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plbr/my_model | c55378e42af34fb39b82969d0ef7a248e10e9c42 | 2022-04-14T05:22:56.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | false | plbr | null | plbr/my_model | 3 | null | transformers | 22,200 | Entry not found |
obokkkk/koelectra-base-v3-discriminator-finetuned-klue-v4 | 1d2d62641ec7998be9ca27778daa39b55c17efdc | 2022-04-14T04:32:20.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | obokkkk | null | obokkkk/koelectra-base-v3-discriminator-finetuned-klue-v4 | 3 | null | transformers | 22,201 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: koelectra-base-v3-discriminator-finetuned-klue-v4
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. -->
# koelectra-base-v3-discriminator-finetuned-klue-v4
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.4979 | 0.33 | 500 | 4.0470 |
| 3.2001 | 0.65 | 1000 | 2.3172 |
| 2.215 | 0.98 | 1500 | 1.9043 |
| 1.7849 | 1.31 | 2000 | 1.7181 |
| 1.6156 | 1.63 | 2500 | 1.5955 |
| 1.5295 | 1.96 | 3000 | 1.5071 |
| 1.2147 | 2.29 | 3500 | 1.5872 |
| 1.1727 | 2.61 | 4000 | 1.5104 |
| 1.1467 | 2.94 | 4500 | 1.6059 |
| 0.9972 | 3.27 | 5000 | 1.6523 |
| 0.9791 | 3.59 | 5500 | 1.6219 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
|
huggingtweets/credenzaclear2-dril-nia_mp4 | 75193fcddcc4e0fa8a1049ccd016fdfeb536edca | 2022-04-14T04:40:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/credenzaclear2-dril-nia_mp4 | 3 | null | transformers | 22,202 | ---
language: en
thumbnail: http://www.huggingtweets.com/credenzaclear2-dril-nia_mp4/1649911222622/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/1510917391533830145/XW-zSFDJ_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/1487740104340918272/7c9spp2E_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/1511875789213638656/WdSSvAhj_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 & Nia & Audrey Horne</div>
<div style="text-align: center; font-size: 14px;">@credenzaclear2-dril-nia_mp4</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 & Nia & Audrey Horne.
| Data | wint | Nia | Audrey Horne |
| --- | --- | --- | --- |
| Tweets downloaded | 3229 | 1552 | 626 |
| Retweets | 477 | 28 | 74 |
| Short tweets | 303 | 133 | 124 |
| Tweets kept | 2449 | 1391 | 428 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rarj99g/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 @credenzaclear2-dril-nia_mp4's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20c2vigo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20c2vigo/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/credenzaclear2-dril-nia_mp4')
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)
|
andidu/aaabbbcccddd | 56abf6d48254723c7958bfd8c80c7f00e7164895 | 2022-05-20T17:56:34.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | andidu | null | andidu/aaabbbcccddd | 3 | null | transformers | 22,203 | Entry not found |
ndavid/autotrain-trec-fine-bert-739422530 | 3956a431b56005206095aadad0cba790a9bee183 | 2022-04-14T09:39:42.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:ndavid/autotrain-data-trec-fine-bert",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | ndavid | null | ndavid/autotrain-trec-fine-bert-739422530 | 3 | null | transformers | 22,204 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ndavid/autotrain-data-trec-fine-bert
co2_eq_emissions: 0.02238820299105448
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 739422530
- CO2 Emissions (in grams): 0.02238820299105448
## Validation Metrics
- Loss: 0.36623290181159973
- Accuracy: 0.9321753515301903
- Macro F1: 0.9066706944656866
- Micro F1: 0.9321753515301903
- Weighted F1: 0.9314858667247282
- Macro Precision: 0.9489233194839841
- Micro Precision: 0.9321753515301903
- Weighted Precision: 0.9347346558570125
- Macro Recall: 0.8842587178845419
- Micro Recall: 0.9321753515301903
- Weighted Recall: 0.9321753515301903
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ndavid/autotrain-trec-fine-bert-739422530
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Ning-fish/xlm-roberta-base-finetuned-panx-de | 964bf3bd3c72ef0535ec77cfd5857b6cfe9d9782 | 2022-04-14T15:17:38.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Ning-fish | null | Ning-fish/xlm-roberta-base-finetuned-panx-de | 3 | null | transformers | 22,205 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8591260810195721
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- F1: 0.8591
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.257 | 1.0 | 525 | 0.1512 | 0.8302 |
| 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 |
| 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022 | 3878f663db355e8c6ea329b79f134bbf9e52e4df | 2022-04-14T17:16:01.000Z | [
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | nntadotzip | null | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022 | 3 | null | transformers | 22,206 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022
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. -->
# xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 357 | 0.6451 |
| 0.8416 | 2.0 | 714 | 0.4428 |
| 0.5227 | 3.0 | 1071 | 0.4240 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
hxz116/distilbert-base-uncased-finetuned-cola | e582dd58de7486c6e21e35edb624cd1057d50a16 | 2022-04-14T19:55:05.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | hxz116 | null | hxz116/distilbert-base-uncased-finetuned-cola | 3 | null | transformers | 22,207 | Entry not found |
SophieTr/PPO-policy_v2 | 24cb8b30547567b8eb41da421b049229578bf3be | 2022-04-19T01:42:45.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SophieTr | null | SophieTr/PPO-policy_v2 | 3 | null | transformers | 22,208 | Entry not found |
NeuralNotwork/blenderbot-400M-baseline | 323e89ee1123c3a59c4743c29bee5b0bf45aa711 | 2022-04-15T05:45:35.000Z | [
"pytorch",
"blenderbot",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | NeuralNotwork | null | NeuralNotwork/blenderbot-400M-baseline | 3 | null | transformers | 22,209 | Entry not found |
NeuralNotwork/blenderbot-400M-ul-ts | 5ce885ceb53bd545d1899b5b78ff4594f4ee4dac | 2022-04-15T09:07:50.000Z | [
"pytorch",
"blenderbot",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | NeuralNotwork | null | NeuralNotwork/blenderbot-400M-ul-ts | 3 | null | transformers | 22,210 | Entry not found |
SophieTr/PPO-policy_v3 | 6a64ac236281c8f1e86f93d527196828ae3b6431 | 2022-04-22T14:28:39.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SophieTr | null | SophieTr/PPO-policy_v3 | 3 | null | transformers | 22,211 | Entry not found |
birgermoell/psst-fairseq-larger-rir | 0a470fc31c1ab22a6c3314aa72b8d38b61e593e9 | 2022-04-15T13:59:09.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/psst-fairseq-larger-rir | 3 | null | transformers | 22,212 | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
---
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`)
The model was finetuned on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec), and the results on the validation set were **PER:** 21\.0%, **FER:** 9\.2%.
|
birgermoell/psst-fairseq-pitch-shift-timit | ddbb2fd8c364520ab82eec898b8f71c170f65b57 | 2022-04-15T13:38:14.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/psst-fairseq-pitch-shift-timit | 3 | null | transformers | 22,213 | Entry not found |
aseifert/comma-xlm-roberta-large | a1d90c0277c63314d8fdd6fed39b6aef38ef05a6 | 2022-04-16T08:49:09.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | aseifert | null | aseifert/comma-xlm-roberta-large | 3 | null | transformers | 22,214 | Entry not found |
dennishe97/longformer-code-relatedness-model | 886e804d91ddeda293a198f1b43f6960fae77f0f | 2022-04-16T05:34:04.000Z | [
"pytorch",
"longformer",
"feature-extraction",
"transformers"
] | feature-extraction | false | dennishe97 | null | dennishe97/longformer-code-relatedness-model | 3 | null | transformers | 22,215 | Entry not found |
jason9693/KcELECTRA-base-apeach | 9b0456868e57315cd960ae0bbdbfee88cccdfc8c | 2022-04-16T14:20:19.000Z | [
"pytorch",
"electra",
"text-classification",
"ko",
"dataset:jason9693/APEACH",
"transformers"
] | text-classification | false | jason9693 | null | jason9693/KcELECTRA-base-apeach | 3 | null | transformers | 22,216 | ---
language: ko
widget:
- text: "응 어쩔티비~~"
datasets:
- jason9693/APEACH
--- |
V3RX2000/distilbert-base-uncased-finetuned-imdb-accelerate | b1be83979e7faba4a5b965259add189b5e7fc314 | 2022-04-16T06:51:29.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | V3RX2000 | null | V3RX2000/distilbert-base-uncased-finetuned-imdb-accelerate | 3 | null | transformers | 22,217 | Entry not found |
Pavithra/madgrad-best-version | d72f4bfc09eccc4774b988008e4e81d7dd7ccc1a | 2022-04-18T01:34:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Pavithra | null | Pavithra/madgrad-best-version | 3 | null | transformers | 22,218 | Entry not found |
adnankhawaja/R_T_FB_LM | f6f0065eefe573ca50d8d7c816f579c5c8de2798 | 2022-04-17T08:06:06.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | adnankhawaja | null | adnankhawaja/R_T_FB_LM | 3 | null | transformers | 22,219 | Entry not found |
rmihaylov/roberta-base-use-qa-theseus-bg | fd4d6b48338499e0462b8921bb936eabfe103be8 | 2022-04-18T10:25:59.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2004.09813",
"arxiv:2002.02925",
"transformers",
"torch",
"license:mit",
"sentence-similarity"
] | sentence-similarity | false | rmihaylov | null | rmihaylov/roberta-base-use-qa-theseus-bg | 3 | null | transformers | 22,220 | ---
inference: false
pipeline_tag: sentence-similarity
language:
- bg
license: mit
datasets:
- oscar
- chitanka
- wikipedia
tags:
- torch
---
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence.
The teacher model is the [USE model by Google](https://aclanthology.org/D18-2029/).
This model is cased: it does make a difference between bulgarian and Bulgarian.
It was trained on private Bulgarian-English parallel data.
Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925).
### How to use
Here is how to use this model in PyTorch:
```python
>>> import scipy
>>> import torch
>>> from transformers import AutoModel, AutoTokenizer
>>>
>>> model = AutoModel.from_pretrained('rmihaylov/roberta-base-use-qa-theseus-bg')
>>> tokenizer = AutoTokenizer.from_pretrained('rmihaylov/roberta-base-use-qa-theseus-bg')
>>>
>>> query = "Какви са съставките на бисквитките?"
>>>
>>> answers = [
>>> "Бисквитката е печена или варена храна, която обикновено е малка, плоска и сладка.",
>>> "Бисквитките обикновено съдържат брашно, захар и някакъв вид масло или мазнини. Те могат да включват други съставки като стафиди, овес, шоколадов чипс, ядки и др.",
>>> "В повечето англоговорящи страни, с изключение на САЩ и Канада, хрупкавите бисквитки се наричат бисквити.",
>>> "Бисквитите Chewier понякога се наричат бисквитки дори в Обединеното кралство. Някои бисквитки могат също да бъдат назовавани според формата им, като квадратчета с дата или барове.",
>>> "Бисквитките или бисквитите могат да се произвеждат масово във фабрики, направени в малки пекарни или домашно приготвени.",
>>> "Вариантите за бисквити или бисквити включват сандвич бисквити, като крем крем, Jammie Dodgers, Bourbons и Oreos, с пълнеж от ружа или конфитюр и понякога потопени в шоколад или друго сладко покритие.",
>>> "Бисквитките често се сервират с напитки като мляко, кафе или чай.",
>>> "Фабричните бисквитки се продават в магазини за хранителни стоки, магазини за удобство и автомати.",
>>> "Американската употреба произлиза от холандското koekje „малка торта“, което е умалително от „koek“ („торта“), което произлиза от средно холандската дума „koke“.",
>>> "Cookie Monster е Muppet в дългогодишното детско телевизионно шоу Sesame Street, който е най-известен с ненаситния си апетит към бисквитките и известните си фрази за ядене, като „Me want cookie!“, „Me eat cookie!“ (или просто „COOKIE!“) и „Om nom nom nom“ (казано през уста, пълна с храна).",
>>> "Домашните бисквитки обикновено се правят от тесто, оформено на малки топчета и пуснато върху лист с бисквитки. След това се пекат във фурна за 5 до 15 минути, в зависимост от рецептата. Температурата на фурната варира от 250 до 350 градуса.",
>>> "Повечето бисквитки със среден размер, ако са направени със захар, брашно и скъсяване, ще съдържат между 100 и 200 калории.",
>>> ]
>>>
>>> query_embedding = model.question(**tokenizer.encode_plus(query, return_tensors='pt')).detach().numpy()[0]
>>>
>>> corpus, corpus_embeddings = [], []
>>> for answer in answers:
>>> value_inputs = tokenizer.encode_plus(answer, answer, return_tensors='pt')
>>> embedding = model.answer(**value_inputs).detach().numpy()[0]
>>> corpus.append(answer)
>>> corpus_embeddings.append(embedding)
>>>
>>> distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0]
>>>
>>> results = zip(range(len(distances)), distances)
>>> results = sorted(results, key=lambda x: x[1])
>>>
>>> print([[corpus[idx].strip(), (1.0 - distance)] for idx, distance in results])
[['Бисквитките обикновено съдържат брашно, захар и някакъв вид масло или мазнини. Те могат да включват други съставки като стафиди, овес, шоколадов чипс, ядки и др.',
0.5449754306536151],
['Фабричните бисквитки се продават в магазини за хранителни стоки, магазини за удобство и автомати.',
0.5049509545814316],
['В повечето англоговорящи страни, с изключение на САЩ и Канада, хрупкавите бисквитки се наричат \u200b\u200bбисквити.',
0.5029661338050297],
['Бисквитките или бисквитите могат да се произвеждат масово във фабрики, направени в малки пекарни или домашно приготвени.',
0.4991678233218718],
['Вариантите за бисквити или бисквити включват сандвич бисквити, като крем крем, Jammie Dodgers, Bourbons и Oreos, с пълнеж от ружа или конфитюр и понякога потопени в шоколад или друго сладко покритие.',
0.49050297326146386],
['Повечето бисквитки със среден размер, ако са направени със захар, брашно и скъсяване, ще съдържат между 100 и 200 калории.',
0.48950875441294106],
['Бисквитката е печена или варена храна, която обикновено е малка, плоска и сладка.',
0.48646309549536737],
['Бисквитите Chewier понякога се наричат \u200b\u200bбисквитки дори в Обединеното кралство. Някои бисквитки могат също да бъдат назовавани според формата им, като квадратчета с дата или барове.',
0.4840599482604815],
['Cookie Monster е Muppet в дългогодишното детско телевизионно шоу Sesame Street, който е най-известен с ненаситния си апетит към бисквитките и известните си фрази за ядене, като „Me want cookie!“, „Me eat cookie!“ (или просто „COOKIE!“) и „Om nom nom nom“ (казано през уста, пълна с храна).',
0.45209677893728206],
['Домашните бисквитки обикновено се правят от тесто, оформено на малки топчета и пуснато върху лист с бисквитки. След това се пекат във фурна за 5 до 15 минути, в зависимост от рецептата. Температурата на фурната варира от 250 до 350 градуса.',
0.4511516464302119],
['Бисквитките често се сервират с напитки като мляко, кафе или чай.',
0.42364528401677803],
['Американската употреба произлиза от холандското koekje „малка торта“, което е умалително от „koek“ („торта“), което произлиза от средно холандската дума „koke“.',
0.3267314582662877]]
```
|
zoha/wav2vec2-base-timit-demo-colab | c740187a3550aac39028a335ebbf86f83b86b959 | 2022-04-18T16:40:09.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | zoha | null | zoha/wav2vec2-base-timit-demo-colab | 3 | null | transformers | 22,221 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
nbhimte/tiny-bert-best | 8c22e512e72c0ed78354fadaeac59be15a8b73e2 | 2022-04-18T11:46:11.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | nbhimte | null | nbhimte/tiny-bert-best | 3 | null | transformers | 22,222 | TrainOutput(global_step=2456, training_loss=0.29150783277878156, metrics={'train_runtime': 939.2154, 'train_samples_per_second': 167.246, 'train_steps_per_second': 2.615, 'total_flos': 321916620637920.0, 'train_loss': 0.29150783277878156, 'epoch': 4.0}) |
Auruncus/gpt-j-6b-8bit-FT | 79930960a42b221c458740ddcb124af9d2686f33 | 2022-04-18T20:17:14.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | Auruncus | null | Auruncus/gpt-j-6b-8bit-FT | 3 | null | transformers | 22,223 | Entry not found |
jenspt/bert_classification | aed660be8ba2f5dce313114ad674753d4007704f | 2022-04-19T10:39:10.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | jenspt | null | jenspt/bert_classification | 3 | null | transformers | 22,224 | Entry not found |
frozenwalker/SciFive_pubmedqa_question_generation_nmconcept | ab0040a0b456a761efa87439d3313ecddc1cb087 | 2022-04-19T10:54:11.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | frozenwalker | null | frozenwalker/SciFive_pubmedqa_question_generation_nmconcept | 3 | null | transformers | 22,225 | Entry not found |
tuhailong/bi_encoder_roberta-wwm-ext | 8ada21c915945bfd3060367ad751a28b1d826d55 | 2022-04-20T02:45:22.000Z | [
"pytorch",
"bert",
"feature-extraction",
"zh",
"dataset:dialogue",
"transformers",
"sbert"
] | feature-extraction | false | tuhailong | null | tuhailong/bi_encoder_roberta-wwm-ext | 3 | null | transformers | 22,226 | ---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
### Usage
```python
>>> from sentence_transformers import SentenceTransformer, util
>>> model = SentenceTransformer("tuhailong/bi_encoder_roberta-wwm-ext", device="cuda:1")
>>> model.max_seq_length=32
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> embeddings1 = model.encode([sentences[0]], convert_to_tensor=True)
>>> embeddings2 = model.encode([sentences[1]], convert_to_tensor=True)
>>> scores = util.cos_sim(embeddings1, embeddings2).cpu().numpy()
>>> print(scores)
```
#### Code
train code from https://github.com/TTurn/bi-encoder
##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-config.json", "2_Dense-config.json" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/config.json", "2_Dense/config.json" and "2_Dense/pytorch_model.bin". |
anshr/t5-small_supervised_baseline_01 | 15cc9098acb754745c5066bce957904959cb8d33 | 2022-04-19T15:15:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | anshr | null | anshr/t5-small_supervised_baseline_01 | 3 | null | transformers | 22,227 | Entry not found |
GPL/webis-touche2020-msmarco-distilbert-gpl | 3b9885ddaffcedfda10dfef9c536638dc945c244 | 2022-04-19T15:16:14.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | GPL | null | GPL/webis-touche2020-msmarco-distilbert-gpl | 3 | null | sentence-transformers | 22,228 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, '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 --> |
GPL/hotpotqa-tsdae-msmarco-distilbert-gpl | 638329fe91a9675c8b8826f863606490dec7870e | 2022-04-19T15:24:12.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | GPL | null | GPL/hotpotqa-tsdae-msmarco-distilbert-gpl | 3 | null | sentence-transformers | 22,229 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, '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 --> |
GPL/trec-news-tsdae-msmarco-distilbert-gpl | fdc9e5b5339e09bb3ccf26390eb5cd019dcf3d5b | 2022-04-19T15:26:45.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | GPL | null | GPL/trec-news-tsdae-msmarco-distilbert-gpl | 3 | null | sentence-transformers | 22,230 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, '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 --> |
GPL/fever-tsdae-msmarco-distilbert-margin-mse | ee01372c4e14d3c91273885f7d67c5a1a7eb5e3a | 2022-04-19T16:43:37.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | GPL | null | GPL/fever-tsdae-msmarco-distilbert-margin-mse | 3 | null | transformers | 22,231 | Entry not found |
GPL/hotpotqa-tsdae-msmarco-distilbert-margin-mse | 50afdbd3640b1a3a699db7f699a40ff117e6c228 | 2022-04-19T16:44:10.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | GPL | null | GPL/hotpotqa-tsdae-msmarco-distilbert-margin-mse | 3 | null | transformers | 22,232 | Entry not found |
GPL/webis-touche2020-tsdae-msmarco-distilbert-margin-mse | f7b6241f93da7f17c7caac2649635e202dc0b7de | 2022-04-19T16:46:45.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | GPL | null | GPL/webis-touche2020-tsdae-msmarco-distilbert-margin-mse | 3 | null | transformers | 22,233 | Entry not found |
celinelee/bart-finetuned-conala-3 | 3e237f46f1ce854df7670c6924fe4ec70010cb47 | 2022-04-20T15:10:58.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | celinelee | null | celinelee/bart-finetuned-conala-3 | 3 | 1 | transformers | 22,234 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: bart-finetuned-conala-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-conala-3
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an CoNaLa.
It achieves the following results on the evaluation set:
- Loss: 1.8253
- Rouge1: 47.4345
- Rouge2: 23.8936
- Rougel: 45.317
- Rougelsum: 45.4339
- Bleu: 0.0657
- Gen Len: 58.0
## Model description
More information needed
## Intended uses & limitations
Code snippet -> NL intent
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:-------:|
| No log | 0.08 | 50 | 2.7823 | 35.8458 | 12.1898 | 33.7466 | 33.8377 | 0.0041 | 58.0 |
| No log | 0.17 | 100 | 2.4223 | 37.2633 | 13.429 | 34.4943 | 34.5533 | 0.0087 | 58.0 |
| No log | 0.25 | 150 | 2.2696 | 40.6963 | 16.5785 | 38.1213 | 38.16 | 0.0167 | 58.0 |
| No log | 0.34 | 200 | 2.3168 | 41.3324 | 17.292 | 39.0117 | 39.113 | 0.0173 | 58.0 |
| No log | 0.42 | 250 | 2.3187 | 41.1345 | 16.6829 | 38.8514 | 38.891 | 0.0237 | 58.0 |
| No log | 0.5 | 300 | 2.1701 | 41.0145 | 17.5601 | 39.166 | 39.249 | 0.0206 | 58.0 |
| No log | 0.59 | 350 | 2.2035 | 41.7506 | 17.7251 | 39.4856 | 39.5647 | 0.0292 | 58.0 |
| No log | 0.67 | 400 | 2.1006 | 43.0324 | 19.9801 | 40.8704 | 40.9399 | 0.0319 | 58.0 |
| No log | 0.76 | 450 | 2.0563 | 43.2151 | 18.7409 | 40.4183 | 40.502 | 0.0244 | 58.0 |
| 2.4902 | 0.84 | 500 | 2.0468 | 43.2215 | 18.3484 | 40.9498 | 41.0682 | 0.0317 | 58.0 |
| 2.4902 | 0.92 | 550 | 2.0222 | 44.9934 | 19.8389 | 42.4478 | 42.5687 | 0.0372 | 58.0 |
| 2.4902 | 1.01 | 600 | 2.1095 | 43.8293 | 19.5682 | 40.882 | 40.9518 | 0.0311 | 58.0 |
| 2.4902 | 1.09 | 650 | 2.0124 | 43.6928 | 19.6878 | 39.6602 | 39.7368 | 0.0417 | 58.0 |
| 2.4902 | 1.18 | 700 | 2.0027 | 46.2115 | 21.9475 | 43.5869 | 43.6713 | 0.0477 | 58.0 |
| 2.4902 | 1.26 | 750 | 1.9599 | 45.9388 | 22.0368 | 43.4731 | 43.5656 | 0.043 | 58.0 |
| 2.4902 | 1.34 | 800 | 1.9467 | 44.7518 | 20.4755 | 42.489 | 42.6274 | 0.0394 | 58.0 |
| 2.4902 | 1.43 | 850 | 1.9643 | 44.1584 | 20.8833 | 41.8848 | 41.9733 | 0.0441 | 58.0 |
| 2.4902 | 1.51 | 900 | 1.8926 | 47.3789 | 22.9104 | 45.0164 | 45.0822 | 0.0445 | 58.0 |
| 2.4902 | 1.6 | 950 | 1.8855 | 46.8329 | 22.1133 | 44.1788 | 44.2666 | 0.0431 | 58.0 |
| 1.8023 | 1.68 | 1000 | 1.9160 | 47.1319 | 22.9792 | 44.4807 | 44.6103 | 0.0475 | 58.0 |
| 1.8023 | 1.76 | 1050 | 1.8498 | 48.8005 | 24.4785 | 46.4564 | 46.5427 | 0.0576 | 58.0 |
| 1.8023 | 1.85 | 1100 | 1.8611 | 47.8327 | 23.2086 | 45.5999 | 45.6868 | 0.0487 | 58.0 |
| 1.8023 | 1.93 | 1150 | 1.8497 | 47.7267 | 23.2021 | 45.5104 | 45.546 | 0.0512 | 58.0 |
| 1.8023 | 2.02 | 1200 | 1.8335 | 47.1502 | 22.8336 | 44.7614 | 44.7927 | 0.0566 | 58.0 |
| 1.8023 | 2.1 | 1250 | 1.8779 | 46.6645 | 22.9162 | 44.0086 | 44.2021 | 0.0539 | 58.0 |
| 1.8023 | 2.18 | 1300 | 1.8514 | 48.1544 | 24.7977 | 45.949 | 46.0254 | 0.0719 | 58.0 |
| 1.8023 | 2.27 | 1350 | 1.8658 | 46.7655 | 23.4813 | 44.5872 | 44.6907 | 0.069 | 58.0 |
| 1.8023 | 2.35 | 1400 | 1.8400 | 46.2749 | 23.6528 | 44.3149 | 44.4056 | 0.0572 | 58.0 |
| 1.8023 | 2.44 | 1450 | 1.8343 | 46.6169 | 23.8005 | 44.5486 | 44.6125 | 0.0547 | 58.0 |
| 1.3851 | 2.52 | 1500 | 1.8220 | 47.4739 | 24.3457 | 45.4959 | 45.6216 | 0.0662 | 58.0 |
| 1.3851 | 2.61 | 1550 | 1.8333 | 47.6311 | 24.3616 | 45.5904 | 45.6146 | 0.0666 | 58.0 |
| 1.3851 | 2.69 | 1600 | 1.8091 | 47.4633 | 24.0785 | 45.2493 | 45.2845 | 0.0645 | 58.0 |
| 1.3851 | 2.77 | 1650 | 1.8085 | 47.6495 | 23.8386 | 45.5077 | 45.5848 | 0.0639 | 58.0 |
| 1.3851 | 2.86 | 1700 | 1.8377 | 46.9721 | 23.4325 | 44.8386 | 44.9003 | 0.0647 | 58.0 |
| 1.3851 | 2.94 | 1750 | 1.8238 | 47.5266 | 23.9843 | 45.3897 | 45.473 | 0.0653 | 58.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 2.1.0
- Tokenizers 0.10.3
|
omar47/wav2vec2-large-xls-r-300m-urdu-cv8-200epochs | 2e040ad35bfa2e6063a5a4bd869b7d9cd8d3921b | 2022-04-21T05:43:51.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | omar47 | null | omar47/wav2vec2-large-xls-r-300m-urdu-cv8-200epochs | 3 | null | transformers | 22,235 | ---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-urdu-cv8-200epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-urdu-cv8-200epochs
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3200
- Wer: 0.7723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 13
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3204 | 1.27 | 32 | 1.3200 | 0.7723 |
| 0.3021 | 2.55 | 64 | 1.3200 | 0.7723 |
| 0.3153 | 3.82 | 96 | 1.3200 | 0.7723 |
| 0.3239 | 5.12 | 128 | 1.3200 | 0.7723 |
| 0.3153 | 6.39 | 160 | 1.3200 | 0.7723 |
| 0.3202 | 7.67 | 192 | 1.3200 | 0.7723 |
| 0.3126 | 8.94 | 224 | 1.3200 | 0.7723 |
| 0.3183 | 10.24 | 256 | 1.3200 | 0.7723 |
| 0.3135 | 11.51 | 288 | 1.3200 | 0.7723 |
| 0.3137 | 12.78 | 320 | 1.3200 | 0.7723 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.12.1
|
birgermoell/wav2vec2-common_voice-lithuanian | 56ea298da3aee6555f11e072cf60e0fe986d1811 | 2022-04-20T08:38:35.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/wav2vec2-common_voice-lithuanian | 3 | null | transformers | 22,236 | ---
language:
- lt
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-lithuanian
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-common_voice-lithuanian
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - LT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5988
- Wer: 0.6546
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 2.7 | 100 | 3.5524 | 1.0 |
| No log | 5.41 | 200 | 3.0275 | 1.0 |
| No log | 8.11 | 300 | 1.8796 | 1.0003 |
| No log | 10.81 | 400 | 0.6796 | 0.7686 |
| 3.3102 | 13.51 | 500 | 0.6373 | 0.7297 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mwong/roberta-base-fever-evidence-related | e29e51200474b100a89df9d166366eec097f7932 | 2022-06-24T03:33:25.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:mwong/fever-evidence-related",
"transformers",
"text classification",
"fact checking",
"license:mit"
] | text-classification | false | mwong | null | mwong/roberta-base-fever-evidence-related | 3 | 1 | transformers | 22,237 | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/fever-evidence-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change and adverse effects of drilling explosions and oil spills in the Gulf of Mexico, legislation has been considered, and governmental regulations and orders have been issued, which, combined with the local economic and employment conditions caused by both, could materially adversely impact the oil and gas industries and the economic health of areas in which a significant number of our stores are located."
example_title: "Evidence related to claim"
metrics: f1
---
# FeverRoberta
FeverRoberta is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 92.67% with test dataset "mwong/fever-evidence-related". Using pretrained roberta-base model, the classifier head is trained on Fever dataset. |
mwong/albert-base-climate-claim-related | 575eff2235e0f2de7532b412457c46c105a4dada | 2022-06-24T03:35:34.000Z | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:mwong/fever-claim-related",
"dataset:mwong/climate-claim-related",
"transformers",
"text classification",
"fact checking",
"license:mit"
] | text-classification | false | mwong | null | mwong/albert-base-climate-claim-related | 3 | 1 | transformers | 22,238 | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/fever-claim-related
- mwong/climate-claim-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change and adverse effects of drilling explosions and oil spills in the Gulf of Mexico, legislation has been considered, and governmental regulations and orders have been issued, which, combined with the local economic and employment conditions caused by both, could materially adversely impact the oil and gas industries and the economic health of areas in which a significant number of our stores are located."
example_title: "Evidence related to claim"
metrics: f1
---
# ClimateAlbert
ClimateAlbert is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 85.33% with test dataset "mwong/climate-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset and adapted to climate domain using ClimateFever dataset. |
mwong/climatebert-base-f-climate-evidence-related | 86db8ab1ea7f751178607a4e99d8263f9093f318 | 2022-06-24T03:32:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:mwong/fever-evidence-related",
"dataset:mwong/climate-evidence-related",
"transformers",
"text classification",
"fact checking",
"license:mit"
] | text-classification | false | mwong | null | mwong/climatebert-base-f-climate-evidence-related | 3 | 1 | transformers | 22,239 | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/fever-evidence-related
- mwong/climate-evidence-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change and adverse effects of drilling explosions and oil spills in the Gulf of Mexico, legislation has been considered, and governmental regulations and orders have been issued, which, combined with the local economic and employment conditions caused by both, could materially adversely impact the oil and gas industries and the economic health of areas in which a significant number of our stores are located."
example_title: "Evidence related to claim"
metrics: f1
---
# ClimateBert-related
ClimateBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 81.90% with test dataset "mwong/climate-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset and adapted to climate domain using ClimateFever dataset. |
orendar/light_generator | aef79913bf8a358114d1fa6f6015806da3254726 | 2022-04-20T16:35:27.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | orendar | null | orendar/light_generator | 3 | null | transformers | 22,240 | Entry not found |
FrozenWolf/dummy-model | 52afddf333219d6c587606d952cd1f09b57fbe33 | 2022-04-20T18:05:28.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | FrozenWolf | null | FrozenWolf/dummy-model | 3 | null | transformers | 22,241 | Entry not found |
BigSalmon/InformalToFormalLincoln39 | d40444abcc06a601d3308f56af200b7b62d5226b | 2022-04-24T15:00:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln39 | 3 | null | transformers | 22,242 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln39")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln39")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
``` |
supriyaraj47/deberta-base-nli | 56d45cc173ac1f5fb68da6d1b5cc7174d22012ca | 2022-04-20T21:33:22.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers"
] | text-classification | false | supriyaraj47 | null | supriyaraj47/deberta-base-nli | 3 | null | transformers | 22,243 | Entry not found |
Goud/DziriBERT-summarization-goud | f423211db351cf22cffa3d7f6988df8d04e2f4c7 | 2022-04-29T15:06:30.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"Moroccan Arabic (MA)",
"Modern Standard Arabic (MSA)",
"dataset:Goud/Goud-sum",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | Goud | null | Goud/DziriBERT-summarization-goud | 3 | 1 | transformers | 22,244 | ---
datasets:
- Goud/Goud-sum
language:
- "Moroccan Arabic (MA)"
- "Modern Standard Arabic (MSA)"
metrics:
- rouge
tags:
- summarization
widget:
-
text: "توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت. وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير. ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها. ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة. وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”. وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي. وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا. "
---
This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [DziriBERT](https://huggingface.co/alger-ia/dziribert) checkpoint. The model is finetuned for text summarization on [Goud dataset](https://huggingface.co/datasets/Goud/Goud-sum).
## How to use
This is how you can use this model
```python
from transformers import EncoderDecoderModel, BertTokenizer
article = """توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت.
وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير.
ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها.
ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة.
وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”.
وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي.
وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا.
"""
tokenizer = BertTokenizer.from_pretrained("Goud/DziriBERT-summarization-goud")
model = EncoderDecoderModel.from_pretrained("Goud/DziriBERT-summarization-goud")
input_ids = tokenizer(article, return_tensors="pt", truncation=True, padding=True).input_ids
generated = model.generate(input_ids)[0]
output = tokenizer.decode(generated, skip_special_tokens=True)
```
## Citation Information
```
@inproceedings{issam2022goudma,
title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija},
author={Abderrahmane Issam and Khalil Mrini},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=BMVq5MELb9}
}
``` |
4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter7 | 2982e53ff037decbc047f45592660ce7e5a716fb | 2022-04-21T05:54:48.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | 4m1g0 | null | 4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter7 | 3 | null | transformers | 22,245 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-gl-jupyter7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-gl-jupyter7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1004
- Wer: 0.0647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8074 | 3.36 | 400 | 0.4882 | 0.5245 |
| 0.2396 | 6.72 | 800 | 0.1335 | 0.1524 |
| 0.0876 | 10.08 | 1200 | 0.1216 | 0.1199 |
| 0.0597 | 13.44 | 1600 | 0.1289 | 0.1241 |
| 0.0449 | 16.8 | 2000 | 0.1164 | 0.1028 |
| 0.0372 | 20.17 | 2400 | 0.1270 | 0.1023 |
| 0.0319 | 23.53 | 2800 | 0.1111 | 0.0966 |
| 0.0286 | 26.89 | 3200 | 0.1142 | 0.0925 |
| 0.0246 | 30.25 | 3600 | 0.1142 | 0.0926 |
| 0.0235 | 33.61 | 4000 | 0.1075 | 0.0836 |
| 0.0181 | 36.97 | 4400 | 0.1083 | 0.0837 |
| 0.0151 | 40.33 | 4800 | 0.1140 | 0.0768 |
| 0.014 | 43.69 | 5200 | 0.1015 | 0.0748 |
| 0.0111 | 47.06 | 5600 | 0.1023 | 0.0702 |
| 0.0093 | 50.42 | 6000 | 0.1028 | 0.0708 |
| 0.0078 | 53.78 | 6400 | 0.0999 | 0.0645 |
| 0.0071 | 57.14 | 6800 | 0.1004 | 0.0647 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
cammy/led-large-16384-arxiv-1000-lit-evalMA-ga1 | 92a73aa12db2b625bd2bd8e1dba9c1a5637e0c12 | 2022-04-21T03:50:40.000Z | [
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | cammy | null | cammy/led-large-16384-arxiv-1000-lit-evalMA-ga1 | 3 | null | transformers | 22,246 | Entry not found |
4m1g0/wav2vec2-large-xls-r-53m-gl-jupyter6 | 27b691125d33115c87e1cc6217741e24c0141afc | 2022-04-21T07:48:44.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 4m1g0 | null | 4m1g0/wav2vec2-large-xls-r-53m-gl-jupyter6 | 3 | null | transformers | 22,247 | Entry not found |
MeshalAlamr/wav2vec2-xls-r-300m-ar-4 | 858791f98a692844331d19441b2a774c9be55337 | 2022-04-26T04:16:51.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | MeshalAlamr | null | MeshalAlamr/wav2vec2-xls-r-300m-ar-4 | 3 | null | transformers | 22,248 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ar-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ar-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7888
- Wer: 0.3697
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8069 | 1.18 | 400 | 1.7793 | 0.9883 |
| 1.1949 | 2.35 | 800 | 0.9662 | 0.7908 |
| 0.8996 | 3.53 | 1200 | 0.8404 | 0.7154 |
| 0.7652 | 4.71 | 1600 | 0.7478 | 0.6379 |
| 0.6611 | 5.88 | 2000 | 0.7687 | 0.6229 |
| 0.6015 | 7.06 | 2400 | 0.7153 | 0.5948 |
| 0.5444 | 8.24 | 2800 | 0.7062 | 0.5826 |
| 0.4872 | 9.41 | 3200 | 0.6568 | 0.5414 |
| 0.4729 | 10.59 | 3600 | 0.6817 | 0.5599 |
| 0.4238 | 11.76 | 4000 | 0.6406 | 0.5262 |
| 0.4022 | 12.94 | 4400 | 0.6797 | 0.5184 |
| 0.3945 | 14.12 | 4800 | 0.6744 | 0.5147 |
| 0.3711 | 15.29 | 5200 | 0.6807 | 0.5090 |
| 0.3318 | 16.47 | 5600 | 0.6286 | 0.5011 |
| 0.3132 | 17.65 | 6000 | 0.6481 | 0.4814 |
| 0.2992 | 18.82 | 6400 | 0.6454 | 0.4958 |
| 0.2734 | 20.0 | 6800 | 0.6465 | 0.4825 |
| 0.2534 | 21.18 | 7200 | 0.6559 | 0.4658 |
| 0.2505 | 22.35 | 7600 | 0.6601 | 0.4618 |
| 0.2495 | 23.53 | 8000 | 0.7080 | 0.4813 |
| 0.2387 | 24.71 | 8400 | 0.6635 | 0.4508 |
| 0.2154 | 25.88 | 8800 | 0.6442 | 0.4538 |
| 0.2096 | 27.06 | 9200 | 0.7399 | 0.4579 |
| 0.2007 | 28.24 | 9600 | 0.6957 | 0.4512 |
| 0.1942 | 29.41 | 10000 | 0.6642 | 0.4267 |
| 0.1854 | 30.59 | 10400 | 0.6842 | 0.4393 |
| 0.1782 | 31.76 | 10800 | 0.7007 | 0.4393 |
| 0.1751 | 32.94 | 11200 | 0.7063 | 0.4321 |
| 0.1695 | 34.12 | 11600 | 0.7057 | 0.4330 |
| 0.1638 | 35.29 | 12000 | 0.7416 | 0.4266 |
| 0.1531 | 36.47 | 12400 | 0.7420 | 0.4273 |
| 0.1475 | 37.65 | 12800 | 0.7334 | 0.4218 |
| 0.1388 | 38.82 | 13200 | 0.7420 | 0.4227 |
| 0.1372 | 40.0 | 13600 | 0.7492 | 0.4238 |
| 0.1341 | 41.18 | 14000 | 0.7803 | 0.4193 |
| 0.133 | 42.35 | 14400 | 0.7396 | 0.4105 |
| 0.1238 | 43.53 | 14800 | 0.7561 | 0.4098 |
| 0.1163 | 44.71 | 15200 | 0.7987 | 0.4049 |
| 0.116 | 45.88 | 15600 | 0.7769 | 0.4093 |
| 0.1079 | 47.06 | 16000 | 0.7780 | 0.3986 |
| 0.1043 | 48.24 | 16400 | 0.7674 | 0.3905 |
| 0.1004 | 49.41 | 16800 | 0.7931 | 0.3949 |
| 0.0987 | 50.59 | 17200 | 0.7605 | 0.3938 |
| 0.0963 | 51.76 | 17600 | 0.7735 | 0.3858 |
| 0.0905 | 52.94 | 18000 | 0.7504 | 0.3802 |
| 0.086 | 54.12 | 18400 | 0.8038 | 0.3867 |
| 0.0839 | 55.29 | 18800 | 0.7887 | 0.3797 |
| 0.0798 | 56.47 | 19200 | 0.7832 | 0.3705 |
| 0.0785 | 57.65 | 19600 | 0.7771 | 0.3706 |
| 0.0765 | 58.82 | 20000 | 0.7858 | 0.3703 |
| 0.0739 | 60.0 | 20400 | 0.7888 | 0.3697 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.11.0
- Datasets 1.18.3
- Tokenizers 0.10.3
|
satyamrajawat1994/distillbert-base-uncase-conll2003 | 9911798506ecb92b77db2cbd947e88b393298645 | 2022-04-21T13:45:10.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | satyamrajawat1994 | null | satyamrajawat1994/distillbert-base-uncase-conll2003 | 3 | null | transformers | 22,249 | Entry not found |
satish860/sms_detection_algorithm | 23972cebe7d3069972fc867f721eaca5edcbf89f | 2022-04-21T16:42:17.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | satish860 | null | satish860/sms_detection_algorithm | 3 | null | transformers | 22,250 | Entry not found |
satish860/finetuning-sentiment-model-3000-samples | 7b3031fd94662c811e4ab8b6fb057f72c446b24b | 2022-04-21T17:02:49.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | satish860 | null | satish860/finetuning-sentiment-model-3000-samples | 3 | null | transformers | 22,251 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0454
- Accuracy: 0.9886
- F1: 0.9571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
|
buddhist-nlp/sanstib | 553a59b9dc1a966428f36e2b97adfc7ca4937251 | 2022-04-22T08:41:48.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers",
"license:lgpl-lr"
] | feature-extraction | false | buddhist-nlp | null | buddhist-nlp/sanstib | 3 | null | transformers | 22,252 | ---
license: lgpl-lr
---
|
tingzhou/finetuning_test | ff8701ed49db84b166ad1d1cf7280a2853cfeb8e | 2022-04-23T14:27:09.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | tingzhou | null | tingzhou/finetuning_test | 3 | null | transformers | 22,253 | Entry not found |
cj-mills/bert-base-uncased-issues-128 | 0f890d1bccd1e84155c2c1b00d39ffdcf56dc39c | 2022-04-22T18:29:07.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | cj-mills | null | cj-mills/bert-base-uncased-issues-128 | 3 | null | transformers | 22,254 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1071 | 1.0 | 291 | 1.6964 |
| 1.6421 | 2.0 | 582 | 1.4279 |
| 1.4853 | 3.0 | 873 | 1.3924 |
| 1.4014 | 4.0 | 1164 | 1.3701 |
| 1.3388 | 5.0 | 1455 | 1.1944 |
| 1.283 | 6.0 | 1746 | 1.2795 |
| 1.2394 | 7.0 | 2037 | 1.2671 |
| 1.2014 | 8.0 | 2328 | 1.2084 |
| 1.1668 | 9.0 | 2619 | 1.1783 |
| 1.14 | 10.0 | 2910 | 1.2076 |
| 1.1277 | 11.0 | 3201 | 1.2081 |
| 1.1053 | 12.0 | 3492 | 1.1628 |
| 1.0819 | 13.0 | 3783 | 1.2544 |
| 1.0763 | 14.0 | 4074 | 1.1695 |
| 1.0634 | 15.0 | 4365 | 1.1157 |
| 1.0637 | 16.0 | 4656 | 1.2526 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
lucaordronneau/finbert-finetuned-FG-SINGLE_SENTENCE-NEWS | 3523f2d0a6a950009b01de7e2c53a564880b46d0 | 2022-05-03T09:58:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | lucaordronneau | null | lucaordronneau/finbert-finetuned-FG-SINGLE_SENTENCE-NEWS | 3 | null | transformers | 22,255 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finbert-finetuned-FG-SINGLE_SENTENCE-NEWS
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. -->
# finbert-finetuned-FG-SINGLE_SENTENCE-NEWS
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2997
- Accuracy: 0.6414
- F1: 0.6295
## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 321 | 0.9371 | 0.5699 | 0.4333 |
| 0.9282 | 2.0 | 642 | 0.9135 | 0.5930 | 0.5447 |
| 0.9282 | 3.0 | 963 | 0.9900 | 0.6033 | 0.5823 |
| 0.6743 | 4.0 | 1284 | 1.0802 | 0.6142 | 0.6065 |
| 0.3134 | 5.0 | 1605 | 1.5156 | 0.6183 | 0.5971 |
| 0.3134 | 6.0 | 1926 | 1.3695 | 0.6319 | 0.6183 |
| 0.1709 | 7.0 | 2247 | 1.8746 | 0.6462 | 0.6267 |
| 0.1112 | 8.0 | 2568 | 2.0880 | 0.6176 | 0.6155 |
| 0.1112 | 9.0 | 2889 | 2.3953 | 0.6190 | 0.6087 |
| 0.0811 | 10.0 | 3210 | 2.3792 | 0.6339 | 0.6225 |
| 0.0608 | 11.0 | 3531 | 2.3783 | 0.6360 | 0.6282 |
| 0.0608 | 12.0 | 3852 | 2.5982 | 0.6544 | 0.6351 |
| 0.039 | 13.0 | 4173 | 2.7687 | 0.6346 | 0.6305 |
| 0.039 | 14.0 | 4494 | 2.8980 | 0.6414 | 0.6299 |
| 0.0206 | 15.0 | 4815 | 3.0858 | 0.6319 | 0.6253 |
| 0.0168 | 16.0 | 5136 | 3.2408 | 0.6244 | 0.6170 |
| 0.0168 | 17.0 | 5457 | 3.1809 | 0.6435 | 0.6293 |
| 0.0123 | 18.0 | 5778 | 3.2629 | 0.6449 | 0.6324 |
| 0.0055 | 19.0 | 6099 | 3.2866 | 0.6449 | 0.6308 |
| 0.0055 | 20.0 | 6420 | 3.2997 | 0.6414 | 0.6295 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/it_its_are_are | 1942a1c79c325857fc0cac3514e017c3472645af | 2022-05-02T22:36:16.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/it_its_are_are | 3 | null | transformers | 22,256 | ---
language: en
thumbnail: http://www.huggingtweets.com/it_its_are_are/1651530971798/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/1480214799539740676/S3W8I0f2_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">angelicism2727272628</div>
<div style="text-align: center; font-size: 14px;">@it_its_are_are</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 angelicism2727272628.
| Data | angelicism2727272628 |
| --- | --- |
| Tweets downloaded | 229 |
| Retweets | 35 |
| Short tweets | 20 |
| Tweets kept | 174 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1p6kjacr/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 @it_its_are_are's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sou4cazg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sou4cazg/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/it_its_are_are')
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)
|
ahmeddbahaa/mt5-base-finetuned-ar-wikilingua | b3f81f1525fdb3512bdc7a7e8d39a086fe9bdf99 | 2022-04-23T14:21:41.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"dataset:wiki_lingua",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | ahmeddbahaa | null | ahmeddbahaa/mt5-base-finetuned-ar-wikilingua | 3 | null | transformers | 22,257 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mt5-base-finetuned-ar-wikilingua
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. -->
# mt5-base-finetuned-ar-wikilingua
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6790
- Rouge-1: 19.46
- Rouge-2: 6.82
- Rouge-l: 17.57
- Gen Len: 18.83
- Bertscore: 70.18
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.9783 | 1.0 | 5111 | 4.0107 | 15.8 | 4.65 | 14.18 | 18.98 | 68.66 |
| 4.2093 | 2.0 | 10222 | 3.8664 | 16.46 | 5.17 | 15.08 | 18.91 | 68.5 |
| 4.0303 | 3.0 | 15333 | 3.7847 | 17.0 | 5.43 | 15.45 | 18.89 | 68.75 |
| 3.9165 | 4.0 | 20444 | 3.7405 | 17.03 | 5.5 | 15.45 | 18.86 | 68.78 |
| 3.8396 | 5.0 | 25555 | 3.7102 | 17.14 | 5.57 | 15.48 | 18.87 | 68.92 |
| 3.7825 | 6.0 | 30666 | 3.6944 | 17.64 | 5.73 | 15.96 | 18.82 | 69.14 |
| 3.7447 | 7.0 | 35777 | 3.6801 | 17.6 | 5.66 | 15.9 | 18.78 | 69.23 |
| 3.7203 | 8.0 | 40888 | 3.6790 | 17.94 | 5.81 | 16.21 | 18.81 | 69.29 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
omar47/wav2vec2-large-xls-r-300m-urdu-common_voice_8_0 | 3b45ba5fe7f041c7131bc40db6a918fb541d41fa | 2022-04-23T23:23:58.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | omar47 | null | omar47/wav2vec2-large-xls-r-300m-urdu-common_voice_8_0 | 3 | null | transformers | 22,258 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-urdu-common_voice_8_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-urdu-common_voice_8_0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3860
- Wer: 0.7546
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 14
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5253 | 1.27 | 32 | 1.3860 | 0.7546 |
| 0.524 | 2.55 | 64 | 1.3860 | 0.7546 |
| 0.5197 | 3.82 | 96 | 1.3860 | 0.7546 |
| 0.523 | 5.12 | 128 | 1.3860 | 0.7546 |
| 0.5224 | 6.39 | 160 | 1.3860 | 0.7546 |
| 0.5332 | 7.67 | 192 | 1.3860 | 0.7546 |
| 0.5227 | 8.94 | 224 | 1.3860 | 0.7546 |
| 0.5272 | 10.24 | 256 | 1.3860 | 0.7546 |
| 0.5294 | 11.51 | 288 | 1.3860 | 0.7546 |
| 0.5146 | 12.78 | 320 | 1.3860 | 0.7546 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.12.1
|
HJHGJGHHG/GAU-Base-Full | f5c867ca172749dc38daea80ff3947261779c02a | 2022-04-24T09:07:58.000Z | [
"pytorch",
"gau",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | HJHGJGHHG | null | HJHGJGHHG/GAU-Base-Full | 3 | null | transformers | 22,259 | Entry not found |
tingzhou/cn_finetuning | eecd2f2d2c5e2ce02acca3d79aaed7d929f77304 | 2022-04-24T14:11:45.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | tingzhou | null | tingzhou/cn_finetuning | 3 | null | transformers | 22,260 | Entry not found |
domenicrosati/t5-small-finetuned-contradiction-finetuned-contradiction | da262f293f592208fe6c6b6cef8b0b65708f97fb | 2022-04-24T14:55:23.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | domenicrosati | null | domenicrosati/t5-small-finetuned-contradiction-finetuned-contradiction | 3 | null | transformers | 22,261 | Entry not found |
fxxcyz/distilbert-base-uncased-finetuned-cola | e117104b4821b31155570bab99f4fe613ea7b9da | 2022-04-24T18:11:52.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | fxxcyz | null | fxxcyz/distilbert-base-uncased-finetuned-cola | 3 | null | transformers | 22,262 | Entry not found |
Felix92/doctr-torch-db-mobilenet-v3-large | c6e0a46f8dc464ff96561d74ee9bbaf895d2e15c | 2022-04-24T20:25:41.000Z | [
"pytorch",
"en",
"transformers"
] | null | false | Felix92 | null | Felix92/doctr-torch-db-mobilenet-v3-large | 3 | null | transformers | 22,263 |
---
language: en
---
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
chrishuber/roberta-kaggledev-testing | 516cdbef69a80c73a8b046d1a30a94783b504379 | 2022-04-25T00:05:27.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | chrishuber | null | chrishuber/roberta-kaggledev-testing | 3 | null | transformers | 22,264 | Entry not found |
wildsheepchaser/distilbert-base-uncased-finetuned-cola | 07bf98c1648f1ef339e33aa3b7d79e9ceb848dae | 2022-04-25T01:25:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | wildsheepchaser | null | wildsheepchaser/distilbert-base-uncased-finetuned-cola | 3 | null | transformers | 22,265 | Entry not found |
PSW/random_sim_del | 661fcb4c845dd7f1e20ab89141abf1cd3e6da312 | 2022-04-25T03:15:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/random_sim_del | 3 | null | transformers | 22,266 | Entry not found |
sai82/distilbert-base-uncased-finetuned-emotion | ff2079567088ecdb6063f464fb3cdff1ddce8f29 | 2022-04-25T03:11:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | sai82 | null | sai82/distilbert-base-uncased-finetuned-emotion | 3 | null | transformers | 22,267 | Entry not found |
Real29/my-model-nela | 97a12e2f8ac1c7d4c47343115ba190e15eac7b6f | 2022-04-25T18:47:03.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Real29 | null | Real29/my-model-nela | 3 | null | transformers | 22,268 | Entry not found |
PSW/max_sim_ins_seed1 | b9f1ef8b7e1b71e0c1e7ad13b4d84c82237972a2 | 2022-04-25T10:38:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/max_sim_ins_seed1 | 3 | null | transformers | 22,269 | Entry not found |
maximedb/glue_sst_classifier | db6a380a282ffef649ded3193ecd008690a3613c | 2022-04-25T19:42:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | maximedb | null | maximedb/glue_sst_classifier | 3 | null | transformers | 22,270 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- f1
- accuracy
model-index:
- name: glue_sst_classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: F1
type: f1
value: 0.9033707865168539
- name: Accuracy
type: accuracy
value: 0.9013761467889908
---
<!-- 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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2359
- F1: 0.9034
- Accuracy: 0.9014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.3653 | 0.19 | 100 | 0.3213 | 0.8717 | 0.8727 |
| 0.291 | 0.38 | 200 | 0.2662 | 0.8936 | 0.8911 |
| 0.2239 | 0.57 | 300 | 0.2417 | 0.9081 | 0.9060 |
| 0.2306 | 0.76 | 400 | 0.2359 | 0.9105 | 0.9094 |
| 0.2185 | 0.95 | 500 | 0.2371 | 0.9011 | 0.8991 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ddobokki/unsup-simcse-klue-roberta-base | c42c6889c5a8dd71f93ae0149b78f6090db129d6 | 2022-04-26T05:22:12.000Z | [
"pytorch",
"roberta",
"ko",
"transformers",
"simcse"
] | null | false | ddobokki | null | ddobokki/unsup-simcse-klue-roberta-base | 3 | null | transformers | 22,271 | ---
language:
- ko
tags:
- simcse
---
# KorSTS-dev
```
"eval_cosine_pearson": 0.8461074829101562
"eval_cosine_spearman": 0.8447369732456155
"eval_euclidean_pearson": 0.8401166200637817
"eval_euclidean_spearman": 0.8441547920405729
"eval_manhattan_pearson": 0.8404706120491028
"eval_manhattan_spearman": 0.8449217524976507
"eval_dot_pearson": 0.8457739353179932
"eval_dot_spearman": 0.8440466726739222
```
# KorSTS-test
```
"eval_cosine_pearson": 0.7702209949493408
"eval_cosine_spearman": 0.7671020822573297
"eval_euclidean_pearson": 0.7617944478988647
"eval_euclidean_spearman": 0.7651634975965186
"eval_manhattan_pearson": 0.7639209032058716
"eval_manhattan_spearman": 0.7674607376361398
"eval_dot_pearson": 0.7696021795272827
"eval_dot_spearman": 0.7667385347139427
``` |
Real29/my-model-proppy | cabad8f23e2198ec0f5aeecc4f15262121b2b786 | 2022-04-26T10:32:43.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Real29 | null | Real29/my-model-proppy | 3 | null | transformers | 22,272 | Entry not found |
Real29/my-model-jacobs | 4ab0e4d76c96889a789ee502b962d972c6bd360b | 2022-04-26T14:26:01.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Real29 | null | Real29/my-model-jacobs | 3 | null | transformers | 22,273 | Entry not found |
plowcow/distilbert-base-uncased-finetuned-emotion | 98bf27d8c650147f4b3efa13fb83fd2979b31772 | 2022-06-21T04:09:25.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | plowcow | null | plowcow/distilbert-base-uncased-finetuned-emotion | 3 | null | transformers | 22,274 | Entry not found |
Caroline-Vandyck/reviews-generator | e1a0b9cda0070e65134b04b3824c026ba26a639d | 2022-04-26T12:58:01.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Caroline-Vandyck | null | Caroline-Vandyck/reviews-generator | 3 | null | transformers | 22,275 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: reviews-generator
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. -->
# reviews-generator
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7955 | 0.08 | 500 | 3.5577 |
| 3.7495 | 0.16 | 1000 | 3.4990 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
rahulgkatre/DialoGPT-marge | 8a3de1c000af958866d425f56c767d3a2d355dd8 | 2022-04-27T03:21:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | rahulgkatre | null | rahulgkatre/DialoGPT-marge | 3 | null | transformers | 22,276 | Entry not found |
PSW/random_sim_ins2_seed27 | a3d44620ad6f81ddb723894536d11fd6b44496e0 | 2022-04-27T03:24:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/random_sim_ins2_seed27 | 3 | null | transformers | 22,277 | Entry not found |
manueltonneau/bert-twitter-pt-is-unemployed | f361fcb96ad8acf771220be33adc1edfea99c11c | 2022-04-27T09:07:42.000Z | [
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"transformers"
] | text-classification | false | manueltonneau | null | manueltonneau/bert-twitter-pt-is-unemployed | 3 | null | transformers | 22,278 | ---
language: pt # <-- my language
widget:
- text: "Tô desempregada!"
---
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets where a user mentions that she is currently unemployed. It was trained on Portuguese tweets from users based in Brazil. The task is framed as a binary classification problem with:
- the positive class referring to tweets mentioning that a user is currently unemployed (label=1)
- the negative class referring to all other tweets (label=0)
## Resources
The dataset of Portuguese tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment).
Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178).
## Citation
If you find this model useful, please cite our paper (citation to come soon). |
fxmarty/resnet-tiny-mnist | d64012c39d4ea6fad998cca3bad7fbf7987709ef | 2022-04-27T09:27:58.000Z | [
"pytorch",
"resnet",
"image-classification",
"transformers",
"license:gpl-3.0"
] | image-classification | false | fxmarty | null | fxmarty/resnet-tiny-mnist | 3 | null | transformers | 22,279 | ---
license: gpl-3.0
---
A small Resnet model for MNIST. Achieves 0.985 accuracy on the validation set. |
PSW/random_sim_swap_seed1 | 8cff3a861574f61d9c6934a3a18d8066e078dfb4 | 2022-04-27T09:49:18.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/random_sim_swap_seed1 | 3 | null | transformers | 22,280 | Entry not found |
Prinernian/xlm-roberta-base-finetuned-panx-de | 5e4b75b157e4cd14f13ed09ffe8488d1466a2c2e | 2022-05-18T19:30:06.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Prinernian | null | Prinernian/xlm-roberta-base-finetuned-panx-de | 3 | null | transformers | 22,281 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8588964027959312
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1383
- F1: 0.8589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2631 | 1.0 | 525 | 0.1596 | 0.8218 |
| 0.1296 | 2.0 | 1050 | 0.1353 | 0.8479 |
| 0.0821 | 3.0 | 1575 | 0.1383 | 0.8589 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european | d8f109ba4c0a717d78317d52b87b2c05afc14270 | 2022-04-28T09:56:22.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"all",
"dataset:google/xtreme_s",
"transformers",
"fleurs-asr",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anton-l | null | anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european | 3 | null | transformers | 22,282 | ---
language:
- all
license: apache-2.0
tags:
- fleurs-asr
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
model-index:
- name: xtreme_s_xlsr_300m_fleurs_asr_western_european
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. -->
# xtreme_s_xlsr_300m_fleurs_asr_western_european
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:
- Cer: 0.2484
- Cer Ast Es: 0.1598
- Cer Bs Ba: 0.1749
- Cer Ca Es: 0.1655
- Cer Cy Gb: 0.2280
- Cer Da Dk: 0.3616
- Cer De De: 0.1287
- Cer El Gr: 0.6020
- Cer En Us: 0.1938
- Cer Es 419: 0.1288
- Cer Fi Fi: 0.2050
- Cer Fr Fr: 0.1811
- Cer Ga Ie: 0.4474
- Cer Gl Es: 0.1324
- Cer Hr Hr: 0.1555
- Cer Hu Hu: 0.3911
- Cer Is Is: 0.4646
- Cer It It: 0.1283
- Cer Kea Cv: 0.1818
- Cer Lb Lu: 0.2594
- Cer Mt Mt: 0.3628
- Cer Nb No: 0.2254
- Cer Nl Nl: 0.1790
- Cer Oci Fr: 0.2159
- Cer Pt Br: 0.2275
- Cer Sv Se: 0.3092
- Loss: 1.3089
- Loss Ast Es: 0.7715
- Loss Bs Ba: 0.7378
- Loss Ca Es: 0.7868
- Loss Cy Gb: 1.1441
- Loss Da Dk: 1.9130
- Loss De De: 0.5391
- Loss El Gr: 3.4904
- Loss En Us: 0.9632
- Loss Es 419: 0.6186
- Loss Fi Fi: 0.8953
- Loss Fr Fr: 0.9076
- Loss Ga Ie: 3.0217
- Loss Gl Es: 0.5788
- Loss Hr Hr: 0.6462
- Loss Hu Hu: 1.9029
- Loss Is Is: 2.6551
- Loss It It: 0.6052
- Loss Kea Cv: 0.9107
- Loss Lb Lu: 1.3705
- Loss Mt Mt: 2.3651
- Loss Nb No: 1.1518
- Loss Nl Nl: 0.8490
- Loss Oci Fr: 1.1421
- Loss Pt Br: 1.1641
- Loss Sv Se: 1.5910
- Wer: 0.6451
- Wer Ast Es: 0.4654
- Wer Bs Ba: 0.5443
- Wer Ca Es: 0.4979
- Wer Cy Gb: 0.5962
- Wer Da Dk: 0.8455
- Wer De De: 0.4221
- Wer El Gr: 0.9805
- Wer En Us: 0.4556
- Wer Es 419: 0.3928
- Wer Fi Fi: 0.8116
- Wer Fr Fr: 0.4690
- Wer Ga Ie: 0.8519
- Wer Gl Es: 0.4245
- Wer Hr Hr: 0.4895
- Wer Hu Hu: 0.9099
- Wer Is Is: 0.9960
- Wer It It: 0.4415
- Wer Kea Cv: 0.5202
- Wer Lb Lu: 0.7225
- Wer Mt Mt: 1.0096
- Wer Nb No: 0.6541
- Wer Nl Nl: 0.5257
- Wer Oci Fr: 0.5770
- Wer Pt Br: 0.6685
- Wer Sv Se: 0.8546
- Predict Samples: 20043
## 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: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- 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 | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.1411 | 0.49 | 500 | 3.1673 | 1.0 | 1.0 |
| 0.6397 | 0.97 | 1000 | 0.9039 | 0.7171 | 0.2862 |
| 0.4033 | 1.46 | 1500 | 0.8914 | 0.6862 | 0.2763 |
| 0.3473 | 1.94 | 2000 | 0.8017 | 0.6505 | 0.2536 |
| 0.3143 | 2.43 | 2500 | 0.8568 | 0.6566 | 0.2627 |
| 0.3004 | 2.91 | 3000 | 0.8898 | 0.6640 | 0.2686 |
| 0.282 | 3.4 | 3500 | 0.8489 | 0.6637 | 0.2571 |
| 0.2489 | 3.88 | 4000 | 0.8955 | 0.6744 | 0.2691 |
| 0.1706 | 4.37 | 4500 | 0.9190 | 0.6788 | 0.2688 |
| 0.3336 | 4.85 | 5000 | 0.8915 | 0.6594 | 0.2572 |
| 0.1426 | 5.34 | 5500 | 0.9501 | 0.6784 | 0.2686 |
| 0.2301 | 5.83 | 6000 | 1.0217 | 0.6719 | 0.2735 |
| 0.1325 | 6.31 | 6500 | 0.9578 | 0.6691 | 0.2655 |
| 0.1145 | 6.8 | 7000 | 0.9129 | 0.6680 | 0.2593 |
| 0.1202 | 7.28 | 7500 | 0.9646 | 0.6749 | 0.2619 |
| 0.143 | 7.77 | 8000 | 0.9200 | 0.6554 | 0.2554 |
| 0.1012 | 8.25 | 8500 | 0.9553 | 0.6787 | 0.2628 |
| 0.1018 | 8.74 | 9000 | 0.9455 | 0.6445 | 0.2511 |
| 0.1148 | 9.22 | 9500 | 1.0206 | 0.6725 | 0.2629 |
| 0.0794 | 9.71 | 10000 | 0.9305 | 0.6547 | 0.2526 |
| 0.2891 | 10.19 | 10500 | 1.0424 | 0.6709 | 0.2570 |
| 0.1665 | 10.68 | 11000 | 0.9760 | 0.6596 | 0.2507 |
| 0.1956 | 11.17 | 11500 | 0.9549 | 0.6340 | 0.2440 |
| 0.0828 | 11.65 | 12000 | 0.9598 | 0.6403 | 0.2460 |
| 0.059 | 12.14 | 12500 | 0.9972 | 0.6574 | 0.2531 |
| 0.0505 | 12.62 | 13000 | 0.9836 | 0.6534 | 0.2525 |
| 0.0336 | 13.11 | 13500 | 1.0619 | 0.6564 | 0.2519 |
| 0.0435 | 13.59 | 14000 | 1.0844 | 0.6480 | 0.2543 |
| 0.0216 | 14.08 | 14500 | 1.1084 | 0.6512 | 0.2521 |
| 0.0265 | 14.56 | 15000 | 1.1152 | 0.6607 | 0.2563 |
| 0.0975 | 15.05 | 15500 | 1.1060 | 0.6456 | 0.2471 |
| 0.1396 | 15.53 | 16000 | 1.1100 | 0.6337 | 0.2418 |
| 0.0701 | 16.02 | 16500 | 1.1731 | 0.6309 | 0.2415 |
| 0.1171 | 16.5 | 17000 | 1.1302 | 0.6315 | 0.2396 |
| 0.0778 | 16.99 | 17500 | 1.1485 | 0.6379 | 0.2447 |
| 0.0642 | 17.48 | 18000 | 1.2009 | 0.6400 | 0.2464 |
| 0.0322 | 17.96 | 18500 | 1.2028 | 0.6357 | 0.2425 |
| 0.031 | 18.45 | 19000 | 1.2381 | 0.6285 | 0.2416 |
| 0.0579 | 18.93 | 19500 | 1.2299 | 0.6265 | 0.2409 |
| 0.0628 | 19.42 | 20000 | 1.2582 | 0.6277 | 0.2395 |
| 0.074 | 19.9 | 20500 | 1.2572 | 0.6278 | 0.2394 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.1+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
|
ajtamayoh/roberta-large-finetuned-ADEs_model_2 | 6c3b6c11c22a90852199ad95432ef2d1428c21a8 | 2022-04-27T21:33:50.000Z | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ajtamayoh | null | ajtamayoh/roberta-large-finetuned-ADEs_model_2 | 3 | null | transformers | 22,283 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ADEs_model_2
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-large-finetuned-ADEs_model_2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2580
- Precision: 0.5407
- Recall: 0.6311
- F1: 0.5824
- Accuracy: 0.8897
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7461 | 1.0 | 640 | 0.3393 | 0.4247 | 0.5095 | 0.4633 | 0.8648 |
| 0.3632 | 2.0 | 1280 | 0.2822 | 0.4934 | 0.6035 | 0.5429 | 0.8819 |
| 0.3102 | 3.0 | 1920 | 0.2663 | 0.5218 | 0.6112 | 0.5630 | 0.8879 |
| 0.2806 | 4.0 | 2560 | 0.2604 | 0.5337 | 0.6311 | 0.5783 | 0.8890 |
| 0.2772 | 5.0 | 3200 | 0.2580 | 0.5407 | 0.6311 | 0.5824 | 0.8897 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/afraidofwasps-dril-senn_spud | a88786922f7b4a04e16359e053b008a67993afcc | 2022-06-07T21:10:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/afraidofwasps-dril-senn_spud | 3 | null | transformers | 22,284 | ---
language: en
thumbnail: http://www.huggingtweets.com/afraidofwasps-dril-senn_spud/1654636210975/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/1510917391533830145/XW-zSFDJ_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/1387151448203358209/HKNuKY7L_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/1182478458552832000/xqEwluRJ_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 & Will Sennett & Boots, 'with the fur'</div>
<div style="text-align: center; font-size: 14px;">@afraidofwasps-dril-senn_spud</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 & Will Sennett & Boots, 'with the fur'.
| Data | wint | Will Sennett | Boots, 'with the fur' |
| --- | --- | --- | --- |
| Tweets downloaded | 3230 | 3228 | 3217 |
| Retweets | 487 | 312 | 504 |
| Short tweets | 297 | 622 | 434 |
| Tweets kept | 2446 | 2294 | 2279 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/156iladp/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 @afraidofwasps-dril-senn_spud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6g2dktc9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6g2dktc9/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/afraidofwasps-dril-senn_spud')
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)
|
princeton-nlp/efficient_mlm_m0.50 | 0f356f1fd8642661fc4d9c70544c9e5356059447 | 2022-04-28T18:58:09.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"transformers",
"autotrain_compatible"
] | fill-mask | false | princeton-nlp | null | princeton-nlp/efficient_mlm_m0.50 | 3 | null | transformers | 22,285 | ---
inference: false
---
This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example,
``` bash
from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification
``` |
aditeyabaral/sonobois | 9e8801d585d4a2fe32ea91a08c61d2215c56177c | 2022-04-29T07:32:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | aditeyabaral | null | aditeyabaral/sonobois | 3 | null | transformers | 22,286 | ---
tags:
- conversational
---
# Model trained on sonobois convos |
zhiguoxu/distilbert-base-uncased-finetuned-emotion | 6775925218c2451103f53acb76f3855771dce208 | 2022-04-29T11:59:42.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | zhiguoxu | null | zhiguoxu/distilbert-base-uncased-finetuned-emotion | 3 | null | transformers | 22,287 | Entry not found |
doc2query/msmarco-arabic-mt5-base-v1 | aec7e95a7efcdf32b81048b70f81c814e2d6a899 | 2022-04-29T11:42:59.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"ar",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-arabic-mt5-base-v1 | 3 | null | transformers | 22,288 | ---
language: ar
datasets:
- unicamp-dl/mmarco
widget:
- text: "بايثون (بالإنجليزية: Python) هي لغة برمجة، عالية المستوى سهلة التعلم مفتوحة المصدر قابلة للتوسيع، تعتمد أسلوب البرمجة الكائنية (OOP). لغة بايثون هي لغة مُفسَّرة، ومُتعدِدة الاستخدامات، وتستخدم بشكل واسع في العديد من المجالات، كبناء البرامج المستقلة باستخدام الواجهات الرسومية وفي تطبيقات الويب، ويمكن استخدامها كلغة برمجة نصية للتحكم في أداء العديد من البرمجيات مثل بلندر. بشكل عام، يمكن استخدام بايثون لعمل البرامج البسيطة للمبتدئين، ولإنجاز المشاريع الضخمة في الوقت نفسه. غالباً ما يُنصح المبتدؤون في ميدان البرمجة بتعلم هذه اللغة لأنها من بين أسرع اللغات البرمجية تعلماً."
license: apache-2.0
---
# doc2query/msmarco-arabic-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (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/beir-cellar/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. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) 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 AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-arabic-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "بايثون (بالإنجليزية: Python) هي لغة برمجة، عالية المستوى سهلة التعلم مفتوحة المصدر قابلة للتوسيع، تعتمد أسلوب البرمجة الكائنية (OOP). لغة بايثون هي لغة مُفسَّرة، ومُتعدِدة الاستخدامات، وتستخدم بشكل واسع في العديد من المجالات، كبناء البرامج المستقلة باستخدام الواجهات الرسومية وفي تطبيقات الويب، ويمكن استخدامها كلغة برمجة نصية للتحكم في أداء العديد من البرمجيات مثل بلندر. بشكل عام، يمكن استخدام بايثون لعمل البرامج البسيطة للمبتدئين، ولإنجاز المشاريع الضخمة في الوقت نفسه. غالباً ما يُنصح المبتدؤون في ميدان البرمجة بتعلم هذه اللغة لأنها من بين أسرع اللغات البرمجية تعلماً."
def create_queries(para):
input_ids = tokenizer.encode(para, return_tensors='pt')
with torch.no_grad():
# Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality
sampling_outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
top_k=10,
num_return_sequences=5
)
# Here we use Beam-search. It generates better quality queries, but with less diversity
beam_outputs = model.generate(
input_ids=input_ids,
max_length=64,
num_beams=5,
no_repeat_ngram_size=2,
num_return_sequences=5,
early_stopping=True
)
print("Paragraph:")
print(para)
print("\nBeam Outputs:")
for i in range(len(beam_outputs)):
query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
print("\nSampling Outputs:")
for i in range(len(sampling_outputs)):
query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
create_queries(text)
```
**Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.
## Training
This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
|
scasutt/wav2vec2-large-xlsr-53_full_final_train | 1a85afba2d9d33dbfa5fb9144e7f988dc9b00484 | 2022-05-07T11:52:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-large-xlsr-53_full_final_train | 3 | null | transformers | 22,289 | Entry not found |
csikasote/xlsr-53-bemba-5hrs | 070c9c786f21ded5a810a1f47f75241d4954be41 | 2022-04-29T23:40:17.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | csikasote | null | csikasote/xlsr-53-bemba-5hrs | 3 | null | transformers | 22,290 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xlsr-53-bemba-5hrs
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. -->
# xlsr-53-bemba-5hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3414
- Wer: 0.4867
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2701 | 2.16 | 400 | 0.4047 | 0.6230 |
| 0.488 | 4.32 | 800 | 0.3002 | 0.4917 |
| 0.2807 | 6.49 | 1200 | 0.3342 | 0.4802 |
| 0.1696 | 8.65 | 1600 | 0.3414 | 0.4867 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tonydiana1/distilroberta-base-finetuned-wikitext2 | 41e637ada53acd14783b113addbaeb22c40b6319 | 2022-04-30T01:23:18.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | tonydiana1 | null | tonydiana1/distilroberta-base-finetuned-wikitext2 | 3 | null | transformers | 22,291 | ---
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: 1.8347
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0853 | 1.0 | 2406 | 1.9214 |
| 1.986 | 2.0 | 4812 | 1.8799 |
| 1.9568 | 3.0 | 7218 | 1.8202 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jacklindsai/is_it_elon_musk | 292bc19761b4acd3dd28c35188c7083db1bf07e7 | 2022-04-30T05:33:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | jacklindsai | null | jacklindsai/is_it_elon_musk | 3 | null | transformers | 22,292 | Entry not found |
dyyyyyyyy/xTune_squad_XLM-RoBERTa-large | 7bffd381d0027673788b6a7bd23677d1fc82a125 | 2022-04-30T09:01:23.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
] | null | false | dyyyyyyyy | null | dyyyyyyyy/xTune_squad_XLM-RoBERTa-large | 3 | null | transformers | 22,293 | Entry not found |
shumail/wav2vec2-base-timit-demo-colab | 2c895c92f1c2b442b445bc08e60df9c03452dd57 | 2022-05-01T07:13:08.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | shumail | null | shumail/wav2vec2-base-timit-demo-colab | 3 | null | transformers | 22,294 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8686
- Wer: 0.6263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0505 | 13.89 | 500 | 3.0760 | 1.0 |
| 1.2748 | 27.78 | 1000 | 0.8686 | 0.6263 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ali221000262/wav2vec2-base-timit-demo-colab | 690b91842210741af5dcf82684fa67aded64e266 | 2022-04-30T18:01:43.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ali221000262 | null | ali221000262/wav2vec2-base-timit-demo-colab | 3 | null | transformers | 22,295 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [ali221000262/wav2vec2-base-timit-demo-colab](https://huggingface.co/ali221000262/wav2vec2-base-timit-demo-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2161
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.6432 | 13.89 | 500 | 3.2161 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
doddle124578/wav2vec2-base-timit-demo-colab-1 | ba1b20d7f45c423f2377d3ccf281d9058a2225d7 | 2022-05-01T12:53:33.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | doddle124578 | null | doddle124578/wav2vec2-base-timit-demo-colab-1 | 3 | null | transformers | 22,296 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6513
- Wer: 0.5544
## 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: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.6074 | 8.77 | 500 | 3.1529 | 1.0 |
| 1.3204 | 17.54 | 1000 | 0.6513 | 0.5544 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
doddle124578/wav2vec2-base-timit-demo-colab-2 | 134e01305d35da523f0ec0e1348d5975af70785b | 2022-04-30T18:57:05.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | doddle124578 | null | doddle124578/wav2vec2-base-timit-demo-colab-2 | 3 | null | transformers | 22,297 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7429
- Wer: 0.5080
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 900
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.984 | 8.77 | 500 | 0.9028 | 0.7036 |
| 0.6412 | 17.54 | 1000 | 0.7275 | 0.5868 |
| 0.3073 | 26.32 | 1500 | 0.7429 | 0.5080 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Worldman/pegasus-samsum | d179db9465e6270c6beb4f9c145f66dfd3fafc90 | 2022-04-30T23:42:21.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Worldman | null | Worldman/pegasus-samsum | 3 | null | transformers | 22,298 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4841
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7073 | 0.54 | 500 | 1.4841 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ouyh18/distilbert-base-uncased-finetuned-cola | 71a064976beb51da2b8d0bc13e204b861cb37753 | 2022-05-01T03:43:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ouyh18 | null | ouyh18/distilbert-base-uncased-finetuned-cola | 3 | null | transformers | 22,299 | ---
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: 0.5500173690801187
---
<!-- 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: 0.8456
- Matthews Correlation: 0.5500
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5197 | 1.0 | 535 | 0.5477 | 0.4130 |
| 0.3456 | 2.0 | 1070 | 0.5035 | 0.5239 |
| 0.2342 | 3.0 | 1605 | 0.6100 | 0.5285 |
| 0.1698 | 4.0 | 2140 | 0.7556 | 0.5456 |
| 0.1295 | 5.0 | 2675 | 0.8456 | 0.5500 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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