers
```python
chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = speech.squeeze(0).numpy()
batch["sampling_rate"] = 16_000
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
batch["target"] = batch["sentence"]
return batch
```
```python
def calc_metrics(truths, hypos):
wers = []
mers = []
wils = []
for t, h in zip(truths, hypos):
try:
wers.append(jiwer.wer(t, h))
mers.append(jiwer.mer(t, h))
wils.append(jiwer.wil(t, h))
except: # Empty string?
pass
wer = sum(wers)/len(wers)
mer = sum(mers)/len(mers)
wil = sum(wils)/len(wils)
return wer, mer, wil
```
```python
def load_data(dataset):
data_files = {'test': f'{dataset}/test.csv'}
dataset = load_dataset('csv', data_files=data_files)["test"]
return dataset.map(map_to_array)
```
### Model
```python
class STT:
def __init__(self,
model_name,
device='cuda' if torch.cuda.is_available() else 'cpu',
lm=None):
self.model_name = model_name
self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
self.processor = Wav2Vec2Processor.from_pretrained(model_name)
self.vocab_dict = self.processor.tokenizer.get_vocab()
self.sorted_dict = {
k.lower(): v for k, v in sorted(self.vocab_dict.items(),
key=lambda item: item[1])
}
self.device = device
self.lm = lm
if self.lm:
self.lm_decoder = build_ctcdecoder(
list(self.sorted_dict.keys()),
self.lm
)
def batch_predict(self, batch):
features = self.processor(batch["speech"],
sampling_rate=batch["sampling_rate"][0],
padding=True,
return_tensors="pt")
input_values = features.input_values.to(self.device)
attention_mask = features.attention_mask.to(self.device)
with torch.no_grad():
logits = self.model(input_values, attention_mask=attention_mask).logits
if self.lm:
logits = logits.cpu().numpy()
batch["predicted"] = []
for sample_logits in logits:
batch["predicted"].append(self.lm_decoder.decode(sample_logits))
else:
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = self.processor.batch_decode(pred_ids)
return batch
```
### Download datasets
```python
%%capture
!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI
!mkdir bp_dataset
!unzip bp_dataset -d bp_dataset/
```
### Tests
```python
stt = STT(MODEL_NAME)
```
#### CETUC
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.05159104708285062
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.14031426198658084
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.07432133838383838
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.11678793514817509
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.12152357273433984
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.24666815906766504
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.11873106060606062
### Tests with LM
```python
!rm -rf ~/.cache
!gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia
stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa')
# !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp
# stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa')
```
### Cetuc
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.030266462438593742
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.09577710237417715
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.043617424242424235
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.10642133314350002
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.11839021001747055
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.22929952467810416
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.11716314935064935
|
birgermoell/psst-libri960_big
|
birgermoell
| 2022-04-01T20:17:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-01T19:05:31Z |
pssteval INFO: ASR metrics for split `valid` FER: 9.8% PER: 20.9%
|
juaner/distilbert-base-uncased-finetuned-cola
|
juaner
| 2022-04-01T18:20:42Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T17:59:52Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juaner/distilbert-base-uncased-finetuned-cola
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. -->
# juaner/distilbert-base-uncased-finetuned-cola
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: 0.1909
- Validation Loss: 0.5553
- Train Matthews Correlation: 0.5279
- Epoch: 2
## 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': 2670, '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 | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5191 | 0.4491 | 0.4718 | 0 |
| 0.3270 | 0.4571 | 0.5196 | 1 |
| 0.1909 | 0.5553 | 0.5279 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
FrankCorrigan/results
|
FrankCorrigan
| 2022-04-01T18:15:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T01:41:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0158
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 0.9563 |
| No log | 2.0 | 2 | 0.9877 |
| No log | 3.0 | 3 | 1.0158 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
FrankCorrigan/test-model
|
FrankCorrigan
| 2022-04-01T17:54:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-01T01:46:45Z |
---
license: apache-2.0
---
|
McGill-NLP/bart-qg-nq-checkpoint
|
McGill-NLP
| 2022-04-01T17:35:04Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T16:32:49Z |
---
license: cc-by-4.0
---
# BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) fine-tuned on [Google NaturalQuestions](https://ai.google.com/research/NaturalQuestions/) for **Question Generation** by treating long answer as input, and question as output.
## Details of BART
The **BART** model was presented in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by *Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer* in Here the abstract:
We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.
## Details of the downstream task (QG) - Dataset 📚 🧐
Dataset: ```NaturalQuestions``` from Google (https://ai.google.com/research/NaturalQuestions/)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| NaturalQuestions | train | 97650 |
| NaturalQuestions | valid | 10850 |
## Model fine-tuning 🏋️
The training script can be found [here](https://github.com/McGill-NLP/MLQuestions/blob/main/QG/train.py)
## Model in Action 🚀
```python
from transformers import AutoModel, BartTokenizer
#Load the tokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
#Load the model
model = AutoModelForSeq2SeqLM.from_pretrained("McGill-NLP/bart-qg-nq-checkpoint")
```
## Citation
If you want to cite this model you can use this:
```bibtex
@inproceedings{kulshreshtha-etal-2021-back,
title = "Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval",
author = "Kulshreshtha, Devang and
Belfer, Robert and
Serban, Iulian Vlad and
Reddy, Siva",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.566",
pages = "7064--7078",
abstract = "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.",
}
```
> Created by [Devang Kulshreshtha](https://geekydevu.netlify.app/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
ahmedzaky91/Fatima-Fake_news_calssifier
|
ahmedzaky91
| 2022-04-01T16:54:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-01T00:00:39Z |
## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle
## The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
num_epochs: 2
|
vicl/canine-c-finetuned-mrpc
|
vicl
| 2022-04-01T16:33:28Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T16:05:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: canine-c-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8627450980392157
- name: F1
type: f1
value: 0.9014084507042254
---
<!-- 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. -->
# canine-c-finetuned-mrpc
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4066
- Accuracy: 0.8627
- F1: 0.9014
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.5014 | 0.7696 | 0.8479 |
| No log | 2.0 | 460 | 0.4755 | 0.7892 | 0.8622 |
| 0.5096 | 3.0 | 690 | 0.3645 | 0.8431 | 0.8869 |
| 0.5096 | 4.0 | 920 | 0.4066 | 0.8627 | 0.9014 |
| 0.2619 | 5.0 | 1150 | 0.4551 | 0.8431 | 0.8877 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
avialfont/ner-dummy-model
|
avialfont
| 2022-04-01T14:59:22Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-01T10:59:27Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ner-dummy-model
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. -->
# ner-dummy-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
eren23/pneumonia_test_attempt
|
eren23
| 2022-04-01T14:41:01Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-19T16:31:28Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pneumonia_test_attempt
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9783163070678711
---
# pneumonia-bielefeld-dl-course
This registry contains the model for making pneumonia predictions and was prepared for Bielefeld University Deep Learning course homework.
The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lightning for another dataset.
|
notexist/ttt
|
notexist
| 2022-04-01T13:16:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-01T12:45:30Z |
---
license: apache-2.0
---
|
bharatR/up_down
|
bharatR
| 2022-04-01T12:38:05Z | 0 | 0 | null |
[
"classification",
"en",
"dataset:cifar10-custom",
"region:us"
] | null | 2022-04-01T12:19:00Z |
---
language: en
tags:
- classification
datasets:
- cifar10-custom
metrics:
- accuracy
---
# Up-Down Classification
This repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task).
|
birgermoell/psst-base-rep
|
birgermoell
| 2022-04-01T12:02:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-01T07:58:20Z |
The model is a reproduction of the baseline trained with Wav2vec2-small on PSST
pssteval INFO: ASR metrics for split `valid` FER: 10.4% PER: 23.1%
|
xxr/bert-base-uncased-multi-128
|
xxr
| 2022-04-01T11:40:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-01T05:36:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-uncased-multi-128
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-multi-128
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: 2.7101
## 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.6636 | 1.0 | 812 | 3.2325 |
| 3.2963 | 2.0 | 1624 | 3.1937 |
| 3.1132 | 3.0 | 2436 | 3.2984 |
| 2.9386 | 4.0 | 3248 | 3.2430 |
| 2.7742 | 5.0 | 4060 | 3.1272 |
| 2.5954 | 6.0 | 4872 | 3.1778 |
| 2.501 | 7.0 | 5684 | 3.1649 |
| 2.4073 | 8.0 | 6496 | 2.9395 |
| 2.2933 | 9.0 | 7308 | 3.1262 |
| 2.2218 | 10.0 | 8120 | 2.9994 |
| 2.1558 | 11.0 | 8932 | 2.9922 |
| 2.0873 | 12.0 | 9744 | 2.8414 |
| 2.0104 | 13.0 | 10556 | 2.9351 |
| 1.9364 | 14.0 | 11368 | 2.9253 |
| 1.9045 | 15.0 | 12180 | 2.8701 |
| 1.9152 | 16.0 | 12992 | 2.7101 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
z5ying/distilgpt2-finetuned-wikitext2
|
z5ying
| 2022-04-01T10:47:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-01T07:10:02Z |
---
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 [z5ying/distilgpt2-finetuned-wikitext2](https://huggingface.co/z5ying/distilgpt2-finetuned-wikitext2) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 118 | 3.0306 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
|
osanseviero/llama-alpaca-snake
|
osanseviero
| 2022-04-01T09:45:01Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-01T09:20:01Z |
---
tags:
- image-classification
- pytorch
- huggingpics
- llama-leaderboard
metrics:
- accuracy
model-index:
- name: llama-alpaca-snake
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7910447716712952
---
# llama-alpaca-snake
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
#### alpaca
#### llamas
#### snake
|
abdusah/aradia-ctc-data2vec-ft
|
abdusah
| 2022-04-01T08:19:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-31T14:34:56Z |
---
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_300hrs
- generated_from_trainer
model-index:
- name: aradia-ctc-data2vec-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. -->
# aradia-ctc-data2vec-ft
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0464
- Wer: 1.0
## 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:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log | 0.43 | 100 | 3.3600 | 1.0 |
| No log | 0.87 | 200 | 3.0887 | 1.0 |
| No log | 1.3 | 300 | 3.0779 | 1.0 |
| No log | 1.74 | 400 | 3.0551 | 1.0 |
| 4.8553 | 2.17 | 500 | 3.0526 | 1.0 |
| 4.8553 | 2.61 | 600 | 3.0560 | 1.0 |
| 4.8553 | 3.04 | 700 | 3.1251 | 1.0 |
| 4.8553 | 3.48 | 800 | 3.0870 | 1.0 |
| 4.8553 | 3.91 | 900 | 3.0822 | 1.0 |
| 3.1133 | 4.35 | 1000 | 3.0484 | 1.0 |
| 3.1133 | 4.78 | 1100 | 3.0558 | 1.0 |
| 3.1133 | 5.22 | 1200 | 3.1019 | 1.0 |
| 3.1133 | 5.65 | 1300 | 3.0914 | 1.0 |
| 3.1133 | 6.09 | 1400 | 3.0691 | 1.0 |
| 3.109 | 6.52 | 1500 | 3.0589 | 1.0 |
| 3.109 | 6.95 | 1600 | 3.0508 | 1.0 |
| 3.109 | 7.39 | 1700 | 3.0540 | 1.0 |
| 3.109 | 7.82 | 1800 | 3.0546 | 1.0 |
| 3.109 | 8.26 | 1900 | 3.0524 | 1.0 |
| 3.1106 | 8.69 | 2000 | 3.0569 | 1.0 |
| 3.1106 | 9.13 | 2100 | 3.0622 | 1.0 |
| 3.1106 | 9.56 | 2200 | 3.0518 | 1.0 |
| 3.1106 | 10.0 | 2300 | 3.0749 | 1.0 |
| 3.1106 | 10.43 | 2400 | 3.0698 | 1.0 |
| 3.1058 | 10.87 | 2500 | 3.0665 | 1.0 |
| 3.1058 | 11.3 | 2600 | 3.0555 | 1.0 |
| 3.1058 | 11.74 | 2700 | 3.0589 | 1.0 |
| 3.1058 | 12.17 | 2800 | 3.0611 | 1.0 |
| 3.1058 | 12.61 | 2900 | 3.0561 | 1.0 |
| 3.1071 | 13.04 | 3000 | 3.0480 | 1.0 |
| 3.1071 | 13.48 | 3100 | 3.0492 | 1.0 |
| 3.1071 | 13.91 | 3200 | 3.0574 | 1.0 |
| 3.1071 | 14.35 | 3300 | 3.0538 | 1.0 |
| 3.1071 | 14.78 | 3400 | 3.0505 | 1.0 |
| 3.1061 | 15.22 | 3500 | 3.0600 | 1.0 |
| 3.1061 | 15.65 | 3600 | 3.0596 | 1.0 |
| 3.1061 | 16.09 | 3700 | 3.0623 | 1.0 |
| 3.1061 | 16.52 | 3800 | 3.0800 | 1.0 |
| 3.1061 | 16.95 | 3900 | 3.0583 | 1.0 |
| 3.1036 | 17.39 | 4000 | 3.0534 | 1.0 |
| 3.1036 | 17.82 | 4100 | 3.0563 | 1.0 |
| 3.1036 | 18.26 | 4200 | 3.0481 | 1.0 |
| 3.1036 | 18.69 | 4300 | 3.0477 | 1.0 |
| 3.1036 | 19.13 | 4400 | 3.0505 | 1.0 |
| 3.1086 | 19.56 | 4500 | 3.0485 | 1.0 |
| 3.1086 | 20.0 | 4600 | 3.0481 | 1.0 |
| 3.1086 | 20.43 | 4700 | 3.0615 | 1.0 |
| 3.1086 | 20.87 | 4800 | 3.0658 | 1.0 |
| 3.1086 | 21.3 | 4900 | 3.0505 | 1.0 |
| 3.1028 | 21.74 | 5000 | 3.0492 | 1.0 |
| 3.1028 | 22.17 | 5100 | 3.0485 | 1.0 |
| 3.1028 | 22.61 | 5200 | 3.0483 | 1.0 |
| 3.1028 | 23.04 | 5300 | 3.0479 | 1.0 |
| 3.1028 | 23.48 | 5400 | 3.0509 | 1.0 |
| 3.1087 | 23.91 | 5500 | 3.0530 | 1.0 |
| 3.1087 | 24.35 | 5600 | 3.0486 | 1.0 |
| 3.1087 | 24.78 | 5700 | 3.0514 | 1.0 |
| 3.1087 | 25.22 | 5800 | 3.0505 | 1.0 |
| 3.1087 | 25.65 | 5900 | 3.0508 | 1.0 |
| 3.1043 | 26.09 | 6000 | 3.0501 | 1.0 |
| 3.1043 | 26.52 | 6100 | 3.0467 | 1.0 |
| 3.1043 | 26.95 | 6200 | 3.0466 | 1.0 |
| 3.1043 | 27.39 | 6300 | 3.0465 | 1.0 |
| 3.1043 | 27.82 | 6400 | 3.0465 | 1.0 |
| 3.1175 | 28.26 | 6500 | 3.0466 | 1.0 |
| 3.1175 | 28.69 | 6600 | 3.0466 | 1.0 |
| 3.1175 | 29.13 | 6700 | 3.0465 | 1.0 |
| 3.1175 | 29.56 | 6800 | 3.0465 | 1.0 |
| 3.1175 | 30.0 | 6900 | 3.0464 | 1.0 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
jkhan447/sentiment-model-sample-27go-emotion
|
jkhan447
| 2022-04-01T08:13:56Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:go_emotions",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T06:05:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- accuracy
model-index:
- name: sentiment-model-sample-27go-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: go_emotions
type: go_emotions
args: simplified
metrics:
- name: Accuracy
type: accuracy
value: 0.5888888888888889
---
<!-- 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. -->
# sentiment-model-sample-27go-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1765
- Accuracy: 0.5889
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
|
z5ying/mbart-large-cc25-finetuned-source-to-target
|
z5ying
| 2022-04-01T03:43:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T18:25:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: mbart-large-cc25-finetuned-source-to-target
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. -->
# mbart-large-cc25-finetuned-source-to-target
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) 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: 0.002
- train_batch_size: 10
- eval_batch_size: 10
- 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
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
|
dchung117/distilbert-base-uncased-finetuned-squad-d5716d28
|
dchung117
| 2022-04-01T02:02:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-01T01:51:41Z |
---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Mr-Wick/xlnet-base-cased
|
Mr-Wick
| 2022-04-01T01:31:59Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"xlnet",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-26T12:52:07Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: xlnet-base-cased
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. -->
# xlnet-base-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 16530, '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
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.0
|
anisdismail/celebA-orientation-detection
|
anisdismail
| 2022-03-31T21:51:37Z | 0 | 2 | null |
[
"image-classification",
"pytorch",
"en",
"dataset:nielsr/CelebA-faces",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] |
image-classification
| 2022-03-31T19:48:26Z |
---
language:
- en
license: cc-by-nc-4.0
tags:
- image-classification
- pytorch
datasets:
- nielsr/CelebA-faces
model-index:
- name: celebA_orientation_detection_model
results:
- task:
type: image_classification # Required. Example: automatic-speech-recognition
name: Image Classification # Optional. Example: Speech Recognition
dataset:
type: nielsr/CelebA-faces
name: CelebA-faces
metrics:
- type: f1score # Required. Example: wer
value: 0.97 # Required. Example: 20.90
name: Val F1 Score # Optional. Example: Test WER
---
## Detecting the Orientation of CelebA pictures using Deep Learning
This model has been trained on a modified version of the CelebA-faces dataset, which was made from flipping 20,000 images upside down and keeping 20,000 images intact.<br>
The model relies on Resnet-18 as a backbone and is connected to one output node to classify whether the images are flipped upside down (1) or not (0).
|
arjundd/dosma-models
|
arjundd
| 2022-03-31T21:39:54Z | 0 | 0 | null |
[
"mri",
"knee",
"segmentation",
"en",
"region:us"
] | null | 2022-03-31T18:30:03Z |
---
language: en
tags:
- mri
- knee
- segmentation
---
# DOSMA models
These models are those that are made publicly available in the [DOSMA](https://github.com/ad12/DOSMA).
More information on these models can be found in the [documentation](https://dosma.readthedocs.io/en/latest/models.html).
## Citation
If you use any models, please cite any reference for the model in addition to the DOSMA reference below:
```
@inproceedings{desai2019dosma,
title={DOSMA: A deep-learning, open-source framework for musculoskeletal MRI analysis},
author={Desai, Arjun D and Barbieri, Marco and Mazzoli, Valentina and Rubin, Elka and Black, Marianne S and Watkins, Lauren E and Gold, Garry E and Hargreaves, Brian A and Chaudhari, Akshay S},
booktitle={Proc 27th Annual Meeting ISMRM, Montreal},
pages={1135},
year={2019}
}
```
|
abdusah/aradia-ctc-hubert-ft
|
abdusah
| 2022-03-31T20:56:27Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-31T08:14:31Z |
---
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_300hrs
- generated_from_trainer
model-index:
- name: aradia-ctc-hubert-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. -->
# aradia-ctc-hubert-ft
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-hubert-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8536
- Wer: 0.3737
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.43 | 100 | 3.6934 | 1.0 |
| No log | 0.87 | 200 | 3.0763 | 1.0 |
| No log | 1.3 | 300 | 2.9737 | 1.0 |
| No log | 1.74 | 400 | 2.5734 | 1.0 |
| 5.0957 | 2.17 | 500 | 1.1900 | 0.9011 |
| 5.0957 | 2.61 | 600 | 0.9726 | 0.7572 |
| 5.0957 | 3.04 | 700 | 0.8960 | 0.6209 |
| 5.0957 | 3.48 | 800 | 0.7851 | 0.5515 |
| 5.0957 | 3.91 | 900 | 0.7271 | 0.5115 |
| 1.0312 | 4.35 | 1000 | 0.7053 | 0.4955 |
| 1.0312 | 4.78 | 1100 | 0.6823 | 0.4737 |
| 1.0312 | 5.22 | 1200 | 0.6768 | 0.4595 |
| 1.0312 | 5.65 | 1300 | 0.6635 | 0.4488 |
| 1.0312 | 6.09 | 1400 | 0.6602 | 0.4390 |
| 0.6815 | 6.52 | 1500 | 0.6464 | 0.4310 |
| 0.6815 | 6.95 | 1600 | 0.6455 | 0.4394 |
| 0.6815 | 7.39 | 1700 | 0.6630 | 0.4312 |
| 0.6815 | 7.82 | 1800 | 0.6521 | 0.4126 |
| 0.6815 | 8.26 | 1900 | 0.6282 | 0.4284 |
| 0.544 | 8.69 | 2000 | 0.6248 | 0.4178 |
| 0.544 | 9.13 | 2100 | 0.6510 | 0.4104 |
| 0.544 | 9.56 | 2200 | 0.6527 | 0.4013 |
| 0.544 | 10.0 | 2300 | 0.6511 | 0.4064 |
| 0.544 | 10.43 | 2400 | 0.6734 | 0.4061 |
| 0.4478 | 10.87 | 2500 | 0.6756 | 0.4145 |
| 0.4478 | 11.3 | 2600 | 0.6727 | 0.3990 |
| 0.4478 | 11.74 | 2700 | 0.6619 | 0.4007 |
| 0.4478 | 12.17 | 2800 | 0.6614 | 0.4019 |
| 0.4478 | 12.61 | 2900 | 0.6695 | 0.4004 |
| 0.3919 | 13.04 | 3000 | 0.6778 | 0.3966 |
| 0.3919 | 13.48 | 3100 | 0.6872 | 0.3971 |
| 0.3919 | 13.91 | 3200 | 0.6882 | 0.3945 |
| 0.3919 | 14.35 | 3300 | 0.7177 | 0.4010 |
| 0.3919 | 14.78 | 3400 | 0.6888 | 0.4043 |
| 0.3767 | 15.22 | 3500 | 0.7124 | 0.4202 |
| 0.3767 | 15.65 | 3600 | 0.7276 | 0.4120 |
| 0.3767 | 16.09 | 3700 | 0.7265 | 0.4034 |
| 0.3767 | 16.52 | 3800 | 0.7392 | 0.4077 |
| 0.3767 | 16.95 | 3900 | 0.7403 | 0.3965 |
| 0.3603 | 17.39 | 4000 | 0.7445 | 0.4016 |
| 0.3603 | 17.82 | 4100 | 0.7579 | 0.4012 |
| 0.3603 | 18.26 | 4200 | 0.7225 | 0.3963 |
| 0.3603 | 18.69 | 4300 | 0.7355 | 0.3951 |
| 0.3603 | 19.13 | 4400 | 0.7482 | 0.3925 |
| 0.3153 | 19.56 | 4500 | 0.7723 | 0.3972 |
| 0.3153 | 20.0 | 4600 | 0.7469 | 0.3898 |
| 0.3153 | 20.43 | 4700 | 0.7800 | 0.3944 |
| 0.3153 | 20.87 | 4800 | 0.7827 | 0.3897 |
| 0.3153 | 21.3 | 4900 | 0.7935 | 0.3914 |
| 0.286 | 21.74 | 5000 | 0.7984 | 0.3750 |
| 0.286 | 22.17 | 5100 | 0.7945 | 0.3830 |
| 0.286 | 22.61 | 5200 | 0.8011 | 0.3775 |
| 0.286 | 23.04 | 5300 | 0.7978 | 0.3824 |
| 0.286 | 23.48 | 5400 | 0.8161 | 0.3833 |
| 0.2615 | 23.91 | 5500 | 0.7823 | 0.3858 |
| 0.2615 | 24.35 | 5600 | 0.8312 | 0.3863 |
| 0.2615 | 24.78 | 5700 | 0.8427 | 0.3819 |
| 0.2615 | 25.22 | 5800 | 0.8432 | 0.3802 |
| 0.2615 | 25.65 | 5900 | 0.8286 | 0.3794 |
| 0.2408 | 26.09 | 6000 | 0.8224 | 0.3824 |
| 0.2408 | 26.52 | 6100 | 0.8228 | 0.3823 |
| 0.2408 | 26.95 | 6200 | 0.8324 | 0.3795 |
| 0.2408 | 27.39 | 6300 | 0.8564 | 0.3744 |
| 0.2408 | 27.82 | 6400 | 0.8629 | 0.3774 |
| 0.2254 | 28.26 | 6500 | 0.8545 | 0.3778 |
| 0.2254 | 28.69 | 6600 | 0.8492 | 0.3767 |
| 0.2254 | 29.13 | 6700 | 0.8511 | 0.3751 |
| 0.2254 | 29.56 | 6800 | 0.8491 | 0.3753 |
| 0.2254 | 30.0 | 6900 | 0.8536 | 0.3737 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
ghees/FatimeFellowship
|
ghees
| 2022-03-31T20:47:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-31T20:45:21Z |
Preprocessing before feeding to model
```
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device='cuda')
...
embeddings = model.encode([text])
return embeddings[0]
```
|
osanseviero/test_model_bertmesh
|
osanseviero
| 2022-03-31T20:35:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T19:47:46Z |
---
license: apache-2.0
---
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.
# Model description
The model is inspired from [BertMesh](https://pubmed.ncbi.nlm.nih.gov/32976559/) which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.
WellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is [PubMedBert](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.
We train the model using data from the [BioASQ](http://bioasq.org) competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs.
The model achieves 63% micro f1 with a 0.5 threshold for all labels.
The code for developing the model is open source and can be found in https://github.com/wellcometrust/grants_tagger
# How to use
⚠️ You need transformers 4.17+ for the example to work due to its recent support for custom models.
You can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass `trust_remote_code=True`. You can get access to the probabilities for all labels by omitting `return_labels=True`.
```
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Wellcome/WellcomeBertMesh"
)
model = AutoModel.from_pretrained(
"Wellcome/WellcomeBertMesh",
trust_remote_code=True
)
text = "This grant is about malaria and not about HIV."
inputs = tokenizer([text], padding="max_length")
labels = model(**inputs, return_labels=True)
print(labels)
```
You can inspect the model code if you navigate to the files and see `model.py`.
|
arampacha/gpt-neo-therapist-small
|
arampacha
| 2022-03-31T20:34:26Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"onnx",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T08:40:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: gpt-neo-therapist-small
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. -->
# gpt-neo-therapist-small
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6731
- Rouge1: 39.5028
- Rouge2: 6.43
- Rougel: 24.0091
- Rougelsum: 35.4481
- Gen Len: 204.1329
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 24
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 9.9955 | 0.97 | 7 | 6.8195 | 18.6047 | 1.0194 | 14.8565 | 17.9774 | 212.0983 |
| 6.9729 | 1.97 | 14 | 5.6783 | 26.3789 | 3.0779 | 18.5195 | 24.8592 | 203.0925 |
| 5.2614 | 2.97 | 21 | 5.0506 | 34.9428 | 4.921 | 21.9741 | 32.1122 | 206.2775 |
| 5.0599 | 3.97 | 28 | 4.7372 | 38.5235 | 6.2251 | 23.5923 | 34.5633 | 204.2428 |
| 4.5479 | 4.97 | 35 | 4.6731 | 39.5028 | 6.43 | 24.0091 | 35.4481 | 204.1329 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
WENGSYX/Deberta-Chinese-Large
|
WENGSYX
| 2022-03-31T20:08:59Z | 56 | 16 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Deberta-Chinese
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
| 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 |
| --------------------- | ------ | --------- | ------ | ------ | ---- | ------ |
| Deberta-Chinese-Large | 1e-5 | 512 | 2*3090 | 200G | 14天 | AdamW |
### 加载与使用
依托于huggingface-transformers
```
tokenizer = BertTokenizer.from_pretrained("WENGSYX/Deberta-Chinese-Large")
model = AutoModel.from_pretrained("WENGSYX/Deberta-Chinese-Large")
```
#### 注意，请使用BertTokenizer加载中文词表
|
huggingtweets/stillconor
|
huggingtweets
| 2022-03-31T17:49:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-31T16:59:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/stillconor/1648748939988/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/1485398297984389121/DmUfFheN_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">conor</div>
<div style="text-align: center; font-size: 14px;">@stillconor</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 conor.
| Data | conor |
| --- | --- |
| Tweets downloaded | 3199 |
| Retweets | 102 |
| Short tweets | 432 |
| Tweets kept | 2665 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z83yigq/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 @stillconor's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30hsnorw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30hsnorw/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/stillconor')
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)
|
israfelsr/UpsideDownClassifier
|
israfelsr
| 2022-03-31T17:06:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-31T15:41:33Z |
# UpsideDownClassifier
This classifier was trained using the [auto-cats-and-dogs](https://huggingface.co/datasets/nateraw/auto-cats-and-dogs) dataset. It was trained over 5 epochs using a pretrained resent18.
The configuration for the model was
```
config = {
"batch_size": 64,
"num_epochs": 5,
"lr": 0.005,
"betas": (0.9, 0.999),
"eps": 1e-6,
"lr": 8e-3,
"do_eval": True
}
```
## Traning Plots
We can see in the figures below the training plots for accuracy and the loss in both, training and validation sets.
### Accuracy Plot
### Loss Plot
## Some Results
Evaluating on the Test Set, we obtain:
- Accuracy = 0.9696
A batch with some missclassifications can be seen in the picture below.
|
huggingtweets/youtube
|
huggingtweets
| 2022-03-31T14:06:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-31T14:05:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/youtube/1648735587597/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/1427292844612595720/RC1YSvuT_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">YouTube</div>
<div style="text-align: center; font-size: 14px;">@youtube</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 YouTube.
| Data | YouTube |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 23 |
| Short tweets | 104 |
| Tweets kept | 3123 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dx34obn/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 @youtube's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3/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/youtube')
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)
|
chrisjay/fonxlsr
|
chrisjay
| 2022-03-31T13:35:06Z | 40 | 7 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"fon",
"dataset:fon_dataset",
"arxiv:2103.07762",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: fon
datasets:
- fon_dataset
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: Fon XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: fon
type: fon_dataset
args: fon
metrics:
- name: Test WER
type: wer
value: 14.97
---
# Wav2Vec2-Large-XLSR-53-Fon
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import json
import random
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
#Load test_dataset from saved files in folder
from datasets import load_dataset, load_metric
#for test
for root, dirs, files in os.walk(test/):
test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
#Remove unnecessary chars
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
def remove_special_characters(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
#No need for resampling because audio dataset already at 16kHz
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"]=speech_array.squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on our unique Fon test data.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
for root, dirs, files in os.walk(test/):
test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model.to("cuda")
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array[0].numpy()
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
#Evaluation on test dataset
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 14.97 %
## Training
The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples).
The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing)
# Collaborators on this project
- Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|(chris.emezue@gmail.com)
- Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|(femipancrace.dossou@gmail.com)
## This is a joint project continuing our research on [OkwuGbé: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762)
|
scasutt/wav2vec2-base_toy_train_data_slow_10pct
|
scasutt
| 2022-03-31T13:12:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-27T02:28:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base_toy_train_data_slow_10pct
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_toy_train_data_slow_10pct
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: 1.3248
- Wer: 0.7175
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0663 | 2.1 | 500 | 3.0725 | 0.9982 |
| 1.1679 | 4.2 | 1000 | 1.3620 | 0.8889 |
| 0.6789 | 6.3 | 1500 | 1.2182 | 0.8160 |
| 0.5764 | 8.4 | 2000 | 1.2469 | 0.7667 |
| 0.4603 | 10.5 | 2500 | 1.2851 | 0.7533 |
| 0.4085 | 12.6 | 3000 | 1.2351 | 0.7401 |
| 0.3583 | 14.7 | 3500 | 1.2455 | 0.7367 |
| 0.3158 | 16.81 | 4000 | 1.3663 | 0.7261 |
| 0.2817 | 18.91 | 4500 | 1.3248 | 0.7175 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
frtna/jwt300_mt-Italian-to-Spanish_transformers
|
frtna
| 2022-03-31T11:18:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:new_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-29T09:49:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- sacrebleu
model-index:
- name: jwt300_mt-Italian-to-Spanish_transformers
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: new_dataset
type: new_dataset
args: jwt300_mt
metrics:
- name: Sacrebleu
type: sacrebleu
value: 0.9057
---
<!-- 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. -->
# jwt300_mt-Italian-to-Spanish_transformers
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4425
- Sacrebleu: 0.9057
- Gen Len: 18.1276
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 2.7545 | 1.0 | 2229 | 2.4425 | 0.9057 | 18.1276 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
scasutt/wav2vec2-base_toy_train_data_random_low_pass
|
scasutt
| 2022-03-31T10:42:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-31T08:21:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base_toy_train_data_random_low_pass
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_toy_train_data_random_low_pass
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: 1.3227
- Wer: 0.7288
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0795 | 2.1 | 500 | 3.2227 | 0.9982 |
| 1.21 | 4.2 | 1000 | 1.3713 | 0.8879 |
| 0.742 | 6.3 | 1500 | 1.2660 | 0.8296 |
| 0.5877 | 8.4 | 2000 | 1.2921 | 0.7794 |
| 0.4823 | 10.5 | 2500 | 1.2899 | 0.7565 |
| 0.4036 | 12.6 | 3000 | 1.3486 | 0.7494 |
| 0.391 | 14.7 | 3500 | 1.2701 | 0.7466 |
| 0.3426 | 16.81 | 4000 | 1.3570 | 0.7279 |
| 0.3015 | 18.91 | 4500 | 1.3227 | 0.7288 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
unjustify/autotrain-commonsence-689620825
|
unjustify
| 2022-03-31T06:38:08Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:unjustify/autotrain-data-commonsence",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T06:18:51Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- unjustify/autotrain-data-commonsence
co2_eq_emissions: 20.656741915705204
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 689620825
- CO2 Emissions (in grams): 20.656741915705204
## Validation Metrics
- Loss: 0.7315372824668884
- Accuracy: 0.6354949675117849
- Precision: 0.63792194092827
- Recall: 0.6191451241361658
- AUC: 0.6912165223485615
- F1: 0.6283932978308872
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/unjustify/autotrain-commonsence-689620825
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("unjustify/autotrain-commonsence-689620825", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("unjustify/autotrain-commonsence-689620825", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ai4bharat/MultiIndicParaphraseGeneration
|
ai4bharat
| 2022-03-31T06:21:30Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"paraphrase-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicParaphrase",
"arxiv:2203.05437",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-16T17:37:59Z |
---
tags:
- paraphrase-generation
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicParaphrase
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- mit
---
# MultiIndicParaphraseGeneration
This repository contains the [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2203.05437).
<ul>
<li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding. </li>
<li> Trained on large Indic language corpora (5.53 million sentences). </li>
<li> All languages, have been represented in Devanagari script to encourage transfer learning among the related languages. </li>
</ul>
## Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("दिल्ली यूनिवर्सिटी देश की प्रसिद्ध यूनिवर्सिटी में से एक है. </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3,encoder_no_repeat_ngram_size=3, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # दिल्ली विश्वविद्यालय देश की प्रमुख विश्वविद्यालयों में शामिल है।
# Note that if your output language is not Hindi or Marathi, you should convert its script from Devanagari to the desired language using the Indic NLP Library.
```
# Note:
If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.
## Benchmarks
Scores on the `IndicParaphrase` test sets are as follows:
Language | BLEU / Self-BLEU / iBLEU
---------|----------------------------
as | 1.66 / 2.06 / 0.54
bn | 11.57 / 1.69 / 7.59
gu | 22.10 / 2.76 / 14.64
hi | 27.29 / 2.87 / 18.24
kn | 15.40 / 2.98 / 9.89
ml | 10.57 / 1.70 / 6.89
mr | 20.38 / 2.20 / 13.61
or | 19.26 / 2.10 / 12.85
pa | 14.87 / 1.35 / 10.00
ta | 18.52 / 2.88 / 12.10
te | 16.70 / 3.34 / 10.69
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
|
danhsf/distilbert-base-uncased-finetuned-emotion
|
danhsf
| 2022-03-31T02:39:15Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T02:00:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.926557813198531
---
<!-- 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 the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2201
- Accuracy: 0.9265
- F1: 0.9266
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8631 | 1.0 | 250 | 0.3221 | 0.904 | 0.9011 |
| 0.254 | 2.0 | 500 | 0.2201 | 0.9265 | 0.9266 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5
|
yy642
| 2022-03-31T02:22:21Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T20:09:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-mnli-rte-wnli-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mnli-rte-wnli-5
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: 0.4400
- Accuracy: 0.9209
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2253 | 1.0 | 16558 | 0.2346 | 0.9139 |
| 0.1667 | 2.0 | 33116 | 0.2973 | 0.9143 |
| 0.1207 | 3.0 | 49674 | 0.3361 | 0.9203 |
| 0.0553 | 4.0 | 66232 | 0.4400 | 0.9209 |
| 0.033 | 5.0 | 82790 | 0.5175 | 0.9203 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.0.0
- Tokenizers 0.11.6
|
michiyasunaga/BioLinkBERT-base
|
michiyasunaga
| 2022-03-31T00:51:21Z | 6,225 | 36 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-08T07:22:12Z |
---
license: apache-2.0
language: en
datasets:
- pubmed
tags:
- bert
- exbert
- linkbert
- biolinkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
widget:
- text: "Sunitinib is a tyrosine kinase inhibitor"
---
## BioLinkBERT-base
BioLinkBERT-base model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA).
## Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
## Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
### How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base')
model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base')
inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
## Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art.
| | BLURB score | PubMedQA | BioASQ | MedQA-USMLE |
| ---------------------- | -------- | -------- | ------- | -------- |
| PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 |
| **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** |
| **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** |
| | MMLU-professional medicine |
| ---------------------- | -------- |
| GPT-3 (175 params) | 38.7 |
| UnifiedQA (11B params) | 43.2 |
| **BioLinkBERT-large (340M params)** | **50.7** |
## Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {Association for Computational Linguistics (ACL)},
}
```
|
michiyasunaga/LinkBERT-base
|
michiyasunaga
| 2022-03-31T00:38:32Z | 847 | 7 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-08T07:21:51Z |
---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
tags:
- bert
- exbert
- linkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
---
## LinkBERT-base
LinkBERT-base model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
## Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
## Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
### How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/LinkBERT-base')
model = AutoModel.from_pretrained('michiyasunaga/LinkBERT-base')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
## Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**General benchmarks ([MRQA](https://github.com/mrqa/MRQA-Shared-Task-2019) and [GLUE](https://gluebenchmark.com/)):**
| | HotpotQA | TriviaQA | SearchQA | NaturalQ | NewsQA | SQuAD | GLUE |
| ---------------------- | -------- | -------- | -------- | -------- | ------ | ----- | -------- |
| | F1 | F1 | F1 | F1 | F1 | F1 | Avg score |
| BERT-base | 76.0 | 70.3 | 74.2 | 76.5 | 65.7 | 88.7 | 79.2 |
| **LinkBERT-base** | **78.2** | **73.9** | **76.8** | **78.3** | **69.3** | **90.1** | **79.6** |
| BERT-large | 78.1 | 73.7 | 78.3 | 79.0 | 70.9 | 91.1 | 80.7 |
| **LinkBERT-large** | **80.8** | **78.2** | **80.5** | **81.0** | **72.6** | **92.7** | **81.1** |
## Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {Association for Computational Linguistics (ACL)},
}
```
|
hoangbinhmta99/wav2vec-NCKH-2022
|
hoangbinhmta99
| 2022-03-31T00:28:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"audio",
"speech",
"Transformer",
"automatic-speech-recognition",
"vi",
"dataset:vivos",
"dataset:common_voice",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-30T04:39:46Z |
---
language: vi
datasets:
- vivos
- common_voice
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech
- Transformer
license: cc-by-nc-4.0
model-index:
- name: Wav2vec2 NCKH Vietnamese 2022
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
- name: Test WER
type: wer
value: No
---
Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt
mkdir dict
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
mkdir outputs
python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py
--pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt
--dict_path ./dict/dict.ltr.txt --not_finetuned
```
# install and upload model
```
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
git lfs install
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo
ls
cd wav2vec-demo/
git status
git add .
git commit -m "First model version"
git config --global user.email [yourname]
git config --global user.name [yourpass]
git commit -m "First model version"
git push
```
|
GleamEyeBeast/ascend_with_english
|
GleamEyeBeast
| 2022-03-30T23:35:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-30T22:09:15Z |
---
tags:
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: ascend_with_english
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. -->
# ascend_with_english
This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3049
- Wer: 0.2251
## 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: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.3524 | 0.3016 |
| 0.4246 | 2.0 | 578 | 0.3132 | 0.2607 |
| 0.4246 | 3.0 | 867 | 0.3044 | 0.2373 |
| 0.2008 | 4.0 | 1156 | 0.3075 | 0.2302 |
| 0.2008 | 5.0 | 1445 | 0.3049 | 0.2251 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
UBC-NLP/MARBERTv2
|
UBC-NLP
| 2022-03-30T21:52:31Z | 3,124 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- ar
tags:
- Arabic BERT
- MSA
- Twitter
- Masked Langauge Model
widget:
- text: "اللغة العربية هي لغة [MASK]."
---
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**MARBERTv2** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.551.pdf)**.
We find that results with ARBERT and MARBERT on QA are not competitive, a clear discrepancy from what we have observed thus far on other tasksWe hypothesize this is because the two models are pre-trained with a sequence length of only 128, which does not allow them to sufficiently capture both a question and its likely answer within the same sequence window during the pre-training.
To rectify this, we further pre-train the stronger model, MARBERT, on the same MSA data as ARBERT in addition to AraNews dataset but with a bigger sequence length of 512 tokens for 40 epochs. We call this
further pre-trained model **MARBERTv2**, noting it has **29B tokens**. MARBERTv2 acquires best performance on all but one test set, where XLM-RLarge marginally outperforms us (only in F1).
For more information, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{abdul-mageed-etal-2021-arbert,
title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.551",
doi = "10.18653/v1/2021.acl-long.551",
pages = "7088--7105",
abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
mrm8488/biomedtra-small-es
|
mrm8488
| 2022-03-30T21:07:50Z | 3 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"pretraining",
"Spanish",
"Electra",
"Bio",
"Medical",
"es",
"dataset:cowese",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: es
tags:
- Spanish
- Electra
- Bio
- Medical
datasets:
- cowese
---
## 🦠 BIOMEDtra 🏥
**BIOMEDtra** (small) is an Electra like model (discriminator in this case) trained on [Spanish Biomedical Crawled Corpus](https://zenodo.org/record/5510033#.Yhdk1ZHMLJx).
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
## Training details
The model was trained using the Electra base code for 3 days on 1 GPU (Tesla V100 16GB).
## Dataset details
The largest Spanish biomedical and heath corpus to date gathered from a massive Spanish health domain crawler over more than 3,000 URLs were downloaded and preprocessed. The collected data have been preprocessed to produce the **CoWeSe** (Corpus Web Salud Español) resource, a large-scale and high-quality corpus intended for biomedical and health NLP in Spanish.
## Model details ⚙
|Param| # Value|
|-----|--------|
|Layers| 12 |
|Hidden | 256 |
|Params| 14M |
## Evaluation metrics (for discriminator) 🧾
|Metric | # Score |
|-------|---------|
|Accuracy| 0.9561|
|Precision| 0.808|
|Recall | 0.531 |
|AUC | 0.949|
## Benchmarks 🔨
WIP 🚧
## How to use the discriminator in `transformers`
```py
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("mrm8488/biomedtra-small-es")
tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/biomedtra-small-es")
sentence = "Los españoles tienden a sufir déficit de vitamina c"
fake_sentence = "Los españoles tienden a déficit sufrir de vitamina c"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % prediction, end="") for prediction in predictions.tolist()]
```
## Acknowledgments
TBA
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2022biomedtra,
title={Spanish BioMedical Electra (small)},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/biomedtra-small-es},
year={2022}
}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
vlsb/autotrain-security-texts-classification-roberta-688020754
|
vlsb
| 2022-03-30T20:55:42Z | 15 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:vlsb/autotrain-data-security-texts-classification-roberta",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T20:52:41Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vlsb/autotrain-data-security-texts-classification-roberta
co2_eq_emissions: 3.1151249696839685
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685
## Validation Metrics
- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/vlsb/autotrain-security-texts-classification-roberta-688020754
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("vlsb/autotrain-security-texts-classification-roberta-688020754", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("vlsb/autotrain-security-texts-classification-roberta-688020754", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
waboucay/camembert-base-finetuned-xnli_fr
|
waboucay
| 2022-03-30T17:47:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-11T08:54:07Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 89.2 | 87.6 |
| test | 88.9 | 87.4 |
|
horsbug98/Part_1_XLM_Model_E1
|
horsbug98
| 2022-03-30T17:13:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-16T18:22:10Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- tydiqa
model-index:
- name: debug_xlm_task1_1
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. -->
# debug_xlm_task1_1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiqa secondary_task 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.10.3
|
manu/lilt-infoxlm-base
|
manu
| 2022-03-30T14:47:15Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"liltrobertalike",
"fill-mask",
"token-classification",
"es",
"fr",
"ru",
"en",
"it",
"dataset:iit-cdip",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-30T07:26:57Z |
---
language:
- es
- fr
- ru
- en
- it
tags:
- token-classification
- fill-mask
license: mit
datasets:
- iit-cdip
---
This model is the pretrained infoxlm checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding".
Original repository: https://github.com/jpWang/LiLT
To use it, it is necessary to fork the modeling and configuration files from the original repository, and load the pretrained model from the corresponding classes (LiLTRobertaLikeConfig, LiLTRobertaLikeForRelationExtraction, LiLTRobertaLikeForTokenClassification, LiLTRobertaLikeModel).
They can also be preloaded with the AutoConfig/model factories as such:
```python
from transformers import AutoModelForTokenClassification, AutoConfig
from path_to_custom_classes import (
LiLTRobertaLikeConfig,
LiLTRobertaLikeForRelationExtraction,
LiLTRobertaLikeForTokenClassification,
LiLTRobertaLikeModel
)
def patch_transformers():
AutoConfig.register("liltrobertalike", LiLTRobertaLikeConfig)
AutoModel.register(LiLTRobertaLikeConfig, LiLTRobertaLikeModel)
AutoModelForTokenClassification.register(LiLTRobertaLikeConfig, LiLTRobertaLikeForTokenClassification)
# etc...
```
To load the model, it is then possible to use:
```python
# patch_transformers() must have been executed beforehand
tokenizer = AutoTokenizer.from_pretrained("microsoft/infoxlm-base")
model = AutoModel.from_pretrained("manu/lilt-infoxlm-base")
model = AutoModelForTokenClassification.from_pretrained("manu/lilt-infoxlm-base") # to be fine-tuned on a token classification task
```
|
GioReg/ita1
|
GioReg
| 2022-03-30T14:42:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T20:17:13Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: ita1
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. -->
# ita1
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892
- Accuracy: 0.776
- F1: 0.5912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
abdusah/aradia-ctc-v1
|
abdusah
| 2022-03-30T13:48:41Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T10:58:05Z |
---
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_300hrs
- generated_from_trainer
model-index:
- name: aradia-ctc-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. -->
# aradia-ctc-v1
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7171
- Wer: 0.3336
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.22 | 100 | 5.1889 | 1.0 |
| No log | 0.43 | 200 | 3.1129 | 1.0 |
| No log | 0.65 | 300 | 3.0503 | 1.0 |
| No log | 0.87 | 400 | 3.0279 | 1.0 |
| 6.2756 | 1.09 | 500 | 2.9965 | 1.0 |
| 6.2756 | 1.3 | 600 | 2.3618 | 0.9993 |
| 6.2756 | 1.52 | 700 | 1.2715 | 0.8758 |
| 6.2756 | 1.74 | 800 | 0.9971 | 0.7156 |
| 6.2756 | 1.96 | 900 | 0.8927 | 0.6382 |
| 1.712 | 2.17 | 1000 | 0.8252 | 0.5926 |
| 1.712 | 2.39 | 1100 | 0.7794 | 0.5434 |
| 1.712 | 2.61 | 1200 | 0.7557 | 0.5092 |
| 1.712 | 2.83 | 1300 | 0.7347 | 0.5203 |
| 1.712 | 3.04 | 1400 | 0.7189 | 0.4929 |
| 0.9305 | 3.26 | 1500 | 0.6820 | 0.4595 |
| 0.9305 | 3.48 | 1600 | 0.6792 | 0.4504 |
| 0.9305 | 3.69 | 1700 | 0.6596 | 0.4442 |
| 0.9305 | 3.91 | 1800 | 0.6756 | 0.4432 |
| 0.9305 | 4.13 | 1900 | 0.6663 | 0.4392 |
| 0.737 | 4.35 | 2000 | 0.6479 | 0.4372 |
| 0.737 | 4.56 | 2100 | 0.6353 | 0.4203 |
| 0.737 | 4.78 | 2200 | 0.6251 | 0.4088 |
| 0.737 | 5.0 | 2300 | 0.6209 | 0.4177 |
| 0.737 | 5.22 | 2400 | 0.6639 | 0.4094 |
| 0.6247 | 5.43 | 2500 | 0.6408 | 0.3970 |
| 0.6247 | 5.65 | 2600 | 0.6373 | 0.3932 |
| 0.6247 | 5.87 | 2700 | 0.6411 | 0.3928 |
| 0.6247 | 6.09 | 2800 | 0.6378 | 0.3897 |
| 0.6247 | 6.3 | 2900 | 0.6396 | 0.3929 |
| 0.5443 | 6.52 | 3000 | 0.6544 | 0.3864 |
| 0.5443 | 6.74 | 3100 | 0.6218 | 0.3786 |
| 0.5443 | 6.96 | 3200 | 0.6200 | 0.3784 |
| 0.5443 | 7.17 | 3300 | 0.6157 | 0.3791 |
| 0.5443 | 7.39 | 3400 | 0.6317 | 0.3798 |
| 0.4845 | 7.61 | 3500 | 0.6540 | 0.3771 |
| 0.4845 | 7.83 | 3600 | 0.6436 | 0.3670 |
| 0.4845 | 8.04 | 3700 | 0.6335 | 0.3695 |
| 0.4845 | 8.26 | 3800 | 0.6579 | 0.3610 |
| 0.4845 | 8.48 | 3900 | 0.6170 | 0.3613 |
| 0.4279 | 8.69 | 4000 | 0.6523 | 0.3617 |
| 0.4279 | 8.91 | 4100 | 0.6349 | 0.3577 |
| 0.4279 | 9.13 | 4200 | 0.6344 | 0.3673 |
| 0.4279 | 9.35 | 4300 | 0.6215 | 0.3641 |
| 0.4279 | 9.56 | 4400 | 0.6513 | 0.3608 |
| 0.3825 | 9.78 | 4500 | 0.6386 | 0.3605 |
| 0.3825 | 10.0 | 4600 | 0.6724 | 0.3549 |
| 0.3825 | 10.22 | 4700 | 0.6776 | 0.3602 |
| 0.3825 | 10.43 | 4800 | 0.6739 | 0.3544 |
| 0.3825 | 10.65 | 4900 | 0.6688 | 0.3557 |
| 0.3477 | 10.87 | 5000 | 0.6674 | 0.3564 |
| 0.3477 | 11.09 | 5100 | 0.6786 | 0.3476 |
| 0.3477 | 11.3 | 5200 | 0.6818 | 0.3478 |
| 0.3477 | 11.52 | 5300 | 0.6874 | 0.3470 |
| 0.3477 | 11.74 | 5400 | 0.6993 | 0.3424 |
| 0.3101 | 11.96 | 5500 | 0.6950 | 0.3404 |
| 0.3101 | 12.17 | 5600 | 0.6872 | 0.3406 |
| 0.3101 | 12.39 | 5700 | 0.6846 | 0.3424 |
| 0.3101 | 12.61 | 5800 | 0.7051 | 0.3405 |
| 0.3101 | 12.83 | 5900 | 0.7051 | 0.3378 |
| 0.2859 | 13.04 | 6000 | 0.6955 | 0.3403 |
| 0.2859 | 13.26 | 6100 | 0.7115 | 0.3390 |
| 0.2859 | 13.48 | 6200 | 0.7074 | 0.3384 |
| 0.2859 | 13.69 | 6300 | 0.7002 | 0.3376 |
| 0.2859 | 13.91 | 6400 | 0.7171 | 0.3360 |
| 0.2714 | 14.13 | 6500 | 0.7193 | 0.3341 |
| 0.2714 | 14.35 | 6600 | 0.7132 | 0.3347 |
| 0.2714 | 14.56 | 6700 | 0.7184 | 0.3353 |
| 0.2714 | 14.78 | 6800 | 0.7171 | 0.3331 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
shalpin87/dialoGPT-homer-simpson
|
shalpin87
| 2022-03-30T13:06:45Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-29T20:28:40Z |
---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## dialogGPT-homer-simpson
This model has been fine tuned with the entire scripts of Homer Simpson from the T.V. show The Simpsons
It will give some nice answers seemingly from Homers brain in the Simpsons Universe during single turn conversation, letting you chat to Homer Simpson
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
* Multi-turn generation examples from an interactive environment:
|Role | Response |
|---------|--------|
|User | Who are you? |
| HomerBot | Homer Simpson .|
|User | What is your favorite Restaurant ? |
| HomerBot | Moes Tavern. |
|User | Have you ever been in a band?! |
| HomerBot | no. |
Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
### How to use Multi-Turn
#### NOTE: Multi-Turn seems to be broken, after a few exchanges the output will mostly be exclamation marks.
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("shalpin87/dialoGPT-homer-simpson")
model = AutoModelForCausalLM.from_pretrained("shalpin87/dialoGPT-homer-simpson")
# Let's chat for 5 lines
for step in range(5):
# 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')
# 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=1000, pad_token_id=tokenizer.eos_token_id)
# pretty print last ouput tokens from bot
print("DialoG-PT-HomerBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
### How to use Single Turn
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("shalpin87/dialoGPT-homer-simpson")
model = AutoModelForCausalLM.from_pretrained("shalpin87/dialoGPT-homer-simpson")
questions = [
"What is your name?",
"Who are you?",
"Where do you work?",
"Who really killed Mr Burns?",
"Have you ever stolen from the Kwik-E-Mart?",
"Did you kill Frank Grimes?",
"Who was the worst member of the Be Sharps?",
"Hey where did Barney go?",
"What is your favorite bar to have a beer?",
"What is the best beer in Springfield?",
"Is Bart working for the Mob?",
"I think there was an incident in sector 7 G",
"Is Ned Flanders house okay?",
"Oh my god it's Sideshow Bob",
"What is a Flaming Moe?",
"What is happening to Apu?",
"Who quit the band?"
]
# Let's chat
for step in range(len(questions)):
print("***************************************")
print("Q. {}".format(questions[step]))
new_user_input_ids = tokenizer.encode(questions[step] + tokenizer.eos_token, return_tensors='pt')
# new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
bot_input_ids = new_user_input_ids
num_return_seqs=1
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=200,
top_p=0.55,
temperature = 0.85,
num_return_sequences=num_return_seqs
)
botname = "HomerBot"
for i in range(0,num_return_seqs):
print("{}:{}: {}".format(i, botname, tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][i], skip_special_tokens=True)))
chat_history_ids = []
```
Sample Output
```
***************************************
Q. What is your name?
0:HomerBot: Homer Simpson.
***************************************
Q. Who are you?
0:HomerBot: Homer Simpson.
***************************************
Q. Where do you work?
0:HomerBot: I work at a nuclear power plant.
***************************************
Q. Who really killed Mr Burns?
0:HomerBot: I dunno. Some guy named Ot-to.
***************************************
Q. Have you ever stolen from the Kwik-E-Mart?
0:HomerBot: No.
***************************************
Q. Did you kill Frank Grimes?
0:HomerBot: No.
***************************************
Q. Who was the worst member of the Be Sharps?
0:HomerBot: Homer Simpson, sir.
***************************************
Q. Hey where did Barney go?
0:HomerBot: He's been eating that thing for a week.
***************************************
Q. What is your favorite bar to have a beer?
0:HomerBot: The Flanders' Club.
***************************************
Q. What is the best beer in Springfield?
0:HomerBot: Pepi!
***************************************
Q. Is Bart working for the Mob?
0:HomerBot: I think he's trying to tell us something.
***************************************
Q. I think there was an incident in sector 7 G
0:HomerBot: Uh, let's not jump to any conclusions. The dog didn't run away.
```
|
yinde/dummy-model
|
yinde
| 2022-03-30T11:59:15Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T11:37:44Z |
Fake news classifier
This model trains a text classification model to detect fake news articles,
it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on
fake and real news dataset from kaggle (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)
|
Peltarion/xlm-roberta-longformer-base-4096
|
Peltarion
| 2022-03-30T09:23:58Z | 75 | 8 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"longformer",
"multilingual",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- longformer
language: multilingual
license: apache-2.0
datasets:
- wikitext
---
## XLM-R Longformer Model
XLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) on the English WikiText-103 corpus.
The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at [Peltarion](https://peltarion.com/) and was fine-tuned on multilingual quesion-answering tasks, with code available [here](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer#xlm-r).
Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps.
## How to Use
The model can be used as expected to fine-tune on a downstream task.
For instance for QA.
```python
import torch
from transformers import AutoModel, AutoTokenizer
MAX_SEQUENCE_LENGTH = 4096
MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096"
tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
padding="max_length",
truncation=True,
)
model = AutoModelForQuestionAnswering.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
)
```
## Training Procedure
The model have been trained on the WikiText-103 corpus, using a **48GB** GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/main/scripts/finetune_qa_models.py) and [Github repo](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer) for more information
```sh
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
unzip wikitext-103-raw-v1.zip
export DATA_DIR=./wikitext-103-raw
scripts/run_long_lm.py \
--model_name_or_path xlm-roberta-base \
--model_name xlm-roberta-to-longformer \
--output_dir ./output \
--logging_dir ./logs \
--val_file_path $DATA_DIR/wiki.valid.raw \
--train_file_path $DATA_DIR/wiki.train.raw \
--seed 42 \
--max_pos 4096 \
--adam_epsilon 1e-8 \
--warmup_steps 500 \
--learning_rate 3e-5 \
--weight_decay 0.01 \
--max_steps 6000 \
--evaluate_during_training \
--logging_steps 50 \
--eval_steps 50 \
--save_steps 6000 \
--max_grad_norm 1.0 \
--per_device_eval_batch_size 2 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 64 \
--overwrite_output_dir \
--fp16 \
--do_train \
--do_eval
```
|
shrishail/t5_paraphrase_msrp_paws
|
shrishail
| 2022-03-30T05:47:27Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-03-29T13:13:11Z |
---
language: "en"
tags:
- paraphrase-generation
- text-generation
- Conditional Generation
inference: false
---
# Simple model for Paraphrase Generation
## Model description
T5-based model for generating paraphrased sentences. It is trained on the labeled [MSRP](https://www.microsoft.com/en-us/download/details.aspx?id=52398) and [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("shrishail/t5_paraphrase_msrp_paws")
model = AutoModelForSeq2SeqLM.from_pretrained("shrishail/t5_paraphrase_msrp_paws")
sentence = "This is something which i cannot understand at all"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
```
|
nlp-waseda/gpt2-small-japanese
|
nlp-waseda
| 2022-03-30T04:28:17Z | 26 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T03:34:11Z |
---
language:
- ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
widget:
- text: "早稲田 大学 で 自然 言語 処理 を"
---
# nlp-waseda/gpt2-small-japanese
This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese')
>>> set_seed(42)
>>> generator("早稲田 大学 で 自然 言語 処理 を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5)
[{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 帰国 後 、 早稲田 大学 理工 学部 に 入学 し ます 。 卒業 後 、 早稲田 大学 工学 研究 科 、'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 アメリカ の 大学 で 学士 号 を 取得 、 修士 の 取得 で 博士 号 を 取得 。 2008 年'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 勉強 して い ます 。 学部 は 日本 語 学科 を 専攻 して い ます 。 英語 が 話せる と いう'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 して いた 。 2011 年 に 第 26 回 日本 化学 会 学生 委員 会 奨励 賞 ( 第 2 年次 審査'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 中心 と する 言語 学 研究 を 行って いる 。 東京 都 ・ 豊島 区 の お 見合い 相手 。'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import ReformerTokenizer, GPT2Model
tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese')
model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese')
text = "早稲田 大学 で 自然 言語 処理 を"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training data
The GPT-2 model was pretrained on Japanese Wikipedia, dumped on 2022-03-20, and the Japanese portion of CC-100.
## Training procedure
### Preprocessing
The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining.
The model was trained on 8 NVIDIA A100 GPUs.
|
lazyturtl/roomidentifier
|
lazyturtl
| 2022-03-30T04:10:41Z | 89 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-30T04:10:32Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: roomidentifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# roomidentifier
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
#### Bathroom
#### Bedroom
#### DinningRoom
#### Kitchen
#### LivingRoom
|
samayash/finetuning-financial-news-sentiment
|
samayash
| 2022-03-30T03:36:40Z | 4 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T03:27:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-financial-news-sentiment
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. -->
# finetuning-financial-news-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3345
- Accuracy: 0.8751
- F1: 0.8751
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
aaraki/vit-base-patch16-224-in21k-finetuned-cifar10
|
aaraki
| 2022-03-30T01:41:47Z | 8,239 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-30T00:18:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9788
---
<!-- 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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2564
- Accuracy: 0.9788
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4291 | 1.0 | 390 | 0.2564 | 0.9788 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/InformalToFormalLincoln33
|
BigSalmon
| 2022-03-30T01:24:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T01:19:07Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln33")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
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:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
|
cammiemw/bert-marco-hdct
|
cammiemw
| 2022-03-30T01:21:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T01:09:55Z |
---
license: cc-by-nc-4.0
---
|
DrishtiSharma/poem-gen-spanish-t5-small-v5
|
DrishtiSharma
| 2022-03-29T23:25:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-29T18:54:38Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small-v5
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. -->
# poem-gen-spanish-t5-small-v5
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8881
## 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: 0.000125
- train_batch_size: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:------:|:---------------:|
| 2.9366 | 0.73 | 30000 | 2.9656 |
| 2.7518 | 1.46 | 60000 | 2.9120 |
| 2.6018 | 2.19 | 90000 | 2.8870 |
| 2.5262 | 2.93 | 120000 | 2.8646 |
| 2.3886 | 3.66 | 150000 | 2.8816 |
| 2.2758 | 4.39 | 180000 | 2.8900 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/PointsOneSent
|
BigSalmon
| 2022-03-29T21:26:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-29T21:19:54Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsOneSent")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsOneSent")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
-
```
It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27
|
efederici/sentence-it5-small
|
efederici
| 2022-03-29T17:29:14Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-27T15:19:10Z |
---
pipeline_tag: sentence-similarity
language:
- it
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-IT5-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 ([IT5](https://huggingface.co/gsarti/it5-small)) small model trained for asymmetric semantic search. Query is a keyword, Paragraph is a short news article.
## 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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
model = SentenceTransformer('efederici/sentence-IT5-small')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-IT5-small')
model = AutoModel.from_pretrained('efederici/sentence-IT5-small')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': None, 'do_lower_case': False}) with Transformer model: T5EncoderModel
(1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
krinal214/augmented
|
krinal214
| 2022-03-29T16:58:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-29T15:02:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: augmented
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. -->
# augmented
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5104
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0609 | 1.0 | 9787 | 0.5104 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
gabitoo1234/autotrain-mut_all_text-680820343
|
gabitoo1234
| 2022-03-29T16:09:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"es",
"dataset:gabitoo1234/autotrain-data-mut_all_text",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-29T14:22:14Z |
---
tags: autotrain
language: es
widget:
- text: "I love AutoTrain 🤗"
datasets:
- gabitoo1234/autotrain-data-mut_all_text
co2_eq_emissions: 115.48848403681228
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 680820343
- CO2 Emissions (in grams): 115.48848403681228
## Validation Metrics
- Loss: 0.3041240870952606
- Accuracy: 0.9462770369425126
- Macro F1: 0.7836898686625933
- Micro F1: 0.9462770369425126
- Weighted F1: 0.9449148298990091
- Macro Precision: 0.8344505891491089
- Micro Precision: 0.9462770369425126
- Weighted Precision: 0.9451247372908952
- Macro Recall: 0.7568785255994025
- Micro Recall: 0.9462770369425126
- Weighted Recall: 0.9462770369425126
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/gabitoo1234/autotrain-mut_all_text-680820343
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("gabitoo1234/autotrain-mut_all_text-680820343", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
tbosse/bert-base-german-cased-finetuned-subj_v1
|
tbosse
| 2022-03-29T15:59:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-29T14:22:30Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_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. -->
# bert-base-german-cased-finetuned-subj_v1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1594
- Precision: 0.1875
- Recall: 0.0077
- F1: 0.0147
- Accuracy: 0.9508
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 136 | 0.1591 | 1.0 | 0.0051 | 0.0102 | 0.9523 |
| No log | 2.0 | 272 | 0.1571 | 0.375 | 0.0077 | 0.015 | 0.9518 |
| No log | 3.0 | 408 | 0.1594 | 0.1875 | 0.0077 | 0.0147 | 0.9508 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
sayef/fsner-bert-base-uncased
|
sayef
| 2022-03-29T14:20:35Z | 9 | 6 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2008.10570",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# FSNER
Implemented by [sayef](https://huggingface.co/sayef).
# Overview
The FSNER model was proposed in [Example-Based Named Entity Recognition](https://arxiv.org/abs/2008.10570) by Morteza
Ziyadi, Yuting Sun, Abhishek Goswami, Jade Huang, Weizhu Chen. To identify entity spans in a new domain, it uses a
train-free few-shot learning approach inspired by question-answering.
## Abstract
> We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER. Our train-free few-shot learning approach takes inspiration from question-answering to identify entity spans in a new and unseen domain. In comparison with the current state-of-the-art, the proposed method performs significantly better, especially when using a low number of support examples.
## Model Training Details
| identifier | epochs | datasets |
| ---------- |:------:|:-----------------------------------------------------------------------------------------------:|
| [sayef/fsner-bert-base-uncased](https://huggingface.co/sayef/fsner-bert-base-uncased) | 25 | ontonotes5, conll2003, wnut2017, mit_movie_trivia, mit_restaurant and fin (Alvarado et al.). |
## Installation and Example Usage
You can use the FSNER model in 3 ways:
1. Install directly from PyPI: `pip install fsner` and import the model as shown in the code example below
or
2. Install from source: `python install .` and import the model as shown in the code example below
or
3. Clone [repo](https://github.com/sayef/fsner) and add absolute path of `fsner/src` directory to your PYTHONPATH and
import the model as shown in the code example below
```python
import json
from fsner import FSNERModel, FSNERTokenizerUtils, pretty_embed
query_texts = [
"Does Luke's serve lunch?",
"Chang does not speak Taiwanese very well.",
"I like Berlin."
]
# Each list in supports are the examples of one entity type
# Wrap entities around with [E] and [/E] in the examples.
# Each sentence should have only one pair of [E] ... [/E]
support_texts = {
"Restaurant": [
"What time does [E] Subway [/E] open for breakfast?",
"Is there a [E] China Garden [/E] restaurant in newark?",
"Does [E] Le Cirque [/E] have valet parking?",
"Is there a [E] McDonalds [/E] on main street?",
"Does [E] Mike's Diner [/E] offer huge portions and outdoor dining?"
],
"Language": [
"Although I understood no [E] French [/E] in those days , I was prepared to spend the whole day with Chien - chien .",
"like what the hell 's that called in [E] English [/E] ? I have to register to be here like since I 'm a foreigner .",
"So , I 'm also working on an [E] English [/E] degree because that 's my real interest .",
"Al - Jazeera TV station , established in November 1996 in Qatar , is an [E] Arabic - language [/E] news TV station broadcasting global news and reports nonstop around the clock .",
"They think it 's far better for their children to be here improving their [E] English [/E] than sitting at home in front of a TV . \"",
"The only solution seemed to be to have her learn [E] French [/E] .",
"I have to read sixty pages of [E] Russian [/E] today ."
]
}
device = 'cpu'
tokenizer = FSNERTokenizerUtils("sayef/fsner-bert-base-uncased")
queries = tokenizer.tokenize(query_texts).to(device)
supports = tokenizer.tokenize(list(support_texts.values())).to(device)
model = FSNERModel("sayef/fsner-bert-base-uncased")
model.to(device)
p_starts, p_ends = model.predict(queries, supports)
# One can prepare supports once and reuse multiple times with different queries
# ------------------------------------------------------------------------------
# start_token_embeddings, end_token_embeddings = model.prepare_supports(supports)
# p_starts, p_ends = model.predict(queries, start_token_embeddings=start_token_embeddings,
# end_token_embeddings=end_token_embeddings)
output = tokenizer.extract_entity_from_scores(query_texts, queries, p_starts, p_ends,
entity_keys=list(support_texts.keys()), thresh=0.50)
print(json.dumps(output, indent=2))
# install displacy for pretty embed
pretty_embed(query_texts, output, list(support_texts.keys()))
```
<!DOCTYPE html>
<html lang="en">
<head>
<title>displaCy</title>
</head>
<body style="font-size: 16px; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; padding: 4rem 2rem; direction: ltr">
<figure style="margin-bottom: 6rem">
<div class="entities" style="line-height: 2.5; direction: ltr">
<div class="entities" style="line-height: 2.5; direction: ltr">Does
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Luke's
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">Restaurant</span>
</mark>
serve lunch?</div>
<div class="entities" style="line-height: 2.5; direction: ltr">Chang does not speak
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Taiwanese
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">Language</span>
</mark>
very well.</div>
<div class="entities" style="line-height: 2.5; direction: ltr">I like Berlin.</div>
</div>
</figure>
</body>
</html>
## Datasets preparation
1. We need to convert dataset into the following format. Let's say we have a dataset file train.json like following.
2. Each list in supports are the examples of one entity type
3. Wrap entities around with [E] and [/E] in the examples.
4. Each example should have only one pair of [E] ... [/E].
```json
{
"CARDINAL_NUMBER": [
"Washington , cloudy , [E] 2 [/E] to 6 degrees .",
"New Dehli , sunny , [E] 6 [/E] to 19 degrees .",
"Well this is number [E] two [/E] .",
"....."
],
"LANGUAGE": [
"They do n't have the Quicken [E] Dutch [/E] version ?",
"they learned a lot of [E] German [/E] .",
"and then [E] Dutch [/E] it 's Mifrau",
"...."
],
"MONEY": [
"Per capita personal income ranged from $ [E] 11,116 [/E] in Mississippi to $ 23,059 in Connecticut ... .",
"The trade surplus was [E] 582 million US dollars [/E] .",
"It settled with a loss of 4.95 cents at $ [E] 1.3210 [/E] a pound .",
"...."
]
}
```
2. Converted ontonotes5 dataset can be found here:
1. [train](https://gist.githubusercontent.com/sayef/46deaf7e6c6e1410b430ddc8aff9c557/raw/ea7ae2ae933bfc9c0daac1aa52a9dc093d5b36f4/ontonotes5.train.json)
2. [dev](https://gist.githubusercontent.com/sayef/46deaf7e6c6e1410b430ddc8aff9c557/raw/ea7ae2ae933bfc9c0daac1aa52a9dc093d5b36f4/ontonotes5.dev.json)
3. Then trainer script can be used to train/evaluate your fsner model.
```bash
fsner trainer --pretrained-model bert-base-uncased --mode train --train-data train.json --val-data val.json \
--train-batch-size 6 --val-batch-size 6 --n-examples-per-entity 10 --neg-example-batch-ratio 1/3 --max-epochs 25 --device gpu \
--gpus -1 --strategy ddp
```
|
maretamasaeva/roberta-finetuned-freeform
|
maretamasaeva
| 2022-03-29T14:19:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-finetuned-freeform
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-finetuned-freeform
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6989
- Accuracy: 0.4668
## 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6919 | 1.0 | 8094 | 0.6910 | 0.4668 |
| 0.6912 | 2.0 | 16188 | 0.6934 | 0.4668 |
| 0.6904 | 3.0 | 24282 | 0.6976 | 0.4668 |
| 0.6918 | 4.0 | 32376 | 0.6989 | 0.4668 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
gayanin/bart-med-term-conditional-masking-0
|
gayanin
| 2022-03-29T12:03:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-28T22:12:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-med-term-conditional-masking-0
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. -->
# bart-med-term-conditional-masking-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5041
- Rouge2 Precision: 0.7497
- Rouge2 Recall: 0.5246
- Rouge2 Fmeasure: 0.5986
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6381 | 1.0 | 13915 | 0.5595 | 0.734 | 0.5152 | 0.5873 |
| 0.5429 | 2.0 | 27830 | 0.5243 | 0.7441 | 0.5225 | 0.5956 |
| 0.5002 | 3.0 | 41745 | 0.5078 | 0.7482 | 0.5238 | 0.5976 |
| 0.4607 | 4.0 | 55660 | 0.5041 | 0.7497 | 0.5246 | 0.5986 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
scasutt/wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms
|
scasutt
| 2022-03-29T11:29:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-28T18:54:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms
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: 0.5945
- Wer: 0.4929
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4049 | 1.05 | 250 | 3.3497 | 1.0 |
| 3.0851 | 2.1 | 500 | 3.4440 | 1.0 |
| 2.3512 | 3.15 | 750 | 1.5938 | 0.9317 |
| 1.1762 | 4.2 | 1000 | 0.8481 | 0.7333 |
| 0.903 | 5.25 | 1250 | 0.7180 | 0.6484 |
| 0.6754 | 6.3 | 1500 | 0.6603 | 0.6044 |
| 0.5961 | 7.35 | 1750 | 0.6410 | 0.5778 |
| 0.5325 | 8.4 | 2000 | 0.6245 | 0.5545 |
| 0.4685 | 9.45 | 2250 | 0.5925 | 0.5359 |
| 0.4526 | 10.5 | 2500 | 0.5991 | 0.5345 |
| 0.3975 | 11.55 | 2750 | 0.5916 | 0.5228 |
| 0.3672 | 12.6 | 3000 | 0.5882 | 0.5037 |
| 0.3774 | 13.65 | 3250 | 0.5693 | 0.5028 |
| 0.3489 | 14.7 | 3500 | 0.5645 | 0.5018 |
| 0.3593 | 15.75 | 3750 | 0.5977 | 0.5043 |
| 0.3167 | 16.81 | 4000 | 0.6049 | 0.5018 |
| 0.3225 | 17.86 | 4250 | 0.6172 | 0.4921 |
| 0.2807 | 18.91 | 4500 | 0.5937 | 0.4923 |
| 0.2889 | 19.96 | 4750 | 0.5945 | 0.4929 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Rishav-hub/xlm-roberta-base-finetuned-panx-de
|
Rishav-hub
| 2022-03-29T11:05:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-29T10:26:12Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8591260810195721
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- F1: 0.8591
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.257 | 1.0 | 525 | 0.1512 | 0.8302 |
| 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 |
| 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
beston91/gpt2-xl_ft_logits_5k_experiment
|
beston91
| 2022-03-29T10:27:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-29T03:13:26Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl_ft_logits_5k_experiment
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. -->
# gpt2-xl_ft_logits_5k_experiment
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8601
## 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
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100.0
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.9 | 7 | 6.1556 |
| No log | 1.9 | 14 | 6.3365 |
| No log | 2.9 | 21 | 6.5909 |
| No log | 3.9 | 28 | 6.8601 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
### Perplexity
Score: 17.589759826660156
|
KeithHorgan/TweetClimateAnalysis
|
KeithHorgan
| 2022-03-29T10:01:24Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:KeithHorgan98/autotrain-data-TweetClimateAnalysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-29T10:16:42Z |
---
tags: autotrain
language: unk
widget:
- text: "Climate Change is a hoax"
- text: "It is freezing, where is global warming"
datasets:
- KeithHorgan98/autotrain-data-TweetClimateAnalysis
co2_eq_emissions: 133.19491276284793
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 678720226
- CO2 Emissions (in grams): 133.19491276284793
## Validation Metrics
- Loss: 0.4864234924316406
- Accuracy: 0.865424430641822
- Macro F1: 0.7665472174344069
- Micro F1: 0.8654244306418221
- Weighted F1: 0.8586375445115083
- Macro Precision: 0.8281449061702826
- Micro Precision: 0.865424430641822
- Weighted Precision: 0.8619727477790186
- Macro Recall: 0.736576343905098
- Micro Recall: 0.865424430641822
- Weighted Recall: 0.865424430641822
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/KeithHorgan98/autotrain-TweetClimateAnalysis-678720226
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ai4bharat/MultiIndicWikiBioUnified
|
ai4bharat
| 2022-03-29T09:25:58Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"wikibio",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"hi",
"kn",
"ml",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicWikiBio",
"arxiv:2203.05437",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-16T11:35:33Z |
---
tags:
- wikibio
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicWikiBio
language:
- as
- bn
- hi
- kn
- ml
- or
- pa
- ta
- te
licenses:
- cc-by-nc-4.0
widget:
- <TAG> name </TAG> नवतेज भारती <TAG> image </TAG> NavtejBharati . jpg <TAG> birth name </TAG> नवतेज <TAG> birth date </TAG> 1938 <TAG> birth place </TAG> रोडे , भारतीय पंजाब , भारत । पंजाब <TAG> occupation </TAG> लेखक , कवि <TAG> nationality </TAG> कैनेडा । कैनेडियन <TAG> ethnicity </TAG> पंजाबी लोक । पंजाबी </s> <2hi>
---
# MultiIndicWikiBioUnified
MultiIndicWikiBioUnified is a multilingual, sequence-to-sequence pre-trained model, a [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint fine-tuned on the 9 languages of [IndicWikiBio](https://huggingface.co/datasets/ai4bharat/IndicWikiBio) dataset. For fine-tuning details,
see the [paper](https://arxiv.org/abs/2203.05437). You can use MultiIndicWikiBio to build biography generation applications for Indian languages by fine-tuning the model with supervised training data. Some salient features of the MultiIndicWikiBio are:
<ul>
<li >Supported languages: Assamese, Bengali, Hindi, Oriya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for fine-tuning and decoding. </li>
<li> Fine-tuned on an Indic language corpora (34,653 examples). </li>
<li> All languages have been represented in Devanagari script to encourage transfer learning among the related languages. </li>
</ul>
You can read more about MultiIndicWikiBioUnified in this <a href="https://arxiv.org/abs/2203.05437">paper</a>.
## Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicWikiBioUnified", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicWikiBioUnified", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicWikiBioUnified")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicWikiBioUnified")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2hi>', '<2kn>', '<2ml>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input and outputs. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("<TAG> name </TAG> भीखा लाल <TAG> office </TAG> विधायक - 318 - हसनगंज विधान सभा निर्वाचन क्षेत्र , उत्तर प्रदेश <TAG> term </TAG> 1957 से 1962 <TAG> nationality </TAG> भारतीय</s><2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
out = tokenizer("<2hi> भीखा लाल ,भारत के उत्तर प्रदेश की दूसरी विधानसभा सभा में विधायक रहे। </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:])
# For loss
model_outputs.loss ## This is not label smoothed.
# For logits
model_outputs.logits
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model.eval() # Set dropouts to zero
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3,encoder_no_repeat_ngram_size=3, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # भीखा लाल ,भारत के उत्तर प्रदेश की दूसरी विधानसभा सभा में विधायक रहे।
# Disclaimer
Note that if your output language is not Hindi or Marathi, you should convert its script from Devanagari to the desired language using the [Indic NLP Library](https://github.com/AI4Bharat/indic-bart/blob/main/indic_scriptmap.py).
```
# Note:
If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.
## Benchmarks
Scores on the `IndicWikiBio` test sets are as follows:
Language | RougeL
---------|----------------------------
as | 56.28
bn | 57.42
hi | 67.48
kn | 40.01
ml | 38.84
or | 67.13
pa | 52.88
ta | 51.82
te | 51.43
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
# License
The model is available under the MIT License.
|
Davlan/m2m100_418M-yor-eng-mt
|
Davlan
| 2022-03-29T09:21:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# m2m100_418M-eng-yor-mt
## Model description
**m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt).
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
```
```
|
PereLluis13/Wav2Vec2-Large-XLSR-53-catalan
|
PereLluis13
| 2022-03-29T08:51:28Z | 6,942 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ca",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: ca
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Catalan XLSR Wav2Vec Large 53 #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53`
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ca
type: common_voice
args: ca #TODO:
metrics:
- name: Test WER
type: wer
value: 8.11
---
# Disclaimer
This model was trained on Common Voice 6, if you need a catalan model for ASR, I recommend checking [wav2vec2-xls-r-1b-ca-lm](https://huggingface.co/PereLluis13/wav2vec2-xls-r-1b-ca-lm) which is a 1b model with a LM on top trained on CV8+ with much better performance or [wav2vec2-xls-r-300m-ca-lm](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) which has the same size (300m) as this model but trained on CV8+ and the same LM.
# Wav2Vec2-Large-XLSR-53-ca
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on catalan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ca", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan")
model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the catalan test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ca", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan")
model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
import jiwer
# Chunk WER computation due to memory issues, taken from https://huggingface.co/pcuenq/wav2vec2-large-xlsr-53-es
def chunked_wer(targets, predictions, chunk_size=None):
if chunk_size is None: return jiwer.wer(targets, predictions)
start = 0
end = chunk_size
H, S, D, I = 0, 0, 0, 0
while start < len(targets):
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
start += chunk_size
end += chunk_size
return float(S + D + I) / float(H + S + D)
print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))
```
**Test Result**: 8.11 %
## Training
The Common Voice `train`, `validation` datasets were used for training. At the second epoch training was halted due to a memory issue, and was continued with lower batch size, but acc. gradient steps were scaled to keep it at 32 batch size during all training. Then the model was trained for an additional 10 epochs where half the male samples were pitched up.
The script used for training can be found [here](https://github.com/huggingface/transformers/blob/master/examples/research_projects/wav2vec2/run_common_voice.py). Slight modifications were done in order to speed up the ordering by length during training, which can be found [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/6). Another version trained for catalan can be found [here](https://huggingface.co/ccoreilly/wav2vec2-large-xlsr-catala), which may be better than this one since it was trained with extra data and for longer time. Whoever, since it used different splits that include part of the Common Voice test set, this version can be used to get a baseline on the Common Voice dataset.
|
PereLluis13/wav2vec2-xls-r-1b-ca
|
PereLluis13
| 2022-03-29T08:44:49Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"collectivat/tv3_parla",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"projecte-aina/parlament_parla",
"robust-speech-event",
"ca",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:collectivat/tv3_parla",
"dataset:projecte-aina/parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ca
license: apache-2.0
tags:
- automatic-speech-recognition
- collectivat/tv3_parla
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- projecte-aina/parlament_parla
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- collectivat/tv3_parla
- projecte-aina/parlament_parla
model-index:
- name: wav2vec2-xls-r-1b-ca
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 ca
type: mozilla-foundation/common_voice_8_0
args: ca
metrics:
- name: Test WER
type: wer
value: 11.030639657300516
- name: Test CER
type: cer
value: 2.8405630530040634
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: projecte-aina/parlament_parla ca
type: projecte-aina/parlament_parla
args: clean
metrics:
- name: Test WER
type: wer
value: 6.483115660665961
- name: Test CER
type: cer
value: 2.0212863746191828
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: collectivat/tv3_parla ca
type: collectivat/tv3_parla
args: ca
metrics:
- name: Test WER
type: wer
value: 17.917773414943988
- name: Test CER
type: cer
value: 8.872589572206396
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Catalan Dev Data
type: speech-recognition-community-v2/dev_data
args: ca
metrics:
- name: Test WER
type: wer
value: 27.126683954209097
- name: Test CER
type: cer
value: 14.213308815078726
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ca
metrics:
- name: Test WER
type: wer
value: 18.7
---
<!-- 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-1b-ca
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets.
## Model description
Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model.
## Intended uses & limitations
As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.
## Training and evaluation data
## Training procedure
The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).
### Training results
Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
# Thanks
Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.
|
PereLluis13/wav2vec2-xls-r-300m-ca
|
PereLluis13
| 2022-03-29T08:43:53Z | 52 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"collectivat/tv3_parla",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"projecte-aina/parlament_parla",
"robust-speech-event",
"ca",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:collectivat/tv3_parla",
"dataset:projecte-aina/parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ca
license: apache-2.0
tags:
- automatic-speech-recognition
- collectivat/tv3_parla
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- projecte-aina/parlament_parla
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- collectivat/tv3_parla
- projecte-aina/parlament_parla
model-index:
- name: wav2vec2-xls-r-300m-ca
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 ca
type: mozilla-foundation/common_voice_8_0
args: ca
metrics:
- name: Test WER
type: wer
value: 13.170091241317552
- name: Test CER
type: cer
value: 3.356726205534543
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: projecte-aina/parlament_parla ca
type: projecte-aina/parlament_parla
args: clean
metrics:
- name: Test WER
type: wer
value: 8.048005647723261
- name: Test CER
type: cer
value: 2.240912911020065
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: collectivat/tv3_parla ca
type: collectivat/tv3_parla
args: ca
metrics:
- name: Test WER
type: wer
value: 23.320629787889285
- name: Test CER
type: cer
value: 10.439216202089989
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech-recognition-community-v2/dev_data ca
type: speech-recognition-community-v2/dev_data
args: ca
metrics:
- name: Test WER
type: wer
value: 31.99671115046487
- name: Test CER
type: cer
value: 15.820020687277325
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ca
metrics:
- name: Test WER
type: wer
value: 22.04
---
# wav2vec2-xls-r-300m-ca
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets.
It achieves the following results on the evaluation set (for the three datasets):
- Loss: 0.2472
- Wer: 0.1499
## Model description
Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model.
## Intended uses & limitations
As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.
## Training and evaluation data
More information needed
## Training procedure
The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 18.0
- mixed_precision_training: Native AMP
### Training results
Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.2099 | 0.09 | 500 | 3.4125 | 1.0 |
| 2.9961 | 0.18 | 1000 | 2.9224 | 1.0 |
| 2.2147 | 0.26 | 1500 | 0.6521 | 0.5568 |
| 1.3017 | 0.35 | 2000 | 0.3153 | 0.2761 |
| 1.1196 | 0.44 | 2500 | 0.2444 | 0.2367 |
| 1.0712 | 0.53 | 3000 | 0.2324 | 0.2132 |
| 1.052 | 0.62 | 3500 | 0.2173 | 0.2032 |
| 1.2813 | 2.13 | 4000 | 0.3326 | 0.2099 |
| 1.2365 | 2.4 | 4500 | 0.3224 | 0.2003 |
| 1.2193 | 2.66 | 5000 | 0.3198 | 0.1957 |
| 1.2072 | 2.93 | 5500 | 0.3063 | 0.1933 |
| 1.213 | 3.2 | 6000 | 0.3051 | 0.1980 |
| 1.2074 | 3.46 | 6500 | 0.3012 | 0.1879 |
| 1.1918 | 3.73 | 7000 | 0.2947 | 0.1829 |
| 1.1893 | 4.0 | 7500 | 0.2895 | 0.1807 |
| 1.1751 | 4.26 | 8000 | 0.2878 | 0.1776 |
| 1.1628 | 4.53 | 8500 | 0.2835 | 0.1731 |
| 1.1577 | 4.79 | 9000 | 0.2816 | 0.1761 |
| 1.1448 | 5.06 | 9500 | 0.2757 | 0.1740 |
| 1.1407 | 5.33 | 10000 | 0.2768 | 0.1798 |
| 1.1401 | 5.59 | 10500 | 0.2780 | 0.1816 |
| 1.1333 | 5.86 | 11000 | 0.2748 | 0.1750 |
| 1.1571 | 6.13 | 11500 | 0.2808 | 0.1708 |
| 1.1505 | 6.39 | 12000 | 0.2726 | 0.1692 |
| 1.1519 | 6.66 | 12500 | 0.2749 | 0.1654 |
| 1.136 | 6.93 | 13000 | 0.2765 | 0.1643 |
| 1.1326 | 7.19 | 13500 | 0.2706 | 0.1668 |
| 1.1342 | 7.46 | 14000 | 0.2665 | 0.1638 |
| 1.1286 | 7.72 | 14500 | 0.2669 | 0.1636 |
| 1.1243 | 7.99 | 15000 | 0.2619 | 0.1623 |
| 1.1173 | 8.26 | 15500 | 0.2652 | 0.1604 |
| 1.1129 | 8.52 | 16000 | 0.2610 | 0.1598 |
| 1.1091 | 8.79 | 16500 | 0.2608 | 0.1584 |
| 1.1053 | 9.06 | 17000 | 0.2633 | 0.1664 |
| 1.1004 | 9.32 | 17500 | 0.2594 | 0.1662 |
| 1.0995 | 9.59 | 18000 | 0.2623 | 0.1569 |
| 1.0964 | 9.86 | 18500 | 0.2624 | 0.1597 |
| 1.09 | 10.12 | 19000 | 0.2577 | 0.1578 |
| 1.089 | 10.39 | 19500 | 0.2574 | 0.1531 |
| 1.0864 | 10.66 | 20000 | 0.2556 | 0.1546 |
| 1.0806 | 10.92 | 20500 | 0.2548 | 0.1583 |
| 1.0842 | 11.19 | 21000 | 0.2550 | 0.1542 |
| 1.0805 | 11.45 | 21500 | 0.2561 | 0.1524 |
| 1.0722 | 11.72 | 22000 | 0.2540 | 0.1566 |
| 1.0763 | 11.99 | 22500 | 0.2549 | 0.1572 |
| 1.0835 | 12.25 | 23000 | 0.2586 | 0.1521 |
| 1.0883 | 12.52 | 23500 | 0.2583 | 0.1519 |
| 1.0888 | 12.79 | 24000 | 0.2551 | 0.1582 |
| 1.0933 | 13.05 | 24500 | 0.2628 | 0.1537 |
| 1.0799 | 13.32 | 25000 | 0.2600 | 0.1508 |
| 1.0804 | 13.59 | 25500 | 0.2620 | 0.1475 |
| 1.0814 | 13.85 | 26000 | 0.2537 | 0.1517 |
| 1.0693 | 14.12 | 26500 | 0.2560 | 0.1542 |
| 1.0724 | 14.38 | 27000 | 0.2540 | 0.1574 |
| 1.0704 | 14.65 | 27500 | 0.2548 | 0.1626 |
| 1.0729 | 14.92 | 28000 | 0.2548 | 0.1601 |
| 1.0724 | 15.18 | 28500 | 0.2511 | 0.1512 |
| 1.0655 | 15.45 | 29000 | 0.2498 | 0.1490 |
| 1.0608 | 15.98 | 30000 | 0.2487 | 0.1481 |
| 1.0541 | 16.52 | 31000 | 0.2468 | 0.1504 |
| 1.0584 | 17.05 | 32000 | 0.2467 | 0.1493 |
| 1.0507 | 17.58 | 33000 | 0.2481 | 0.1517 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
# Thanks
Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.
|
PereLluis13/wav2vec2-xls-r-300m-ca-lm
|
PereLluis13
| 2022-03-29T08:42:55Z | 20 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"collectivat/tv3_parla",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"projecte-aina/parlament_parla",
"robust-speech-event",
"ca",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:collectivat/tv3_parla",
"dataset:projecte-aina/parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ca
license: apache-2.0
tags:
- automatic-speech-recognition
- collectivat/tv3_parla
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- projecte-aina/parlament_parla
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- collectivat/tv3_parla
- projecte-aina/parlament_parla
model-index:
- name: wav2vec2-xls-r-300m-ca-lm
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 ca
type: mozilla-foundation/common_voice_8_0
args: ca
metrics:
- name: Test WER
type: wer
value: 6.771703090587865
- name: Test CER
type: cer
value: 2.1007777843712293
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: projecte-aina/parlament_parla ca
type: projecte-aina/parlament_parla
args: clean
metrics:
- name: Test WER
type: wer
value: 5.565360630662431
- name: Test CER
type: cer
value: 1.8594390167034354
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: collectivat/tv3_parla ca
type: collectivat/tv3_parla
args: ca
metrics:
- name: Test WER
type: wer
value: 13.53312545713516
- name: Test CER
type: cer
value: 8.684635913340556
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Catalan Dev Data
type: speech-recognition-community-v2/dev_data
args: ca
metrics:
- name: Test WER
type: wer
value: 26.04515843400164
- name: Test CER
type: cer
value: 15.056890012642224
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ca
metrics:
- name: Test WER
type: wer
value: 17.68
---
# wav2vec2-xls-r-300m-ca-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets.
It achieves the following results on the evaluation set (for the three datasets and without the LM):
- Loss: 0.2472
- Wer: 0.1499
## Model description
Please check the original [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) Model card. This is just a finetuned version of that model.
## Intended uses & limitations
As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.
## Training and evaluation data
More information needed
## Training procedure
The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 18.0
- mixed_precision_training: Native AMP
### Training results
Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.2099 | 0.09 | 500 | 3.4125 | 1.0 |
| 2.9961 | 0.18 | 1000 | 2.9224 | 1.0 |
| 2.2147 | 0.26 | 1500 | 0.6521 | 0.5568 |
| 1.3017 | 0.35 | 2000 | 0.3153 | 0.2761 |
| 1.1196 | 0.44 | 2500 | 0.2444 | 0.2367 |
| 1.0712 | 0.53 | 3000 | 0.2324 | 0.2132 |
| 1.052 | 0.62 | 3500 | 0.2173 | 0.2032 |
| 1.2813 | 2.13 | 4000 | 0.3326 | 0.2099 |
| 1.2365 | 2.4 | 4500 | 0.3224 | 0.2003 |
| 1.2193 | 2.66 | 5000 | 0.3198 | 0.1957 |
| 1.2072 | 2.93 | 5500 | 0.3063 | 0.1933 |
| 1.213 | 3.2 | 6000 | 0.3051 | 0.1980 |
| 1.2074 | 3.46 | 6500 | 0.3012 | 0.1879 |
| 1.1918 | 3.73 | 7000 | 0.2947 | 0.1829 |
| 1.1893 | 4.0 | 7500 | 0.2895 | 0.1807 |
| 1.1751 | 4.26 | 8000 | 0.2878 | 0.1776 |
| 1.1628 | 4.53 | 8500 | 0.2835 | 0.1731 |
| 1.1577 | 4.79 | 9000 | 0.2816 | 0.1761 |
| 1.1448 | 5.06 | 9500 | 0.2757 | 0.1740 |
| 1.1407 | 5.33 | 10000 | 0.2768 | 0.1798 |
| 1.1401 | 5.59 | 10500 | 0.2780 | 0.1816 |
| 1.1333 | 5.86 | 11000 | 0.2748 | 0.1750 |
| 1.1571 | 6.13 | 11500 | 0.2808 | 0.1708 |
| 1.1505 | 6.39 | 12000 | 0.2726 | 0.1692 |
| 1.1519 | 6.66 | 12500 | 0.2749 | 0.1654 |
| 1.136 | 6.93 | 13000 | 0.2765 | 0.1643 |
| 1.1326 | 7.19 | 13500 | 0.2706 | 0.1668 |
| 1.1342 | 7.46 | 14000 | 0.2665 | 0.1638 |
| 1.1286 | 7.72 | 14500 | 0.2669 | 0.1636 |
| 1.1243 | 7.99 | 15000 | 0.2619 | 0.1623 |
| 1.1173 | 8.26 | 15500 | 0.2652 | 0.1604 |
| 1.1129 | 8.52 | 16000 | 0.2610 | 0.1598 |
| 1.1091 | 8.79 | 16500 | 0.2608 | 0.1584 |
| 1.1053 | 9.06 | 17000 | 0.2633 | 0.1664 |
| 1.1004 | 9.32 | 17500 | 0.2594 | 0.1662 |
| 1.0995 | 9.59 | 18000 | 0.2623 | 0.1569 |
| 1.0964 | 9.86 | 18500 | 0.2624 | 0.1597 |
| 1.09 | 10.12 | 19000 | 0.2577 | 0.1578 |
| 1.089 | 10.39 | 19500 | 0.2574 | 0.1531 |
| 1.0864 | 10.66 | 20000 | 0.2556 | 0.1546 |
| 1.0806 | 10.92 | 20500 | 0.2548 | 0.1583 |
| 1.0842 | 11.19 | 21000 | 0.2550 | 0.1542 |
| 1.0805 | 11.45 | 21500 | 0.2561 | 0.1524 |
| 1.0722 | 11.72 | 22000 | 0.2540 | 0.1566 |
| 1.0763 | 11.99 | 22500 | 0.2549 | 0.1572 |
| 1.0835 | 12.25 | 23000 | 0.2586 | 0.1521 |
| 1.0883 | 12.52 | 23500 | 0.2583 | 0.1519 |
| 1.0888 | 12.79 | 24000 | 0.2551 | 0.1582 |
| 1.0933 | 13.05 | 24500 | 0.2628 | 0.1537 |
| 1.0799 | 13.32 | 25000 | 0.2600 | 0.1508 |
| 1.0804 | 13.59 | 25500 | 0.2620 | 0.1475 |
| 1.0814 | 13.85 | 26000 | 0.2537 | 0.1517 |
| 1.0693 | 14.12 | 26500 | 0.2560 | 0.1542 |
| 1.0724 | 14.38 | 27000 | 0.2540 | 0.1574 |
| 1.0704 | 14.65 | 27500 | 0.2548 | 0.1626 |
| 1.0729 | 14.92 | 28000 | 0.2548 | 0.1601 |
| 1.0724 | 15.18 | 28500 | 0.2511 | 0.1512 |
| 1.0655 | 15.45 | 29000 | 0.2498 | 0.1490 |
| 1.0608 | 15.98 | 30000 | 0.2487 | 0.1481 |
| 1.0541 | 16.52 | 31000 | 0.2468 | 0.1504 |
| 1.0584 | 17.05 | 32000 | 0.2467 | 0.1493 |
| 1.0507 | 17.58 | 33000 | 0.2481 | 0.1517 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
# Thanks
Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.
|
PereLluis13/wav2vec2-xls-r-1b-ca-lm
|
PereLluis13
| 2022-03-29T08:41:46Z | 3,126 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"collectivat/tv3_parla",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"projecte-aina/parlament_parla",
"robust-speech-event",
"ca",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:collectivat/tv3_parla",
"dataset:projecte-aina/parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ca
license: apache-2.0
tags:
- automatic-speech-recognition
- collectivat/tv3_parla
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- projecte-aina/parlament_parla
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- collectivat/tv3_parla
- projecte-aina/parlament_parla
model-index:
- name: wav2vec2-xls-r-1b-ca-lm
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 ca
type: mozilla-foundation/common_voice_8_0
args: ca
metrics:
- name: Test WER
type: wer
value: 6.0722669958130644
- name: Test CER
type: cer
value: 1.9180697705166526
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: projecte-aina/parlament_parla ca
type: projecte-aina/parlament_parla
args: clean
metrics:
- name: Test WER
type: wer
value: 5.139820371024042
- name: Test CER
type: cer
value: 2.0163620128164722
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: collectivat/tv3_parla ca
type: collectivat/tv3_parla
args: ca
metrics:
- name: Test WER
type: wer
value: 11.207991684952073
- name: Test CER
type: cer
value: 7.32119307305963
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Catalan Dev Data
type: speech-recognition-community-v2/dev_data
args: ca
metrics:
- name: Test WER
type: wer
value: 22.870153690468661
- name: Test CER
type: cer
value: 13.59039190897598
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ca
metrics:
- name: Test WER
type: wer
value: 15.41
---
# wav2vec2-xls-r-1b-ca-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets.
## Model description
Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model.
## Intended uses & limitations
As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.
## Training and evaluation data
## Training procedure
The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).
### Training results
Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
# Thanks
Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.
|
jorge-henao/gpt2-small-spanish-disco-poetry-15
|
jorge-henao
| 2022-03-29T05:17:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-29T04:20:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-small-spanish-disco-poetry-15
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. -->
# gpt2-small-spanish-disco-poetry-15
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2465
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
rampasek/prot_bert_bfd_rosetta204060aa
|
rampasek
| 2022-03-29T04:35:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"protein language model",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-29T04:02:40Z |
---
language: protein
tags:
- protein language model
datasets:
- BFD
- Custom Rosetta
---
# ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences:
100k of 20AA, 100k of 40AA, and 100k of 60AA
Current model in this repo: `prot_bert_bfd-finetuned-032822_1323`
## Performance
- 20AA sequences (1k eval set):\
Metrics: 'mae': 0.100418, 'r2': 0.989028, 'mse': 0.016266, 'rmse': 0.127537
- 40AA sequences (10k eval set):\
Metrics: 'mae': 0.173888, 'r2': 0.963361, 'mse': 0.048218, 'rmse': 0.219587
- 60AA sequences (10k eval set):\
Metrics: 'mae': 0.235238, 'r2': 0.930164, 'mse': 0.088131, 'rmse': 0.2968
## `prot_bert_bfd` from ProtTrans
The starting pretrained model is from ProtTrans, trained on 2.1 billion proteins from BFD.
It was trained on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans).
> Created by [Ladislav Rampasek](https://rampasek.github.io)
|
rampasek/prot_bert_bfd_rosetta20aa
|
rampasek
| 2022-03-29T04:33:02Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"protein language model",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T04:13:53Z |
---
language: protein
tags:
- protein language model
datasets:
- BFD
- Custom Rosetta
---
# ProtBert-BFD finetuned on Rosetta 20AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.
Current model in this repo: `prot_bert_bfd-finetuned-032722_1752`
## Performance
- 20AA sequences (1k eval set):\
Metrics: 'mae': 0.090115, 'r2': 0.991208, 'mse': 0.013034, 'rmse': 0.114165
- 40AA sequences (10k eval set):\
Metrics: 'mae': 0.537456, 'r2': 0.659122, 'mse': 0.448607, 'rmse': 0.669781
- 60AA sequences (10k eval set):\
Metrics: 'mae': 0.629267, 'r2': 0.506747, 'mse': 0.622476, 'rmse': 0.788972
## `prot_bert_bfd` from ProtTrans
The starting pretrained model is from ProtTrans, trained on 2.1 billion proteins from BFD.
It was trained on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans).
> Created by [Ladislav Rampasek](https://rampasek.github.io)
|
DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9
|
DrishtiSharma
| 2022-03-29T00:52:52Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-29T00:13:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-sentiment-mesd-v9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-sentiment-mesd-v9
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: 0.3500
- Accuracy: 0.9154
## 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: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.86 | 3 | 1.7825 | 0.1846 |
| 1.9553 | 1.86 | 6 | 1.7212 | 0.4308 |
| 1.9553 | 2.86 | 9 | 1.6164 | 0.3769 |
| 2.002 | 3.86 | 12 | 1.4904 | 0.3769 |
| 1.6191 | 4.86 | 15 | 1.4426 | 0.4385 |
| 1.6191 | 5.86 | 18 | 1.3516 | 0.5231 |
| 1.6209 | 6.86 | 21 | 1.2176 | 0.5538 |
| 1.6209 | 7.86 | 24 | 1.1683 | 0.5692 |
| 1.371 | 8.86 | 27 | 1.0885 | 0.5923 |
| 1.1568 | 9.86 | 30 | 1.0152 | 0.6385 |
| 1.1568 | 10.86 | 33 | 0.9289 | 0.6385 |
| 1.1023 | 11.86 | 36 | 0.9141 | 0.6308 |
| 1.1023 | 12.86 | 39 | 0.8526 | 0.6462 |
| 0.9448 | 13.86 | 42 | 0.8420 | 0.6769 |
| 0.7972 | 14.86 | 45 | 0.7976 | 0.6692 |
| 0.7972 | 15.86 | 48 | 0.8192 | 0.7308 |
| 0.7793 | 16.86 | 51 | 0.7108 | 0.7615 |
| 0.7793 | 17.86 | 54 | 0.6712 | 0.7769 |
| 0.6468 | 18.86 | 57 | 0.6684 | 0.7923 |
| 0.5083 | 19.86 | 60 | 0.6922 | 0.7385 |
| 0.5083 | 20.86 | 63 | 0.6148 | 0.7923 |
| 0.4988 | 21.86 | 66 | 0.5846 | 0.7923 |
| 0.4988 | 22.86 | 69 | 0.6050 | 0.8154 |
| 0.4123 | 23.86 | 72 | 0.5506 | 0.7846 |
| 0.3511 | 24.86 | 75 | 0.6095 | 0.7846 |
| 0.3511 | 25.86 | 78 | 0.5916 | 0.8154 |
| 0.3268 | 26.86 | 81 | 0.5912 | 0.8077 |
| 0.3268 | 27.86 | 84 | 0.5142 | 0.8538 |
| 0.3036 | 28.86 | 87 | 0.5492 | 0.8077 |
| 0.3066 | 29.86 | 90 | 0.6007 | 0.8231 |
| 0.3066 | 30.86 | 93 | 0.5748 | 0.8231 |
| 0.2538 | 31.86 | 96 | 0.6027 | 0.7692 |
| 0.2538 | 32.86 | 99 | 0.6979 | 0.7462 |
| 0.2281 | 33.86 | 102 | 0.7002 | 0.7615 |
| 0.2183 | 34.86 | 105 | 0.6650 | 0.7769 |
| 0.2183 | 35.86 | 108 | 0.5192 | 0.8462 |
| 0.2202 | 36.86 | 111 | 0.5389 | 0.8308 |
| 0.2202 | 37.86 | 114 | 0.5050 | 0.8385 |
| 0.1906 | 38.86 | 117 | 0.5722 | 0.7769 |
| 0.154 | 39.86 | 120 | 0.5239 | 0.8308 |
| 0.154 | 40.86 | 123 | 0.4448 | 0.8615 |
| 0.1474 | 41.86 | 126 | 0.4623 | 0.8615 |
| 0.1474 | 42.86 | 129 | 0.4282 | 0.8615 |
| 0.1345 | 43.86 | 132 | 0.5087 | 0.8615 |
| 0.1567 | 44.86 | 135 | 0.4859 | 0.8385 |
| 0.1567 | 45.86 | 138 | 0.6603 | 0.8077 |
| 0.1731 | 46.86 | 141 | 0.5379 | 0.8385 |
| 0.1731 | 47.86 | 144 | 0.8666 | 0.7538 |
| 0.1606 | 48.86 | 147 | 0.7518 | 0.8 |
| 0.1484 | 49.86 | 150 | 0.5986 | 0.8385 |
| 0.1484 | 50.86 | 153 | 0.6368 | 0.8231 |
| 0.2256 | 51.86 | 156 | 0.4639 | 0.8692 |
| 0.2256 | 52.86 | 159 | 0.5533 | 0.8462 |
| 0.1178 | 53.86 | 162 | 0.5038 | 0.8615 |
| 0.0815 | 54.86 | 165 | 0.5052 | 0.8692 |
| 0.0815 | 55.86 | 168 | 0.4337 | 0.8846 |
| 0.0998 | 56.86 | 171 | 0.4422 | 0.8769 |
| 0.0998 | 57.86 | 174 | 0.4317 | 0.8692 |
| 0.0855 | 58.86 | 177 | 0.4025 | 0.8923 |
| 0.0962 | 59.86 | 180 | 0.4605 | 0.8769 |
| 0.0962 | 60.86 | 183 | 0.4356 | 0.8769 |
| 0.0763 | 61.86 | 186 | 0.4614 | 0.8769 |
| 0.0763 | 62.86 | 189 | 0.4382 | 0.8846 |
| 0.0902 | 63.86 | 192 | 0.4701 | 0.8692 |
| 0.0654 | 64.86 | 195 | 0.4922 | 0.8692 |
| 0.0654 | 65.86 | 198 | 0.5413 | 0.8538 |
| 0.0651 | 66.86 | 201 | 0.5759 | 0.8615 |
| 0.0651 | 67.86 | 204 | 0.4238 | 0.9 |
| 0.0822 | 68.86 | 207 | 0.3500 | 0.9154 |
| 0.0625 | 69.86 | 210 | 0.3878 | 0.8923 |
| 0.0625 | 70.86 | 213 | 0.4952 | 0.8615 |
| 0.0548 | 71.86 | 216 | 0.4544 | 0.8615 |
| 0.0548 | 72.86 | 219 | 0.5497 | 0.8769 |
| 0.054 | 73.86 | 222 | 0.4434 | 0.8846 |
| 0.0543 | 74.86 | 225 | 0.4732 | 0.8769 |
| 0.0543 | 75.86 | 228 | 0.4425 | 0.8923 |
| 0.0881 | 76.86 | 231 | 0.4788 | 0.8769 |
| 0.0881 | 77.86 | 234 | 0.5448 | 0.8769 |
| 0.061 | 78.86 | 237 | 0.4221 | 0.9077 |
| 0.0567 | 79.86 | 240 | 0.4404 | 0.8769 |
| 0.0567 | 80.86 | 243 | 0.4099 | 0.9 |
| 0.052 | 81.86 | 246 | 0.5259 | 0.8769 |
| 0.052 | 82.86 | 249 | 0.5874 | 0.8692 |
| 0.0444 | 83.86 | 252 | 0.5555 | 0.8846 |
| 0.0332 | 84.86 | 255 | 0.5156 | 0.8615 |
| 0.0332 | 85.86 | 258 | 0.4564 | 0.8615 |
| 0.0449 | 86.86 | 261 | 0.4826 | 0.8692 |
| 0.0449 | 87.86 | 264 | 0.4726 | 0.8615 |
| 0.0385 | 88.86 | 267 | 0.4206 | 0.8846 |
| 0.0356 | 89.86 | 270 | 0.4050 | 0.8769 |
| 0.0356 | 90.86 | 273 | 0.4161 | 0.8923 |
| 0.0391 | 91.86 | 276 | 0.4100 | 0.9077 |
| 0.0391 | 92.86 | 279 | 0.4047 | 0.9 |
| 0.0249 | 93.86 | 282 | 0.4044 | 0.9 |
| 0.0399 | 94.86 | 285 | 0.3968 | 0.8846 |
| 0.0399 | 95.86 | 288 | 0.3802 | 0.9 |
| 0.031 | 96.86 | 291 | 0.3689 | 0.9 |
| 0.031 | 97.86 | 294 | 0.3616 | 0.9077 |
| 0.036 | 98.86 | 297 | 0.3584 | 0.9077 |
| 0.0386 | 99.86 | 300 | 0.3574 | 0.9077 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Chikashi/t5-small-finetuned-cnndm1
|
Chikashi
| 2022-03-28T22:00:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-28T14:55:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnndm1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.4246
---
<!-- 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-cnndm1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6853
- Rouge1: 24.4246
- Rouge2: 11.6944
- Rougel: 20.1717
- Rougelsum: 23.0424
- Gen Len: 18.9996
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.912 | 0.14 | 5000 | 1.7167 | 24.4232 | 11.7049 | 20.1758 | 23.0345 | 18.9997 |
| 1.8784 | 0.28 | 10000 | 1.7018 | 24.4009 | 11.6918 | 20.1561 | 23.0073 | 18.9997 |
| 1.8628 | 0.42 | 15000 | 1.6934 | 24.385 | 11.683 | 20.1285 | 22.9823 | 18.9997 |
| 1.8594 | 0.56 | 20000 | 1.6902 | 24.4407 | 11.6835 | 20.1734 | 23.0369 | 18.9996 |
| 1.8537 | 0.7 | 25000 | 1.6864 | 24.3635 | 11.658 | 20.1318 | 22.9782 | 18.9993 |
| 1.8505 | 0.84 | 30000 | 1.6856 | 24.4267 | 11.6991 | 20.1629 | 23.0361 | 18.9994 |
| 1.8505 | 0.98 | 35000 | 1.6853 | 24.4246 | 11.6944 | 20.1717 | 23.0424 | 18.9996 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
hf-test/xls-r-300m-sv
|
hf-test
| 2022-03-28T20:07:57Z | 28 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hello",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hello
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- sv
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Swedish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 16.98
- name: Test CER
type: cer
value: 5.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 27.01
- name: Test CER
type: cer
value: 13.14
---
<!-- 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. -->
# XLS-R-300m-SV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3171
- Wer: 0.2468
## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3349 | 1.45 | 500 | 3.2858 | 1.0 |
| 2.9298 | 2.91 | 1000 | 2.9225 | 1.0000 |
| 2.0839 | 4.36 | 1500 | 1.1546 | 0.8295 |
| 1.7093 | 5.81 | 2000 | 0.6827 | 0.5701 |
| 1.5855 | 7.27 | 2500 | 0.5597 | 0.4947 |
| 1.4831 | 8.72 | 3000 | 0.4923 | 0.4527 |
| 1.4416 | 10.17 | 3500 | 0.4670 | 0.4270 |
| 1.3848 | 11.63 | 4000 | 0.4341 | 0.3980 |
| 1.3749 | 13.08 | 4500 | 0.4203 | 0.4011 |
| 1.3311 | 14.53 | 5000 | 0.4310 | 0.3961 |
| 1.317 | 15.99 | 5500 | 0.3898 | 0.4322 |
| 1.2799 | 17.44 | 6000 | 0.3806 | 0.3572 |
| 1.2771 | 18.89 | 6500 | 0.3828 | 0.3427 |
| 1.2451 | 20.35 | 7000 | 0.3702 | 0.3359 |
| 1.2182 | 21.8 | 7500 | 0.3685 | 0.3270 |
| 1.2152 | 23.26 | 8000 | 0.3650 | 0.3308 |
| 1.1837 | 24.71 | 8500 | 0.3568 | 0.3187 |
| 1.1721 | 26.16 | 9000 | 0.3659 | 0.3249 |
| 1.1764 | 27.61 | 9500 | 0.3547 | 0.3145 |
| 1.1606 | 29.07 | 10000 | 0.3514 | 0.3104 |
| 1.1431 | 30.52 | 10500 | 0.3469 | 0.3062 |
| 1.1047 | 31.97 | 11000 | 0.3313 | 0.2979 |
| 1.1315 | 33.43 | 11500 | 0.3298 | 0.2992 |
| 1.1022 | 34.88 | 12000 | 0.3296 | 0.2973 |
| 1.0935 | 36.34 | 12500 | 0.3278 | 0.2926 |
| 1.0676 | 37.79 | 13000 | 0.3208 | 0.2868 |
| 1.0571 | 39.24 | 13500 | 0.3322 | 0.2885 |
| 1.0536 | 40.7 | 14000 | 0.3245 | 0.2831 |
| 1.0525 | 42.15 | 14500 | 0.3285 | 0.2826 |
| 1.0464 | 43.6 | 15000 | 0.3223 | 0.2796 |
| 1.0415 | 45.06 | 15500 | 0.3166 | 0.2774 |
| 1.0356 | 46.51 | 16000 | 0.3177 | 0.2746 |
| 1.04 | 47.96 | 16500 | 0.3150 | 0.2735 |
| 1.0209 | 49.42 | 17000 | 0.3175 | 0.2731 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id hf-test/xls-r-300m-sv --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id hf-test/xls-r-300m-sv --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "hf-test/xls-r-300m-sv"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "sv-SE", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "jag lämnade grovjobbet åt honom"
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 24.68 | 16.98 |
|
DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v2
|
DrishtiSharma
| 2022-03-28T19:04:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-28T17:20:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-sentiment-mesd-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-sentiment-mesd-v2
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: 1.7213
- Accuracy: 0.3923
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.25e-05
- train_batch_size: 64
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.86 | 3 | 1.7961 | 0.1462 |
| 1.9685 | 1.86 | 6 | 1.7932 | 0.1692 |
| 1.9685 | 2.86 | 9 | 1.7891 | 0.2 |
| 2.1386 | 3.86 | 12 | 1.7820 | 0.2923 |
| 1.9492 | 4.86 | 15 | 1.7750 | 0.2923 |
| 1.9492 | 5.86 | 18 | 1.7684 | 0.2846 |
| 2.1143 | 6.86 | 21 | 1.7624 | 0.3231 |
| 2.1143 | 7.86 | 24 | 1.7561 | 0.3308 |
| 2.0945 | 8.86 | 27 | 1.7500 | 0.3462 |
| 1.9121 | 9.86 | 30 | 1.7443 | 0.3385 |
| 1.9121 | 10.86 | 33 | 1.7386 | 0.3231 |
| 2.0682 | 11.86 | 36 | 1.7328 | 0.3231 |
| 2.0682 | 12.86 | 39 | 1.7272 | 0.3769 |
| 2.0527 | 13.86 | 42 | 1.7213 | 0.3923 |
| 1.8705 | 14.86 | 45 | 1.7154 | 0.3846 |
| 1.8705 | 15.86 | 48 | 1.7112 | 0.3846 |
| 2.0263 | 16.86 | 51 | 1.7082 | 0.3769 |
| 2.0263 | 17.86 | 54 | 1.7044 | 0.3846 |
| 2.0136 | 18.86 | 57 | 1.7021 | 0.3846 |
| 1.8429 | 19.86 | 60 | 1.7013 | 0.3846 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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