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Sin/DialoGPT-small-zai | Sin | 2021-10-21T23:21:07Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | conver = pipeline("conversational")
---
tags:
- conversational
---
# Harry potter DialoGPT model |
aditeyabaral/sentencetransformer-distilbert-base-cased | aditeyabaral | 2021-10-21T22:30:29Z | 129 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-distilbert-base-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-distilbert-base-cased')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-distilbert-base-cased')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-distilbert-base-cased')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-distilbert-base-cased)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9234 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Neuralearn/autonlp-Summarization-AutoNLP-24135330 | Neuralearn | 2021-10-21T21:44:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:Neuralearn/autonlp-data-Summarization-AutoNLP",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Neuralearn/autonlp-data-Summarization-AutoNLP
co2_eq_emissions: 155.8470724053265
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 24135330
- CO2 Emissions (in grams): 155.8470724053265
## Validation Metrics
- Loss: 1.369327425956726
- Rouge1: 52.6656
- Rouge2: 30.5879
- RougeL: 40.1268
- RougeLsum: 47.4438
- Gen Len: 75.4625
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/Neuralearn/autonlp-Summarization-AutoNLP-24135330
``` |
pritoms/distilgpt2-finetuned-wikitext2 | pritoms | 2021-10-21T21:16:24Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0540
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 130 | 3.1733 |
| No log | 2.0 | 260 | 3.0756 |
| No log | 3.0 | 390 | 3.0540 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
JonatanGk/roberta-base-bne-finetuned-sqac | JonatanGk | 2021-10-21T21:06:47Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:sqac",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sqac
model-index:
- name: roberta-base-bne-finetuned-sqac
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the sqac dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9924 | 1.0 | 1196 | 0.8670 |
| 0.474 | 2.0 | 2392 | 0.8923 |
| 0.1637 | 3.0 | 3588 | 1.2066 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/degg-dril-fred_delicious | huggingtweets | 2021-10-21T19:39:06Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/degg-dril-fred_delicious/1634845142916/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/58546628/goat22_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/726824334002638848/BEZFr1k8_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & deg & Fred Delicious</div>
<div style="text-align: center; font-size: 14px;">@degg-dril-fred_delicious</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & deg & Fred Delicious.
| Data | wint | deg | Fred Delicious |
| --- | --- | --- | --- |
| Tweets downloaded | 3227 | 3152 | 3235 |
| Retweets | 473 | 142 | 429 |
| Short tweets | 318 | 42 | 398 |
| Tweets kept | 2436 | 2968 | 2408 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mwoed1f/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 @degg-dril-fred_delicious's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a691ucn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a691ucn/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/degg-dril-fred_delicious')
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)
|
lewtun/xlm-roberta-base-finetuned-marc-en | lewtun | 2021-10-21T18:53:52Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8850
- Mae: 0.4390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1589 | 1.0 | 235 | 0.9769 | 0.5122 |
| 0.974 | 2.0 | 470 | 0.8850 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
patrickvonplaten/unispeech-sat-large-timit-ft | patrickvonplaten | 2021-10-21T16:38:43Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: unispeech-sat-large-timit-ft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# unispeech-sat-large-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6074
- Wer: 0.3880
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2516 | 0.69 | 100 | 5.8638 | 1.0 |
| 2.9596 | 1.38 | 200 | 2.9550 | 1.0 |
| 2.8831 | 2.07 | 300 | 2.8547 | 1.0 |
| 2.3223 | 2.76 | 400 | 2.2044 | 1.0063 |
| 1.2104 | 3.45 | 500 | 1.0845 | 0.7706 |
| 0.6779 | 4.14 | 600 | 0.7342 | 0.5663 |
| 0.6319 | 4.83 | 700 | 0.6054 | 0.4881 |
| 0.664 | 5.52 | 800 | 0.5808 | 0.4913 |
| 0.402 | 6.21 | 900 | 0.5647 | 0.4611 |
| 0.3176 | 6.9 | 1000 | 0.5211 | 0.4440 |
| 0.3392 | 7.59 | 1100 | 0.5187 | 0.4359 |
| 0.3888 | 8.28 | 1200 | 0.5501 | 0.4391 |
| 0.2874 | 8.97 | 1300 | 0.5249 | 0.4148 |
| 0.208 | 9.66 | 1400 | 0.5407 | 0.4152 |
| 0.1457 | 10.34 | 1500 | 0.5722 | 0.4155 |
| 0.2375 | 11.03 | 1600 | 0.5780 | 0.4059 |
| 0.2111 | 11.72 | 1700 | 0.5823 | 0.4094 |
| 0.1422 | 12.41 | 1800 | 0.5754 | 0.3977 |
| 0.125 | 13.1 | 1900 | 0.5784 | 0.4031 |
| 0.1996 | 13.79 | 2000 | 0.5630 | 0.3956 |
| 0.1747 | 14.48 | 2100 | 0.5880 | 0.3964 |
| 0.1263 | 15.17 | 2200 | 0.5987 | 0.3951 |
| 0.11 | 15.86 | 2300 | 0.5688 | 0.3964 |
| 0.1411 | 16.55 | 2400 | 0.6223 | 0.3906 |
| 0.1647 | 17.24 | 2500 | 0.6135 | 0.3960 |
| 0.1162 | 17.93 | 2600 | 0.6224 | 0.3960 |
| 0.098 | 18.62 | 2700 | 0.6017 | 0.3907 |
| 0.1183 | 19.31 | 2800 | 0.6121 | 0.3885 |
| 0.1717 | 20.0 | 2900 | 0.6074 | 0.3880 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
abhishek/autonlp-hindi-question-answering-23865268 | abhishek | 2021-10-21T13:51:44Z | 14 | 5 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"hi",
"dataset:abhishek/autonlp-data-hindi-question-answering",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- autonlp
- question-answering
language: hi
widget:
- text: "´सतीश धवन अंतरिक्ष केंद्र´ किस राज्य में स्थित है?"
context: "सतीश धवन अंतरिक्ष केंद्र, भारतीय अंतरिक्ष अनुसंधान संगठन (इसरो) का प्रक्षेपण केंद्र है। यह आंध्र प्रदेश के श्रीहरीकोटा में स्थित है, इसे 'श्रीहरीकोटा रेंज' या 'श्रीहरीकोटा लाँचिंग रेंज' के नाम से भी जाना जाता है। 2002 में इसरो के पूर्व प्रबंधक और वैज्ञानिक सतीश धवन के मरणोपरांत उनके सम्मान में इसका नाम बदला गया। प्रक्षेपण यान की असेम्\u200dबली के लिए दूसरा भवन केन्\u200dद्रीय मंत्रिमंडल ने 12 सितम्\u200dबर, 2013 को सतीश धवन अंतरिक्ष केन्\u200dद्र, श्रीहरिकोटा में प्रक्षेपण यान की असेम्\u200dबली के लिए दूसरे भवन के निर्माण की मंजूरी दी। इस पर 363.95 करोड़ रुपये की अनुमानित लागत आएगी, जिसमें सात करोड़ रुपये का खर्च विदेशी मुद्रा में होगा। इस दूसरी बिल्डिंग के उपलब्\u200dध हो जाने से पीएसएलवी और जीएसएलवी की प्रक्षेपण फ्रीक्वेंसी बढ़ेगी। यह जीएसएलवी एमके-III के एकीकरण के लिए वर्तमान व्\u200dहीकल असेम्\u200dबली बिल्डिंग को अतिरिक्\u200dत सुविधा मुहैया करायेगी। तीसरे प्रक्षेपण पैड तथा भविष्\u200dय में सामान्\u200dय यान प्रक्षेपण के लिए भी इससे काफी सुविधा मिलेगी।[1]\nलांच पैड\nउपग्रह प्रक्षेपण यान लॉन्च पैड\nइस लांच पैड से उपग्रह प्रक्षेपण यान और संवर्धित उपग्रह प्रक्षेपण यान को लांच किया गया था। यह वर्तमान प्रक्षेपण स्थल के दक्षिणी सिरे पर स्थित है। इसे सेवामुक्त कर दिया गया है। शुरू में इसे उपग्रह प्रक्षेपण यान लांच करने के लिए बनाया गया था। लेकिन बाद में इसे संवर्धित उपग्रह प्रक्षेपण यान प्रक्षेपण परिसर के रूप में इस्तेमाल किया गया था।\nप्रथम लांच पैड\nद्वितीय लॉन्च पैड\nतृतीय लांच पैड\nसन्दर्भ श्रेणी:भारतीय अंतरिक्ष अनुसंधान संगठन\nश्रेणी:भारत के रॉकेट प्रक्षेपण स्थल"
datasets:
- abhishek/autonlp-data-hindi-question-answering
co2_eq_emissions: 39.76330395590446
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-hindi-question-answering-23865268
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
``` |
tiennvcs/distilbert-base-uncased-finetuned-infovqa | tiennvcs | 2021-10-21T11:37:56Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-infovqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.02 | 100 | 4.7706 |
| No log | 0.05 | 200 | 4.4399 |
| No log | 0.07 | 300 | 3.8175 |
| No log | 0.09 | 400 | 3.8306 |
| 3.3071 | 0.12 | 500 | 3.6480 |
| 3.3071 | 0.14 | 600 | 3.6451 |
| 3.3071 | 0.16 | 700 | 3.4974 |
| 3.3071 | 0.19 | 800 | 3.4686 |
| 3.3071 | 0.21 | 900 | 3.4703 |
| 3.5336 | 0.23 | 1000 | 3.3165 |
| 3.5336 | 0.25 | 1100 | 3.3634 |
| 3.5336 | 0.28 | 1200 | 3.3466 |
| 3.5336 | 0.3 | 1300 | 3.3411 |
| 3.5336 | 0.32 | 1400 | 3.2456 |
| 3.3593 | 0.35 | 1500 | 3.3257 |
| 3.3593 | 0.37 | 1600 | 3.2941 |
| 3.3593 | 0.39 | 1700 | 3.2581 |
| 3.3593 | 0.42 | 1800 | 3.1680 |
| 3.3593 | 0.44 | 1900 | 3.2077 |
| 3.2436 | 0.46 | 2000 | 3.2422 |
| 3.2436 | 0.49 | 2100 | 3.2529 |
| 3.2436 | 0.51 | 2200 | 3.2681 |
| 3.2436 | 0.53 | 2300 | 3.1055 |
| 3.2436 | 0.56 | 2400 | 3.0174 |
| 3.093 | 0.58 | 2500 | 3.0608 |
| 3.093 | 0.6 | 2600 | 3.0200 |
| 3.093 | 0.63 | 2700 | 2.9884 |
| 3.093 | 0.65 | 2800 | 3.0041 |
| 3.093 | 0.67 | 2900 | 2.9700 |
| 3.0087 | 0.69 | 3000 | 3.0993 |
| 3.0087 | 0.72 | 3100 | 3.0499 |
| 3.0087 | 0.74 | 3200 | 2.9317 |
| 3.0087 | 0.76 | 3300 | 3.0817 |
| 3.0087 | 0.79 | 3400 | 3.0035 |
| 2.9694 | 0.81 | 3500 | 3.0850 |
| 2.9694 | 0.83 | 3600 | 2.9948 |
| 2.9694 | 0.86 | 3700 | 2.9874 |
| 2.9694 | 0.88 | 3800 | 2.9202 |
| 2.9694 | 0.9 | 3900 | 2.9322 |
| 2.8277 | 0.93 | 4000 | 2.9195 |
| 2.8277 | 0.95 | 4100 | 2.8638 |
| 2.8277 | 0.97 | 4200 | 2.8809 |
| 2.8277 | 1.0 | 4300 | 2.8872 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anton-l/wav2vec2-base-finetuned-ks | anton-l | 2021-10-21T11:04:30Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7908 | 1.0 | 399 | 0.6776 | 0.9009 |
| 0.3202 | 2.0 | 798 | 0.2061 | 0.9763 |
| 0.221 | 3.0 | 1197 | 0.1257 | 0.9785 |
| 0.1773 | 4.0 | 1596 | 0.0990 | 0.9813 |
| 0.1729 | 5.0 | 1995 | 0.0952 | 0.9823 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
BSC-LT/roberta-large-bne | BSC-LT | 2021-10-21T10:32:31Z | 37 | 7 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las <mask> Pedroche y Chicote."
- text: "El artista Antonio Orozco es un colaborador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne
# RoBERTa large trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BSC-LT/roberta-large-bne-sqac | BSC-LT | 2021-10-21T10:32:05Z | 28 | 3 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "qa"
- "question answering"
datasets:
- "BSC-TeMU/SQAC"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-sqac
# Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC).
## Evaluation and results
F1 Score: 0.7993 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BSC-LT/roberta-base-bne | BSC-LT | 2021-10-21T10:30:31Z | 2,054 | 9 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las presentará <mask>."
- text: "David Broncano es un presentador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
# RoBERTa base trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BSC-LT/roberta-base-bne-sqac | BSC-LT | 2021-10-21T10:30:10Z | 17 | 4 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "qa"
- "question answering"
datasets:
- "BSC-TeMU/SQAC"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac
# Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC).
## Evaluation and results
F1 Score: 0.7923 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BSC-LT/roberta-base-bne-capitel-pos | BSC-LT | 2021-10-21T10:29:55Z | 27 | 3 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "pos"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
widget:
- text: "Festival de San Sebastián: Johnny Depp recibirá el premio Donostia en pleno rifirrafe judicial con Amber Heard"
- text: "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."
- text: "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."
- text: "El Tribunal Superior de Justicia se pronunció ayer: \"Hay base legal dentro del marco jurídico actual\"."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).
## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BSC-LT/roberta-base-bne-capitel-ner | BSC-LT | 2021-10-21T10:29:35Z | 43 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
## Evaluation and results
F1 Score: 0.8960
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
MINYOUNG/distilbert-base-uncased-finetuned-cola | MINYOUNG | 2021-10-21T09:42:00Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5494735380761103
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8540
- Matthews Correlation: 0.5495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5219 | 1.0 | 535 | 0.5314 | 0.4095 |
| 0.346 | 2.0 | 1070 | 0.5141 | 0.5054 |
| 0.2294 | 3.0 | 1605 | 0.6351 | 0.5200 |
| 0.1646 | 4.0 | 2140 | 0.7575 | 0.5459 |
| 0.1235 | 5.0 | 2675 | 0.8540 | 0.5495 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
Roberta55/deberta-base-mnli-finetuned-cola | Roberta55 | 2021-10-21T09:07:56Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: deberta-base-mnli-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.6281691768918801
---
<!-- 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. -->
# deberta-base-mnli-finetuned-cola
This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/microsoft/deberta-base-mnli) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8205
- Matthews Correlation: 0.6282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4713 | 1.0 | 535 | 0.5110 | 0.5797 |
| 0.2678 | 2.0 | 1070 | 0.6648 | 0.5154 |
| 0.1811 | 3.0 | 1605 | 0.6681 | 0.6121 |
| 0.113 | 4.0 | 2140 | 0.8205 | 0.6282 |
| 0.0831 | 5.0 | 2675 | 1.0413 | 0.6057 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
pritoms/distilgpt2-finetuned-mit-lecture | pritoms | 2021-10-21T08:59:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-mit-lecture
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-mit-lecture
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8377
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 144 | 3.8737 |
| No log | 2.0 | 288 | 3.8436 |
| No log | 3.0 | 432 | 3.8377 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
tucan9389/distilbert-base-uncased-finetuned-cola | tucan9389 | 2021-10-21T00:28:21Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5308757570358055
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7501
- Matthews Correlation: 0.5309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5286 | 1.0 | 535 | 0.5067 | 0.4301 |
| 0.3469 | 2.0 | 1070 | 0.5216 | 0.4802 |
| 0.2343 | 3.0 | 1605 | 0.6431 | 0.5002 |
| 0.1753 | 4.0 | 2140 | 0.7501 | 0.5309 |
| 0.1251 | 5.0 | 2675 | 0.8695 | 0.5222 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT | AyushPJ | 2021-10-20T23:38:45Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-distilBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-club-inductions-21-nlp-distilBERT
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT | AyushPJ | 2021-10-20T23:28:44Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-ALBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-club-inductions-21-nlp-ALBERT
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/s66jewelevans | huggingtweets | 2021-10-20T23:06:38Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/s66jewelevans/1634771194675/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1313199276852342784/fJ8Lb2C__400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jewel Evans</div>
<div style="text-align: center; font-size: 14px;">@s66jewelevans</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jewel Evans.
| Data | Jewel Evans |
| --- | --- |
| Tweets downloaded | 1714 |
| Retweets | 2 |
| Short tweets | 20 |
| Tweets kept | 1692 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ec5yuuj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @s66jewelevans's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kxbfdnt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kxbfdnt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/s66jewelevans')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AyushPJ/ai-club-inductions-21-nlp-roBERTa | AyushPJ | 2021-10-20T22:33:57Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-club-inductions-21-nlp-roBERTa
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
monologg/koelectra-base-discriminator | monologg | 2021-10-20T16:55:57Z | 1,292 | 1 | transformers | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-discriminator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3]
```
## Example using ElectraForPreTraining
```python
import torch
from transformers import ElectraForPreTraining, ElectraTokenizer
discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
sentence = "나는 방금 밥을 먹었다."
fake_sentence = "나는 내일 밥을 먹었다."
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
print(list(zip(fake_tokens, predictions.tolist()[1:-1])))
```
|
monologg/koelectra-base-generator | monologg | 2021-10-20T16:55:00Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-generator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3]
```
## Example using ElectraForMaskedLM
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="monologg/koelectra-base-generator",
tokenizer="monologg/koelectra-base-generator"
)
print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token)))
```
|
monologg/koelectra-base-v2-generator | monologg | 2021-10-20T16:54:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v2 (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v2-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-v2-generator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v2-generator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v2-generator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 5084, 16248, 3770, 19059, 29965, 2259, 10431, 5, 3]
```
## Example using ElectraForMaskedLM
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="monologg/koelectra-base-v2-generator",
tokenizer="monologg/koelectra-base-v2-generator"
)
print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token)))
```
|
monologg/koelectra-base-v3-discriminator | monologg | 2021-10-20T16:53:40Z | 31,234 | 30 | transformers | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v3 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-v3-discriminator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##EC', '##TRA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 11229, 29173, 13352, 25541, 4110, 7824, 17788, 18, 3]
```
## Example using ElectraForPreTraining
```python
import torch
from transformers import ElectraForPreTraining, ElectraTokenizer
discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-v3-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")
sentence = "나는 방금 밥을 먹었다."
fake_sentence = "나는 내일 밥을 먹었다."
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
print(list(zip(fake_tokens, predictions.tolist()[1:-1])))
```
|
jbarry/irish-gpt2 | jbarry | 2021-10-20T16:40:12Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | This model was trained on the OSCAR ga dataset for experimental purposes. The files used for training the tokenizer and model are included in this repository. |
bochaowei/t5-small-finetuned-xsum-wei0 | bochaowei | 2021-10-20T15:10:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei0
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 25.7398
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6289
- Rouge1: 25.7398
- Rouge2: 6.1361
- Rougel: 19.8262
- Rougelsum: 19.8284
- Gen Len: 18.7984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
YushiUeda/test | YushiUeda | 2021-10-20T14:48:21Z | 4 | 0 | espnet | [
"espnet",
"audio",
"diarization",
"dataset:mini_librispeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
tags:
- espnet
- audio
- diarization
language:
datasets:
- mini_librispeech
license: cc-by-4.0
---
## ESPnet2 DIAR model
### `YushiUeda/test`
This model was trained by Yushi Ueda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4
pip install -e .
cd egs2/mini_librispeech/diar1
./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test
```
<!-- Generated by scripts/utils/show_diar_result.sh -->
# RESULTS
## Environments
- date: `Wed Aug 25 23:29:07 EDT 2021`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.2a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92`
- Commit date: `Tue Aug 24 19:50:44 2021 -0400`
## `diar_train_diar_raw_max_epoch20`
### DER
`dev_clean_2_ns2_beta2_500`
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med1_collar0.0|32.42|
|result_th0.3_med11_collar0.0|32.03|
|result_th0.4_med1_collar0.0|30.96|
|result_th0.4_med11_collar0.0|30.26|
|result_th0.5_med1_collar0.0|30.35|
|result_th0.5_med11_collar0.0|29.37|
|result_th0.6_med1_collar0.0|30.77|
|result_th0.6_med11_collar0.0|29.52|
|result_th0.7_med1_collar0.0|32.60|
|result_th0.7_med11_collar0.0|31.03|
## DIAR config
<details><summary>expand</summary>
```
config: conf/train_diar.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_train_diar_raw_max_epoch20
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 20
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 3
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_stats_8k/train/speech_shape
- exp/diar_stats_8k/train/spk_labels_shape
valid_shape_file:
- exp/diar_stats_8k/valid/speech_shape
- exp/diar_stats_8k/valid/spk_labels_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 200000
chunk_shift_ratio: 0.5
num_cache_chunks: 64
train_data_path_and_name_and_type:
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
valid_data_path_and_name_and_type:
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.01
scheduler: noamlr
scheduler_conf:
warmup_steps: 1000
num_spk: 2
init: xavier_uniform
input_size: null
model_conf:
loss_type: pit
use_preprocessor: true
frontend: default
frontend_conf:
fs: 8k
hop_length: 128
normalize: global_mvn
normalize_conf:
stats_file: exp/diar_stats_8k/train/feats_stats.npz
encoder: transformer
encoder_conf:
input_layer: linear
num_blocks: 2
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
decoder: linear
decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf: {}
required:
- output_dir
version: 0.10.2a1
distributed: false
```
</details>
|
Monsia/autonlp-tweets-classification-23044997 | Monsia | 2021-10-20T14:38:58Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:Monsia/autonlp-data-tweets-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Monsia/autonlp-data-tweets-classification
co2_eq_emissions: 4.819872182577655
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 23044997
- CO2 Emissions (in grams): 4.819872182577655
## Validation Metrics
- Loss: 0.001594889909029007
- Accuracy: 0.9997478885667465
- Macro F1: 0.9991190902836993
- Micro F1: 0.9997478885667465
- Weighted F1: 0.9997476735518704
- Macro Precision: 0.9998014460161265
- Micro Precision: 0.9997478885667465
- Weighted Precision: 0.9997479944069787
- Macro Recall: 0.9984426545713851
- Micro Recall: 0.9997478885667465
- Weighted Recall: 0.9997478885667465
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Monsia/autonlp-tweets-classification-23044997
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
huggingtweets/dril-linaarabii | huggingtweets | 2021-10-20T11:36:30Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/dril-linaarabii/1634729786636/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1423543147305619456/9RT-Ji0Z_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Lina Arabi</div>
<div style="text-align: center; font-size: 14px;">@dril-linaarabii</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Lina Arabi.
| Data | wint | Lina Arabi |
| --- | --- | --- |
| Tweets downloaded | 3227 | 3130 |
| Retweets | 473 | 896 |
| Short tweets | 317 | 322 |
| Tweets kept | 2437 | 1912 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yq3shwo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-linaarabii's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21rpwe17) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21rpwe17/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dril-linaarabii')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
facebook/hubert-xlarge-ll60k | facebook | 2021-10-20T10:20:44Z | 794 | 5 | transformers | [
"transformers",
"pytorch",
"tf",
"hubert",
"feature-extraction",
"speech",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- libri-light
tags:
- speech
license: apache-2.0
---
# Hubert-Extra-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
The model was pretrained on [Libri-Light](https://github.com/facebookresearch/libri-light).
[Paper](https://arxiv.org/abs/2106.07447)
Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed
**Abstract**
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `HubertForCTC`. |
lapcameraatp/cameragiamsat | lapcameraatp | 2021-10-20T08:53:25Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | ERROR: type should be string, got "https://camerasaigon24h.com\nhttps://cameragiamsat360.com\nhttps://lapdatcameracongty.vn\nhttps://lapdatcamerawifi.vn\nhttps://lapcamerawifi.com\nhttps://giacameraquansat.com\nhttps://cameraquansatre.com\nhttps://cameraanninhwifi.com\n\nhttps://camerawifigiadinh.com/\nhttps://lapcameratanphu.com\nhttp://camerathehemoi.com\nhttp://lapcameratanbinh.com\nhttp://lapcamerabinhtan.com\nhttp://lapcameraquan2giare.com\nhttp://cameraquan12.com\nhttp://cameraquan3giare.com\nhttp://lapdatcameraquan4.com\nhttp://lapdatcameraquan10.com\nhttp://lapdatcameraquan7.com\nhttp://camerabinhthanh.com\nhttp://lapcameraquan9giare.com\nhttp://lapdatcameraquan11.com\nhttp://lapcameragiarethuduc.com\nhttp://lapdatcameraquan6.com\nhttp://lapdatcameraquan5.com\nhttp://lapcameraquan1.com\nhttp://cameraquan8.com\nhttp://cameranhatranggiare.com\nhttp://lapcamerahocmon.com\nhttp://lapcameragiaregovap.com\nhttp://lapcameraphunhuan.com\nhttp://cameragiarebinhduong.com\nhttp://phanphoicameragiare.com\nhttp://camerawifigiadinh.com/\nhttp://cameraphanthietgiare.com/" |
mrm8488/t5-base-finetuned-break_data | mrm8488 | 2021-10-20T08:31:28Z | 962 | 3 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:break_data",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- break_data
widget:
- text: "paraphrase: The composer of Sands Theme plays what type of guitar?"
---
# T5-base fine-tuned on break_data / QDMR-high-level ❓➡️📋
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [break_data](https://huggingface.co/nlp/viewer/?dataset=break_data&config=QDMR-high-level) dataset for **QDMRs**.
## Details of T5 📜 ➡️ 📜
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (QDMRs) - Dataset 📚
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases. This repository contains the Break dataset along with information on the exact data format.
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| break_data | train | 17503 |
| break_data | valid | 3130 |
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The main change is at preprocessing ```inputs``` and ```targets``` we feed to the model. We do it as a *paraphrasing task*.
## Model in Action 🚀
```python
# Tip: By now, install transformers from source
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-break_data")
model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-break_data")
def get_decomposition(question):
input_text = "paraphrase: %s </s>" % question
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=32)
return tokenizer.decode(output[0])
question = "The composer of Sands Theme plays what type of guitar?"
get_decomposition(question)
# output: 'return Sands Theme ;return composer of #1 ;return guitar that #2 plays'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | Bagus | 2021-10-20T05:38:41Z | 37 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio",
"audio-classification",
"speech",
"el",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2022-03-02T23:29:04Z | ---
language: el
datasets:
- aesdd
tags:
- audio
- audio-classification
- speech
license: apache-2.0
---
~~~
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!git clone https://github.com/m3hrdadfi/soxan
cd soxan
~~~
# prediction
~~~
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
~~~
~~~
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
~~~
~~~
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torchaudio.load(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
~~~
# prediction
~~~
# path for a sample
path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'
outputs = predict(path, sampling_rate)
~~~
~~~
[{'Emotion': 'anger', 'Score': '98.3%'},
{'Emotion': 'disgust', 'Score': '0.0%'},
{'Emotion': 'fear', 'Score': '0.4%'},
{'Emotion': 'happiness', 'Score': '0.7%'},
{'Emotion': 'sadness', 'Score': '0.5%'}]
~~~ |
Manishl7/xlm-roberta-large-language-detection | Manishl7 | 2021-10-20T05:20:44Z | 20 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | Language Detection Model for Nepali, English, Hindi and Spanish
Model fine tuned on xlm-roberta-large |
huggingartists/adele | huggingartists | 2021-10-20T04:50:21Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/adele",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/adele
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4c3ac1f1d845d251671a892309b5f9b5.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Adele</div>
<a href="https://genius.com/artists/adele">
<div style="text-align: center; font-size: 14px;">@adele</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Adele.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/adele).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/adele")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1yyqw6ss/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Adele's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/adele')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/adele")
model = AutoModelWithLMHead.from_pretrained("huggingartists/adele")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingtweets/l3gacyb3ta | huggingtweets | 2021-10-19T23:49:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/l3gacyb3ta/1634687376092/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/1410799369016782849/rn80bxNq_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">Arcade</div>
<div style="text-align: center; font-size: 14px;">@l3gacyb3ta</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 Arcade.
| Data | Arcade |
| --- | --- |
| Tweets downloaded | 919 |
| Retweets | 283 |
| Short tweets | 91 |
| Tweets kept | 545 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/77o64yn7/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 @l3gacyb3ta's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12xpesbj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12xpesbj/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/l3gacyb3ta')
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)
|
yazdipour/text-to-sparql-t5-base-qald9 | yazdipour | 2021-10-19T23:25:20Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-base-2021-10-19_23-02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sparql-qald9-t5-base-2021-10-19_23-02
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_lastDS](https://huggingface.co/yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_lastDS) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:-----------------------------------------------------------------------------:|:-------:|
| No log | 1.0 | 51 | 1.8300 | 19.0 | 0.3640 | 0.0346 | 0.1943 | 10.0358 | [72.88988261598658, 50.27455765710799, 35.93015446608462, 28.454070201643017] | 0.2281 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
aditeyabaral/sentencetransformer-roberta-hinglish-small | aditeyabaral | 2021-10-19T22:53:39Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-roberta-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-roberta-hinglish-small')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-roberta-hinglish-small')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-roberta-hinglish-small')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-roberta-hinglish-small)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
aditeyabaral/sentencetransformer-roberta-hinglish-big | aditeyabaral | 2021-10-19T22:41:56Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-roberta-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('aditeyabaral/sentencetransformer-roberta-hinglish-big')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-roberta-hinglish-big')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-roberta-hinglish-big')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-roberta-hinglish-big)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4617 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
aguilara42/audacity-Wav2Vec2-Base | aguilara42 | 2021-10-19T21:23:28Z | 0 | 1 | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
tags:
- audacity
inference: false
---
# Text to Speech Model
## Being used for the `Audio Labeler` effect in Audacity
metadata:
```
{
metadata = {
'sample_rate': 16000,
'domain_tags': ['speech'],
'short_description': 'I will label your speech into text :]',
'long_description':
'This is an Audacity wrapper for the model, '
'forked from the repository '
'facebook/s2t-medium-librispeech-asr'
'This model was trained by Changhan Wang'
'and Yun Tang and Xutai Ma and Anne Wu'
'and Dmytro Okhonko and Juan Pino.',
'tags': ['speech-to-text'],
'effect_type': 'waveform-to-labels',
'multichannel': False,
'labels': ["<pad>", "<s>", "</s>", "<unk>", "|", "E", "T", "A", "O", "N", "I", "H", "S", "R", "D", "L", "U", "M", "W", "C", "F", "G", "Y", "P", "B", "V", "K", "'", "X", "J", "Q", "Z"],
}
``` |
huggingtweets/iamdevloper | huggingtweets | 2021-10-19T20:59:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/iamdevloper/1634677176847/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1178631635606151168/yIlrcg4o_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">I Am Devloper</div>
<div style="text-align: center; font-size: 14px;">@iamdevloper</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from I Am Devloper.
| Data | I Am Devloper |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 190 |
| Short tweets | 233 |
| Tweets kept | 2821 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2k1120ro/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iamdevloper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wr63mia) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wr63mia/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iamdevloper')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/gerardsans | huggingtweets | 2021-10-19T19:13:05Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/gerardsans/1634670781074/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/1431241007421665284/qoHnns8I_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">ᐸGerardSans/ᐳ🤣🇬🇧</div>
<div style="text-align: center; font-size: 14px;">@gerardsans</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 ᐸGerardSans/ᐳ🤣🇬🇧.
| Data | ᐸGerardSans/ᐳ🤣🇬🇧 |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 648 |
| Short tweets | 586 |
| Tweets kept | 2016 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/115pr1rh/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 @gerardsans's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10heg4by) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10heg4by/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/gerardsans')
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)
|
maxspaziani/bert-base-italian-xxl-uncased-finetuned-ComunaliRoma | maxspaziani | 2021-10-19T17:58:13Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6717 | 1.0 | 1014 | 2.6913 |
| 2.4869 | 2.0 | 2028 | 2.5843 |
| 2.3411 | 3.0 | 3042 | 2.5095 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
meghana/hitalmqa-finetuned-squad | meghana | 2021-10-19T17:34:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: hitalmqa-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hitalmqa-finetuned-squad
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Recognai/selectra_medium | Recognai | 2021-10-19T15:28:03Z | 5 | 3 | transformers | [
"transformers",
"pytorch",
"electra",
"pretraining",
"es",
"dataset:oscar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z | ---
language:
- es
thumbnail: "url to a thumbnail used in social sharing"
license: apache-2.0
datasets:
- oscar
---
# SELECTRA: A Spanish ELECTRA
SELECTRA is a Spanish pre-trained language model based on [ELECTRA](https://github.com/google-research/electra).
We release a `small` and `medium` version with the following configuration:
| Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased |
| --- | --- | --- | --- | --- | --- | --- |
| [SELECTRA small](https://huggingface.co/Recognai/selectra_small) | 12 | 256 | 22M | 50k | 512 | True |
| **SELECTRA medium** | **12** | **384** | **41M** | **50k** | **512** | **True** |
**SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results** (see Metrics section below).
## Usage
From the original [ELECTRA model card](https://huggingface.co/google/electra-small-discriminator): "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."
The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:
```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
discriminator = ElectraForPreTraining.from_pretrained("Recognai/selectra_small")
tokenizer = ElectraTokenizerFast.from_pretrained("Recognai/selectra_small")
sentence_with_fake_token = "Estamos desayunando pan rosa con tomate y aceite de oliva."
inputs = tokenizer.encode(sentence_with_fake_token, return_tensors="pt")
logits = discriminator(inputs).logits.tolist()[0]
print("\t".join(tokenizer.tokenize(sentence_with_fake_token)))
print("\t".join(map(lambda x: str(x)[:4], logits[1:-1])))
"""Output:
Estamos desayun ##ando pan rosa con tomate y aceite de oliva .
-3.1 -3.6 -6.9 -3.0 0.19 -4.5 -3.3 -5.1 -5.7 -7.7 -4.4 -4.2
"""
```
However, you probably want to use this model to fine-tune it on a downstream task.
We provide models fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which can be used together with the zero-shot classification pipeline:
- [Zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small)
- [Zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium)
## Metrics
We fine-tune our models on 3 different down-stream tasks:
- [XNLI](https://huggingface.co/datasets/xnli)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [CoNLL2002 - NER](https://huggingface.co/datasets/conll2002)
For each task, we conduct 5 trials and state the mean and standard deviation of the metrics in the table below.
To compare our results to other Spanish language models, we provide the same metrics taken from the [evaluation table](https://github.com/PlanTL-SANIDAD/lm-spanish#evaluation-) of the [Spanish Language Model](https://github.com/PlanTL-SANIDAD/lm-spanish) repo.
| Model | CoNLL2002 - NER (f1) | PAWS-X (acc) | XNLI (acc) | Params |
| --- | --- | --- | --- | --- |
| SELECTRA small | 0.865 +- 0.004 | 0.896 +- 0.002 | 0.784 +- 0.002 | 22M |
| SELECTRA medium | 0.873 +- 0.003 | 0.896 +- 0.002 | 0.804 +- 0.002 | 41M |
| | | | | |
| [mBERT](https://huggingface.co/bert-base-multilingual-cased) | 0.8691 | 0.8955 | 0.7876 | 178M |
| [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) | 0.8759 | 0.9000 | 0.8130 | 110M |
| [RoBERTa-b](https://huggingface.co/BSC-TeMU/roberta-base-bne) | 0.8851 | 0.9000 | 0.8016 | 125M |
| [RoBERTa-l](https://huggingface.co/BSC-TeMU/roberta-large-bne) | 0.8772 | 0.9060 | 0.7958 | 355M |
| [Bertin](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v1-512) | 0.8835 | 0.8990 | 0.7890 | 125M |
| [ELECTRICIDAD](https://huggingface.co/mrm8488/electricidad-base-discriminator) | 0.7954 | 0.9025 | 0.7878 | 109M |
Some details of our fine-tuning runs:
- epochs: 5
- batch-size: 32
- learning rate: 1e-4
- warmup proportion: 0.1
- linear learning rate decay
- layerwise learning rate decay
For all the details, check out our [selectra repo](https://github.com/recognai/selectra).
## Training
We pre-trained our SELECTRA models on the Spanish portion of the [Oscar](https://huggingface.co/datasets/oscar) dataset, which is about 150GB in size.
Each model version is trained for 300k steps, with a warm restart of the learning rate after the first 150k steps.
Some details of the training:
- steps: 300k
- batch-size: 128
- learning rate: 5e-4
- warmup steps: 10k
- linear learning rate decay
- TPU cores: 8 (v2-8)
For all details, check out our [selectra repo](https://github.com/recognai/selectra).
**Note:** Due to a misconfiguration in the pre-training scripts the embeddings of the vocabulary containing an accent were not optimized. If you fine-tune this model on a down-stream task, you might consider using a tokenizer that does not strip the accents:
```python
tokenizer = ElectraTokenizerFast.from_pretrained("Recognai/selectra_small", strip_accents=False)
```
## Motivation
Despite the abundance of excellent Spanish language models (BETO, BSC-BNE, Bertin, ELECTRICIDAD, etc.), we felt there was still a lack of distilled or compact Spanish language models and a lack of comparing those to their bigger siblings.
## Acknowledgment
This research was supported by the Google TPU Research Cloud (TRC) program.
## Authors
- David Fidalgo ([GitHub](https://github.com/dcfidalgo))
- Javier Lopez ([GitHub](https://github.com/javispp))
- Daniel Vila ([GitHub](https://github.com/dvsrepo))
- Francisco Aranda ([GitHub](https://github.com/frascuchon)) |
Fhrozen/test_an4 | Fhrozen | 2021-10-19T15:20:32Z | 3 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:an4",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:04Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- an4
license: cc-by-4.0
---
## ESPnet2 ASR model
### `Fhrozen/test_an4`
This model was trained by Fhrozen using an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b8df4c928e132acff78d196988bdb68a66987952
pip install -e .
cd egs2/an4/asr1
./run.sh --skip_data_prep false --skip_train true --download_model Fhrozen/test_an4
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Oct 20 00:00:46 JST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: `b8df4c928e132acff78d196988bdb68a66987952`
- Commit date: `Tue Oct 19 07:48:11 2021 -0400`
## asr_train_raw_en_bpe30
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|773|4.0|22.3|73.7|0.1|96.1|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|591|2.7|21.8|75.5|0.0|97.3|100.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2565|17.2|16.4|66.4|1.0|83.8|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|1915|15.5|16.4|68.1|0.9|85.5|100.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2695|21.1|15.6|63.3|0.9|79.9|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|2015|19.4|15.6|65.0|0.9|81.5|100.0|
## ASR config
<details><summary>expand</summary>
```
config: null
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_raw_en_bpe30
ngpu: 0
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe30/train/speech_shape
- exp/asr_stats_raw_en_bpe30/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe30/valid/speech_shape
- exp/asr_stats_raw_en_bpe30/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_nodev/wav.scp
- speech
- sound
- - dump/raw/train_nodev/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev/wav.scp
- speech
- sound
- - dump/raw/train_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf: {}
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- T
- E
- O
- R
- Y
- A
- H
- U
- S
- I
- F
- B
- L
- P
- D
- G
- M
- C
- V
- X
- J
- K
- Z
- W
- N
- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.5
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram30/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe30/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: rnn
encoder_conf: {}
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf: {}
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
## LM config
<details><summary>expand</summary>
```
config: conf/train_lm.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/lm_train_lm_en_bpe30
ngpu: 0
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 1
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 256
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/lm_stats_en_bpe30/train/text_shape.bpe
valid_shape_file:
- exp/lm_stats_en_bpe30/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/lm_train.txt
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- T
- E
- O
- R
- Y
- A
- H
- U
- S
- I
- F
- B
- L
- P
- D
- G
- M
- C
- V
- X
- J
- K
- Z
- W
- N
- Q
- <sos/eos>
init: null
model_conf:
ignore_id: 0
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram30/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
lm: seq_rnn
lm_conf:
unit: 650
nlayers: 2
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
|
soikit/distilgpt2-finetuned-wikitext2 | soikit | 2021-10-19T13:23:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7608 | 1.0 | 2334 | 3.6655 |
| 3.6335 | 2.0 | 4668 | 3.6455 |
| 3.6066 | 3.0 | 7002 | 3.6424 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
doc2query/all-with_prefix-t5-base-v1 | doc2query | 2021-10-19T12:52:47Z | 1,990 | 10 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/reddit-title-body",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- sentence-transformers/reddit-title-body
- sentence-transformers/embedding-training-data
widget:
- text: "text2reddit: Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
license: apache-2.0
---
# doc2query/all-with_prefix-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/all-with_prefix-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
prefix = "answer2question"
text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
text = prefix+": "+text
input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt')
outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
num_return_sequences=5)
print("Text:")
print(text)
print("\nGenerated Queries:")
for i in range(len(outputs)):
query = tokenizer.decode(outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
```
**Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
## Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 575k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a large collection of datasets. For the exact datasets names and weights see the `data_config.json` in this repository. Most of the datasets are available at [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers).
The datasets include besides others:
- (title, body) pairs from [Reddit](https://huggingface.co/datasets/sentence-transformers/reddit-title-body)
- (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers!
- (title, review) pairs from Amazon reviews
- (query, paragraph) pairs from MS MARCO, NQ, and GooAQ
- (question, duplicate_question) from Quora and WikiAnswers
- (title, abstract) pairs from S2ORC
## Prefix
This model was trained **with a prefix**: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different.
E.g. the above text about Python produces the following output:
| Prefix | Output |
| --- | --- |
| answer2question | Why should I use python in my business? ; What is the difference between Python and.NET? ; what is the python design philosophy? |
| review2title | Python a powerful and useful language ; A new and improved programming language ; Object-oriented, practical and accessibl |
| abstract2title | Python: A Software Development Platform ; A Research Guide for Python X: Conceptual Approach to Programming ; Python : Language and Approach |
| text2query | is python a low level language? ; what is the primary idea of python? ; is python a programming language? |
These are all available pre-fixes:
- text2reddit
- question2title
- answer2question
- abstract2title
- review2title
- news2title
- text2query
- question2question
For the datasets and weights for the different pre-fixes see `data_config.json` in this repository.
|
maximedb/autonlp-vaccinchat-22134694 | maximedb | 2021-10-19T12:50:01Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"autonlp",
"nl",
"dataset:maximedb/autonlp-data-vaccinchat",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: nl
widget:
- text: "I love AutoNLP 🤗"
datasets:
- maximedb/autonlp-data-vaccinchat
co2_eq_emissions: 14.525955245648218
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22134694
- CO2 Emissions (in grams): 14.525955245648218
## Validation Metrics
- Loss: 1.7039562463760376
- Accuracy: 0.6369376479873717
- Macro F1: 0.5363181342408181
- Micro F1: 0.6369376479873717
- Weighted F1: 0.6309793486221543
- Macro Precision: 0.5533353910494714
- Micro Precision: 0.6369376479873717
- Weighted Precision: 0.676981050732216
- Macro Recall: 0.5828723356986293
- Micro Recall: 0.6369376479873717
- Weighted Recall: 0.6369376479873717
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/maximedb/autonlp-vaccinchat-22134694
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("maximedb/autonlp-vaccinchat-22134694", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("maximedb/autonlp-vaccinchat-22134694", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
Emanuel/autonlp-pos-tag-bosque | Emanuel | 2021-10-19T12:09:29Z | 19 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"pt",
"dataset:Emanuel/autonlp-data-pos-tag-bosque",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
tags: autonlp
language: pt
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Emanuel/autonlp-data-pos-tag-bosque
co2_eq_emissions: 6.2107269129101805
---
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805
## Validation Metrics
- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Emanuel/autonlp-pos-tag-bosque-21124427
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("Emanuel/autonlp-pos-tag-bosque")
tokenizer = AutoTokenizer.from_pretrained("Emanuel/autonlp-pos-tag-bosque")
inputs = tokenizer("A noiva casa de branco", return_tensors="pt")
outputs = model(**inputs)
labelids = outputs.logits.squeeze().argmax(axis=-1)
labels = [model.config.id2label[int(x)] for x in labelids]
labels = labels[1:-1]# Filter start and end of sentence symbols
``` |
yazdipour/text-to-sparql-t5-small | yazdipour | 2021-10-19T11:17:46Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-small-2021-10-19_10-17_lastDS
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.3129461705684662
---
<!-- 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. -->
# text-to-sparql-t5-small-2021-10-19_10-17_lastDS
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2335
- Gen Len: 19.0
- P: 0.5580
- R: 0.0884
- F1: 0.3129
- Score: 5.9585
- Bleu-precisions: [90.11303396628615, 80.34125695971072, 73.81487011728768, 69.48796722990271]
- Bleu-bp: 0.0763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:----------------------------------------------------------------------------:|:-------:|
| 0.3166 | 1.0 | 4807 | 0.2335 | 19.0 | 0.5580 | 0.0884 | 0.3129 | 5.9585 | [90.11303396628615, 80.34125695971072, 73.81487011728768, 69.48796722990271] | 0.0763 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
DeepESP/gpt2-spanish | DeepESP | 2021-10-19T08:52:48Z | 5,155 | 36 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"es",
"dataset:ebooks",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
language: es
tags:
- GPT-2
- Spanish
- ebooks
- nlg
datasets:
- ebooks
widget:
- text: "Quisiera saber que va a suceder"
license: mit
---
# GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the small version of the original OpenAI GPT2 model.
## Corpus
This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).
## Tokenizer
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.
This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.
Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training.
## Training
The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.
## Authors
The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).
Thanks to the members of the community who collaborated with funding for the initial tests.
## Cautions
The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.
|
yazdipour/sparql-qald9-t5-small-2021-10-19_00-01 | yazdipour | 2021-10-19T00:13:21Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-small-2021-10-19_00-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sparql-qald9-t5-small-2021-10-19_00-01
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-2021-10-18_23-00](https://huggingface.co/yazdipour/text-to-sparql-t5-small-2021-10-18_23-00) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:-------------------------------------------------------------------------------:|:-------:|
| No log | 1.0 | 51 | 2.4058 | 19.0 | 0.3946 | 0.0660 | 0.2253 | 9.8438 | [72.36042012161415, 47.920433996383366, 33.929754804506295, 26.416482707873435] | 0.2344 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
mmcquade11/autonlp-imdb-test-21134442 | mmcquade11 | 2021-10-18T20:16:41Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-imdb-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 298.7849611952843
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134442
- CO2 Emissions (in grams): 298.7849611952843
## Validation Metrics
- Loss: 0.21618066728115082
- Accuracy: 0.9393
- Precision: 0.9360730593607306
- Recall: 0.943
- AUC: 0.98362804
- F1: 0.9395237620803029
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/mmcquade11/autonlp-imdb-test-21134442
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("mmcquade11/autonlp-imdb-test-21134442", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("mmcquade11/autonlp-imdb-test-21134442", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
gagan3012/pickuplines | gagan3012 | 2021-10-18T19:53:36Z | 7 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: pickuplines
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. -->
# pickuplines
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7873
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
yazdipour/text-to-sparql-t5-base-2021-10-18_16-15 | yazdipour | 2021-10-18T18:58:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: text-to-sparql-t5-base-2021-10-18_16-15
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- 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. -->
# text-to-sparql-t5-base-2021-10-18_16-15
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1294
- Gen Len: 19.0
- Bertscorer-p: 0.5827
- Bertscorer-r: 0.0812
- Bertscorer-f1: 0.3202
- Sacrebleu-score: 5.9410
- Sacrebleu-precisions: [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601]
- Bleu-bp: 0.0721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:|
| nan | 1.0 | 4772 | 0.1294 | 19.0 | 0.5827 | 0.0812 | 0.3202 | 5.9410 | [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601] | 0.0721 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
hiiamsid/autonlp-Summarization-20684327 | hiiamsid | 2021-10-18T18:30:54Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: es
widget:
- text: "I love AutoNLP 🤗"
datasets:
- hiiamsid/autonlp-data-Summarization
co2_eq_emissions: 437.2441955971972
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/hiiamsid/autonlp-Summarization-20684327
``` |
gagan3012/model | gagan3012 | 2021-10-18T18:23:58Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6250
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
astarostap/autonlp-antisemitism-2-21194454 | astarostap | 2021-10-18T18:06:19Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:astarostap/autonlp-data-antisemitism-2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "the jews have a lot of power"
datasets:
- astarostap/autonlp-data-antisemitism-2
co2_eq_emissions: 2.0686690092905224
---
# Description
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
Training data:
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
Note:
The goal for this model is not to be used as a final say on what is or is not antisemitic, but rather as a first pass on what might be antisemitic and should be reviewed by human experts.
Please keep in mind that I'm not an expert on antisemitism or hatespeech.
Whether something is antisemitic or not depends on the context, as for any hate speech, and everyone has a different definition for what is hate speech.
If you would like to collaborate on antisemitism detection, please feel free to contact me at [email protected]
This model is not ready for production, it needs more evaluation and more training data.
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21194454
- CO2 Emissions (in grams): 2.0686690092905224
- Dataset: https://huggingface.co/datasets/astarostap/autonlp-data-antisemitism-2
## Validation Metrics
- Loss: 0.5291365385055542
- Accuracy: 0.7572692793931732
- Precision: 0.7126948775055679
- Recall: 0.835509138381201
- AUC: 0.8185826549941126
- F1: 0.7692307692307693
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/astarostap/autonlp-antisemitism-2-21194454
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("astarostap/autonlp-antisemitism-2-21194454", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("astarostap/autonlp-antisemitism-2-21194454", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
mmcquade11/autonlp-imdb-test-21134453 | mmcquade11 | 2021-10-18T17:47:59Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-imdb-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 38.102565360610484
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Loss: 0.172550767660141
- Accuracy: 0.9355
- Precision: 0.9362853135644159
- Recall: 0.9346
- AUC: 0.98267064
- F1: 0.9354418977079372
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/mmcquade11/autonlp-imdb-test-21134453
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("mmcquade11/autonlp-imdb-test-21134453", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("mmcquade11/autonlp-imdb-test-21134453", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
huggingtweets/muratpak | huggingtweets | 2021-10-18T17:22:31Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/muratpak/1634577747584/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/1442159742558765064/RFB5JjIk_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">Pak</div>
<div style="text-align: center; font-size: 14px;">@muratpak</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 Pak.
| Data | Pak |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 686 |
| Short tweets | 964 |
| Tweets kept | 1600 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1s58abff/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 @muratpak's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30zzcgkm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30zzcgkm/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/muratpak')
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)
|
JonatanGk/roberta-base-ca-finetuned-hate-speech-offensive-catalan | JonatanGk | 2021-10-18T17:10:50Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-ca-finetuned-mnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ca-finetuned-mnli
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4137
- Accuracy: 0.8778
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3699 | 1.0 | 1255 | 0.3712 | 0.8669 |
| 0.3082 | 2.0 | 2510 | 0.3401 | 0.8766 |
| 0.2375 | 3.0 | 3765 | 0.4137 | 0.8778 |
| 0.1889 | 4.0 | 5020 | 0.4671 | 0.8733 |
| 0.1486 | 5.0 | 6275 | 0.5205 | 0.8749 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
cambridgeltl/trans-encoder-bi-simcse-roberta-base | cambridgeltl | 2021-10-18T13:29:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-base
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-roberta-base](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
cambridgeltl/trans-encoder-bi-simcse-roberta-large | cambridgeltl | 2021-10-18T13:29:43Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-roberta-large](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-large) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
lewtun/results | lewtun | 2021-10-18T13:16:42Z | 4 | 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 | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9251012149383893
---
<!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2147
- Accuracy: 0.925
- F1: 0.9251
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8221 | 1.0 | 250 | 0.3106 | 0.9125 | 0.9102 |
| 0.2537 | 2.0 | 500 | 0.2147 | 0.925 | 0.9251 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3
|
AyushPJ/test-squad-trained-finetuned-squad | AyushPJ | 2021-10-18T11:01:55Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: test-squad-trained-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-squad-trained-finetuned-squad
This model was trained from scratch on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.13.3
- Tokenizers 0.10.3
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy | CAMeL-Lab | 2021-10-18T10:18:01Z | 134 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'عامل ايه ؟'
---
# CAMeLBERT-CA POS-EGY Model
## Model description
**CAMeLBERT-CA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-CA POS-EGY model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy')
>>> text = 'عامل ايه ؟'
>>> pos(text)
[{'entity': 'adj', 'score': 0.9990943, 'index': 1, 'word': 'عامل', 'start': 0, 'end': 4}, {'entity': 'pron_interrog', 'score': 0.99863535, 'index': 2, 'word': 'ايه', 'start': 5, 'end': 8}, {'entity': 'punc', 'score': 0.99990875, 'index': 3, 'word': '؟', 'start': 9, 'end': 10}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
ahmetbagci/bert2bert-turkish-paraphrase-generation | ahmetbagci | 2021-10-18T10:17:40Z | 67 | 10 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"paraphrasing",
"seq2seq",
"bert",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- tr
tags:
- paraphrasing
- encoder-decoder
- seq2seq
- bert
---
#Bert2Bert Turkish Paraphrase Generation
#INISTA 2021
#Comparison of Turkish Paraphrase Generation Models
#Dataset
The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset.
Dataset [Link](https://drive.google.com/file/d/1-2l9EwIzXZ7fUkNW1vdeF3lzQp2pygp_/view?usp=sharing)
#How To Use
```python
from transformers import BertTokenizerFast,EncoderDecoderModel
tokenizer=BertTokenizerFast.from_pretrained("dbmdz/bert-base-turkish-cased")
model = EncoderDecoderModel.from_pretrained("ahmetbagci/bert2bert-turkish-paraphrase-generation")
text="son model arabalar çevreye daha mı az zarar veriyor?"
input_ids = tokenizer(text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
#sample output
#son model arabalar çevre için daha az zararlı mı?
```
#Cite
```bibtex
@INPROCEEDINGS{9548335,
author={Bağcı, Ahmet and Amasyali, Mehmet Fatih},
booktitle={2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)},
title={Comparison of Turkish Paraphrase Generation Models},
year={2021},
volume={},
number={},
pages={1-6},
doi={10.1109/INISTA52262.2021.9548335}
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | CAMeL-Lab | 2021-10-18T10:15:57Z | 12 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'عامل ايه ؟'
---
# CAMeLBERT-Mix POS-EGY Model
## Model description
**CAMeLBERT-Mix POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix POS-EGY model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy')
>>> text = 'عامل ايه ؟'
>>> pos(text)
[{'entity': 'adj', 'score': 0.9972628, 'index': 1, 'word': 'عامل', 'start': 0, 'end': 4}, {'entity': 'pron_interrog', 'score': 0.9525163, 'index': 2, 'word': 'ايه', 'start': 5, 'end': 8}, {'entity': 'punc', 'score': 0.99869114, 'index': 3, 'word': '؟', 'start': 9, 'end': 10}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy | CAMeL-Lab | 2021-10-18T10:15:37Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'عامل ايه ؟'
---
# CAMeLBERT-DA POS-EGY Model
## Model description
**CAMeLBERT-DA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-DA POS-EGY model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy')
>>> text = 'عامل ايه ؟'
>>> pos(text)
[{'entity': 'adj', 'score': 0.99843216, 'index': 1, 'word': 'عامل', 'start': 0, 'end': 4}, {'entity': 'pron_interrog', 'score': 0.9990083, 'index': 2, 'word': 'ايه', 'start': 5, 'end': 8}, {'entity': 'punc', 'score': 0.82973784, 'index': 3, 'word': '؟', 'start': 9, 'end': 10}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | CAMeL-Lab | 2021-10-18T09:58:40Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'شلونك ؟ شخبارك ؟'
---
# CAMeLBERT-DA POS-GLF Model
## Model description
**CAMeLBERT-DA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-DA POS-GLF model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf')
>>> text = 'شلونك ؟ شخبارك ؟'
>>> pos(text)
[{'entity': 'noun', 'score': 0.84596395, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'prep', 'score': 0.7230489, 'index': 2, 'word': '##ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.99996364, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9990874, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.99985224, 'index': 5, 'word': '##خبار', 'start': 9, 'end': 13}, {'entity': 'noun', 'score': 0.9988868, 'index': 6, 'word': '##ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999683, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf | CAMeL-Lab | 2021-10-18T09:57:26Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'شلونك ؟ شخبارك ؟'
---
# CAMeLBERT-MSA POS-GLF Model
## Model description
**CAMeLBERT-MSA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-MSA POS-GLF model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf')
>>> text = 'شلونك ؟ شخبارك ؟'
>>> pos(text)
[{'entity': 'adv_interrog', 'score': 0.5622676, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'prep', 'score': 0.99969727, 'index': 2, 'word': '##ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999299, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9843815, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.9998467, 'index': 5, 'word': '##خبار', 'start': 9, 'end': 13}, {'entity': 'prep', 'score': 0.9993611, 'index': 6, 'word': '##ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.99993765, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | CAMeL-Lab | 2021-10-18T09:44:57Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
---
# CAMeLBERT-CA POS-MSA Model
## Model description
**CAMeLBERT-CA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-CA POS-MSA model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999758, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.9997559, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.99996257, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.9958452, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999635, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.99991685, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99997497, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.9999795, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99924207, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.99994195, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.9997414, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | CAMeL-Lab | 2021-10-18T09:44:42Z | 1,178 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
---
# CAMeLBERT-Mix POS-MSA Model
## Model description
**CAMeLBERT-Mix POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix POS-MSA model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999592, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.9997877, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998405, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.9697179, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.99967164, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.99980617, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99997973, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99995637, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.9983974, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999469, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.9993273, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa | CAMeL-Lab | 2021-10-18T09:44:25Z | 12 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
---
# CAMeLBERT-DA POS-MSA Model
## Model description
**CAMeLBERT-DA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-DA POS-MSA model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999913, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.9992475, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.999919, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.99993193, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.99999106, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.99998987, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.9999933, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.9999899, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99990565, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.99997944, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.99938935, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa | CAMeL-Lab | 2021-10-18T09:34:42Z | 22 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
---
# CAMeLBERT-MSA POS-MSA Model
## Model description
**CAMeLBERT-MSA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-MSA POS-MSA model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999764, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.99991846, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998356, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.99368894, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999426, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.9999339, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99996775, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99996895, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99990183, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999347, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.99931145, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
rifkat/uztext_568Mb_Roberta_BPE | rifkat | 2021-10-18T05:32:18Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | <p><b>UzRoBerta model.</b>
Pre-prepared model in Uzbek (Cyrillic script) to model the masked language and predict the next sentences.
<p><b>Training data.</b>
UzBERT model was pretrained on ≈167K news articles (≈568Mb).
|
airKlizz/t5-base-with-title-multi-fr-wiki-news | airKlizz | 2021-10-17T20:20:45Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: fr
license: mit
---
|
airKlizz/bart-large-multi-fr-wiki-news | airKlizz | 2021-10-17T20:10:41Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: fr
license: mit
---
|
airKlizz/t5-base-multi-fr-wiki-news | airKlizz | 2021-10-17T20:09:42Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: fr
license: mit
---
|
yazdipour/text-to-sparql-t5-small-2021-10-17_18-47 | yazdipour | 2021-10-17T19:48:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-small-2021-10-17_18-47
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.2345714420080185
---
<!-- 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. -->
# text-to-sparql-t5-small-2021-10-17_18-47
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5258
- Gen Len: 19.0
- P: 0.4582
- R: 0.0278
- F1: 0.2346
- Score: 3.5848
- Bleu-precisions: [82.57739877107295, 62.13358857503344, 48.43062944877681, 41.90172321318059]
- Bleu-bp: 0.0631
## 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 | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:----------------------------------------------------------------------------:|:-------:|
| 0.7575 | 1.0 | 4807 | 0.5258 | 19.0 | 0.4582 | 0.0278 | 0.2346 | 3.5848 | [82.57739877107295, 62.13358857503344, 48.43062944877681, 41.90172321318059] | 0.0631 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
huggingartists/bushido-zho | huggingartists | 2021-10-17T16:58:48Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/bushido-zho",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/bushido-zho
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/6e5b165de8561df37790229c26b25692.959x959x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BUSHIDO ZHO</div>
<a href="https://genius.com/artists/bushido-zho">
<div style="text-align: center; font-size: 14px;">@bushido-zho</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from BUSHIDO ZHO.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/bushido-zho).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/bushido-zho")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/vtfjc0qi/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 BUSHIDO ZHO's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/iwclgqsj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/iwclgqsj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/bushido-zho')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/bushido-zho")
model = AutoModelWithLMHead.from_pretrained("huggingartists/bushido-zho")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | CAMeL-Lab | 2021-10-17T13:35:38Z | 1,090 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-MSA DID MADAR Twitter-5 Model
## Model description
**CAMeLBERT-MSA DID MADAR Twitter-5 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [MADAR Twitter-5](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/) dataset, which includes 21 labels.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-MSA DID MADAR Twitter-5 model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.5741344094276428},
{'label': 'Kuwait', 'score': 0.5225679278373718}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
biu-nlp/cdlm | biu-nlp | 2021-10-17T12:24:59Z | 45 | 1 | transformers | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"cdlm",
"en",
"arxiv:2101.00406",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: en
tags:
- longformer
- cdlm
license: apache-2.0
inference: false
---
# Cross-Document Language Modeling
CDLM: Cross-Document Language Modeling.
Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan and Ido Dagan. In EMNLP Findings, 2021. [PDF](https://arxiv.org/pdf/2101.00406.pdf)
Please note that during our pretraining we used the document and sentence separators, which you might want to add to your data. The document and sentence separators are `<doc-s>`, `</doc-s>` (the last two tokens in the vocabulary), and `<s>`, `</s>`, respectively.
```python
from transformers import AutoTokenizer, AutoModel
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('biu-nlp/cdlm')
model = AutoModel.from_pretrained('biu-nlp/cdlm')
```
The original repo is [here](https://github.com/aviclu/CDLM).
If you find our work useful, please cite the paper as:
```python
@article{caciularu2021cross,
title={Cross-Document Language Modeling},
author={Caciularu, Avi and Cohan, Arman and Beltagy, Iz and Peters, Matthew E and Cattan, Arie and Dagan, Ido},
journal={Findings of the Association for Computational Linguistics: EMNLP 2021},
year={2021}
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | CAMeL-Lab | 2021-10-17T12:10:17Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-Mix Poetry Classification Model
## Model description
**CAMeLBERT-Mix Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [APCD](https://arxiv.org/pdf/1905.05700.pdf) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix Poetry Classification model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry')
>>> # A list of verses where each verse consists of two parts.
>>> verses = [
['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'],
['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا']
]
>>> # A function that concatenates the halves of each verse by using the [SEP] token.
>>> join_verse = lambda half: ' [SEP] '.join(half)
>>> # Apply this to all the verses in the list.
>>> verses = [join_verse(verse) for verse in verses]
>>> poetry(sentences)
[{'label': 'البسيط', 'score': 0.9937475919723511},
{'label': 'الكامل', 'score': 0.971284031867981}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | CAMeL-Lab | 2021-10-17T12:09:56Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-DA Poetry Classification Model
## Model description
**CAMeLBERT-DA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [APCD](https://arxiv.org/pdf/1905.05700.pdf) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-DA Poetry Classification model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-poetry')
>>> # A list of verses where each verse consists of two parts.
>>> verses = [
['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'],
['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا']
]
>>> # A function that concatenates the halves of each verse by using the [SEP] token.
>>> join_verse = lambda half: ' [SEP] '.join(half)
>>> # Apply this to all the verses in the list.
>>> verses = [join_verse(verse) for verse in verses]
>>> poetry(sentences)
[{'label': 'البسيط', 'score': 0.9874765276908875},
{'label': 'السلسلة', 'score': 0.6877778172492981}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | CAMeL-Lab | 2021-10-17T12:09:38Z | 13 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-CA Poetry Classification Model
## Model description
**CAMeLBERT-CA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [APCD](https://arxiv.org/pdf/1905.05700.pdf) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-CA Poetry Classification model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry')
>>> # A list of verses where each verse consists of two parts.
>>> verses = [
['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'],
['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا']
]
>>> # A function that concatenates the halves of each verse by using the [SEP] token.
>>> join_verse = lambda half: ' [SEP] '.join(half)
>>> # Apply this to all the verses in the list.
>>> verses = [join_verse(verse) for verse in verses]
>>> poetry(sentences)
[{'label': 'البسيط', 'score': 0.9845284819602966},
{'label': 'الكامل', 'score': 0.752918004989624}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
castorini/monot5-base-msmarco-10k | castorini | 2021-10-17T11:24:22Z | 3,178 | 14 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than `monot5-base-msmarco`, i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
- [Rerank MS MARCO passages](https://github.com/castorini/pygaggle/blob/master/docs/experiments-msmarco-passage-subset.md)
- [Rerank Robust04 documents](https://github.com/castorini/pygaggle/blob/master/docs/experiments-robust04-monot5-gpu.md)
Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.63/) |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | CAMeL-Lab | 2021-10-17T11:17:53Z | 30 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-Mix DID MADAR Corpus6 Model
## Model description
**CAMeLBERT-Mix DID MADAR Corpus6 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [MADAR Corpus 6](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/) dataset, which includes 6 labels.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix DID MADAR Corpus6 model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar6')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'CAI', 'score': 0.9996405839920044},
{'label': 'DOH', 'score': 0.9997853636741638}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment | CAMeL-Lab | 2021-10-17T11:15:12Z | 35 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT-CA SA Model
## Model description
**CAMeLBERT-CA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf), and [SemEval](https://aclanthology.org/S17-2088.pdf) datasets.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."
* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-CA SA model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component (*recommended*) or as part of the transformers pipeline.
#### How to use
To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component:
```python
>>> from camel_tools.sentiment import SentimentAnalyzer
>>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment")
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa.predict(sentences)
>>> ['positive', 'negative']
```
You can also use the SA model directly with a transformers pipeline:
```python
>>> from transformers import pipeline
e
>>> sa = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment')
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa(sentences)
[{'label': 'positive', 'score': 0.9616648554801941},
{'label': 'negative', 'score': 0.9779177904129028}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | CAMeL-Lab | 2021-10-17T11:14:08Z | 30 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
---
# CAMeLBERT-CA NER Model
## Model description
**CAMeLBERT-CA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."
* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-CA NER model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component (*recommended*) or as part of the transformers pipeline.
#### How to use
To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component:
```python
>>> from camel_tools.ner import NERecognizer
>>> from camel_tools.tokenizers.word import simple_word_tokenize
>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-ca-ner')
>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
>>> ner.predict_sentence(sentence)
>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
```
You can also use the NER model directly with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-ner')
>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
[{'word': 'أبوظبي',
'score': 0.9895730018615723,
'entity': 'B-LOC',
'index': 2,
'start': 6,
'end': 12},
{'word': 'الإمارات',
'score': 0.8156259655952454,
'entity': 'B-LOC',
'index': 8,
'start': 33,
'end': 41},
{'word': 'العربية',
'score': 0.890906810760498,
'entity': 'I-LOC',
'index': 9,
'start': 42,
'end': 49},
{'word': 'المتحدة',
'score': 0.8169114589691162,
'entity': 'I-LOC',
'index': 10,
'start': 50,
'end': 57}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a da of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-ner | CAMeL-Lab | 2021-10-17T11:13:27Z | 49 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
---
# CAMeLBERT-DA NER Model
## Model description
**CAMeLBERT-DA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."
* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-DA NER model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component (*recommended*) or as part of the transformers pipeline.
#### How to use
To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component:
```python
>>> from camel_tools.ner import NERecognizer
>>> from camel_tools.tokenizers.word import simple_word_tokenize
>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
>>> ner.predict_sentence(sentence)
>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
```
You can also use the NER model directly with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
[{'word': 'أبوظبي',
'score': 0.9895730018615723,
'entity': 'B-LOC',
'index': 2,
'start': 6,
'end': 12},
{'word': 'الإمارات',
'score': 0.8156259655952454,
'entity': 'B-LOC',
'index': 8,
'start': 33,
'end': 41},
{'word': 'العربية',
'score': 0.890906810760498,
'entity': 'I-LOC',
'index': 9,
'start': 42,
'end': 49},
{'word': 'المتحدة',
'score': 0.8169114589691162,
'entity': 'I-LOC',
'index': 10,
'start': 50,
'end': 57}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a da of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | CAMeL-Lab | 2021-10-17T11:13:00Z | 107,110 | 12 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
---
# CAMeLBERT-Mix NER Model
## Model description
**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678).
"* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix NER model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component (*recommended*) or as part of the transformers pipeline.
#### How to use
To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component:
```python
>>> from camel_tools.ner import NERecognizer
>>> from camel_tools.tokenizers.word import simple_word_tokenize
>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-mix-ner')
>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
>>> ner.predict_sentence(sentence)
>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
```
You can also use the NER model directly with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-ner')
>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
[{'word': 'أبوظبي',
'score': 0.9895730018615723,
'entity': 'B-LOC',
'index': 2,
'start': 6,
'end': 12},
{'word': 'الإمارات',
'score': 0.8156259655952454,
'entity': 'B-LOC',
'index': 8,
'start': 33,
'end': 41},
{'word': 'العربية',
'score': 0.890906810760498,
'entity': 'I-LOC',
'index': 9,
'start': 42,
'end': 49},
{'word': 'المتحدة',
'score': 0.8169114589691162,
'entity': 'I-LOC',
'index': 10,
'start': 50,
'end': 57}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | CAMeL-Lab | 2021-10-17T11:05:21Z | 33 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-MSA DID NADI Model
## Model description
**CAMeLBERT-MSA DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [NADI Coountry-level](https://sites.google.com/view/nadi-shared-task) dataset, which includes 21 labels.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-MSA DID NADI model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.9242768287658691},
{'label': 'Saudi_Arabia', 'score': 0.3400847613811493}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` |
lucius/distilroberta-base-finetuned-wikitext2 | lucius | 2021-10-17T10:40:14Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0827 | 1.0 | 2406 | 1.9227 |
| 1.9993 | 2.0 | 4812 | 1.8828 |
| 1.9614 | 3.0 | 7218 | 1.8172 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
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