index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
599 | abhishek/autonlp-imdb_eval-71421 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-imdb_eval
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- R... |
600 | abhishek/autonlp-imdb_sentiment_classification-31154 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219... |
601 | abhishek/autonlp-japanese-sentiment-59362 | [
"negative",
"positive"
] | ---
tags: autonlp
language: ja
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-japanese-sentiment
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59362
## Validation Metrics
- Loss: 0.13092292845249176
- Accuracy: 0.9527127414314258
- Precision: 0.9634070704... |
602 | abhishek/autonlp-japanese-sentiment-59363 | [
"negative",
"positive"
] | ---
tags: autonlp
language: ja
widget:
- text: "🤗AutoNLPが大好きです"
datasets:
- abhishek/autonlp-data-japanese-sentiment
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.972968827882... |
603 | abhishek/autonlp-toxic-new-30516963 | [
"False",
"True"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-toxic-new
co2_eq_emissions: 30.684995819386277
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss... |
604 | adam-chell/tweet-sentiment-analyzer | [
"NEG",
"NEU",
"POS"
] | This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : adam.chellaoui@epfl.ch |
605 | adamlin/filter | [
"LABEL_0"
] | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
model_index:
- name: filter
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
---
<!-- This model card has been generated automatically according to... |
606 | addy88/perceiver_imdb | [
"neg",
"pos"
] | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverTokenizer, PerceiverForMaskedLM
tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb")
model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb")
text = "This is an incomplete sentence where ... |
607 | addy88/programming-lang-identifier | [
"go",
"java",
"javascript",
"php",
"python",
"ruby"
] | This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/progr... |
608 | adelgasmi/autonlp-kpmg_nlp-18833547 | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: ar
widget:
- text: "I love AutoNLP 🤗"
datasets:
- adelgasmi/autonlp-data-kpmg_nlp
co2_eq_emissions: 64.58945483765274
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18833547
- CO2 Emissions (in grams): 64.58945483765274
## Validation Metrics
- L... |
610 | Jackett/subject_classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
|
611 | adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717 | [
"Dance",
"Heavy Metal",
"Hip Hop",
"Indie",
"Pop",
"Rock"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- adrianmoses/autonlp-data-auto-nlp-lyrics-classification
co2_eq_emissions: 88.89388195672073
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 19333717
- CO2 Emissions (in grams): 88.89388195672073
##... |
613 | ahmedrachid/FinancialBERT-Sentiment-Analysis | [
"negative",
"neutral",
"positive"
] | ---
language: en
tags:
- financial-sentiment-analysis
- sentiment-analysis
datasets:
- financial_phrasebank
widget:
- text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.
- text: Bids or offers include at least 1,000 shares and the value of the ... |
614 | ainize/klue-bert-base-re | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a>
- <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Gi... |
617 | akahana/indonesia-emotion-roberta | [
"SEDIH",
"MARAH",
"CINTA",
"TAKUT",
"BAHAGIA"
] | ---
language: "id"
widget:
- text: "dia orang yang baik ya bunds."
---
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-emotion-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang... |
618 | akahana/indonesia-sentiment-roberta | [
"POSITIF",
"NETRAL",
"NEGATIF"
] | ---
language: "id"
widget:
- text: "dia orang yang baik ya bunds."
---
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-sentiment-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang yang baik ya ... |
619 | akdeniz27/bert-turkish-text-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8"
] | ---
language: tr
---
# Turkish Text Classification for Complaints Data Set
This model is a fine-tune model of https://github.com/stefan-it/turkish-bert by using text classification data with 9 categories as follows:
id_to_category = {0: 'KONFORSUZLUK', 1: 'TARİFE İHLALİ', 2: 'DURAKTA DURMAMA', 3: 'ŞOFÖR-PERSONEL ŞİK... |
620 | akhooli/xlm-r-large-arabic-sent | [
"LABEL_0_mixed",
"LABEL_1_neg",
"LABEL_2_pos"
] | ---
language:
- ar
- en
- multilingual
license: mit
---
### xlm-r-large-arabic-sent
Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic &... |
621 | akhooli/xlm-r-large-arabic-toxic | [
"LABEL_0_negative",
"LABEL_1_positive"
] | ---
language:
- ar
- en
license: mit
---
### xlm-r-large-arabic-toxic (toxic/hate speech classifier)
Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & ... |
622 | akilesh96/autonlp-mrcooper_text_classification-529614927 | [
"Animals",
"Compliment",
"Education",
"Health",
"Heavy Emotion",
"Joke",
"Love",
"Politics",
"Religion",
"Science",
"Self"
] | ---
tags: autonlp
language: en
widget:
- text: "Not Many People Know About The City 1200 Feet Below Detroit"
- text: "Bob accepts the challenge, and the next week they're standing in Saint Peters square. 'This isnt gonna work, he's never going to see me here when theres this much people. You stay here, I'll go talk to ... |
623 | akshara23/distilbert-base-uncased-finetuned-cola | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
metric:
name: Matthews Correlation
type: matthews_correlation
valu... |
624 | albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135 | [
"0",
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autonlp
language: bn
widget:
- text: "I love AutoNLP 🤗"
datasets:
- albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 1311135
## Validation Metrics
- Loss: 0.35616958141326904
- Accuracy: 0.8... |
625 | alecmullen/autonlp-group-classification-441411446 | [
"Beauty",
"Business/Finance",
"Faith",
"Fitness",
"Food",
"Gaming",
"Local",
"Marketplace",
"Memes",
"Music",
"None",
"Social",
"Sports",
"TV/Movies",
"Travel"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- alecmullen/autonlp-data-group-classification
co2_eq_emissions: 0.4362732160754736
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736
## Validat... |
627 | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
628 | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
629 | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
630 | ali2066/finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofrea... |
631 | ali2066/finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread an... |
632 | ali2066/finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofr... |
633 | ali2066/finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should prob... |
634 | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 | [
"NEGATIVE",
"POSITIVE"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51
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 remo... |
635 | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should prob... |
636 | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should prob... |
637 | ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
638 | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
639 | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
640 | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
641 | ali2066/finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread... |
642 | ali2066/finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and... |
643 | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
sh... |
644 | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
645 | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
646 | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
647 | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
648 | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
649 | ali2066/finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread... |
650 | ali2066/finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and... |
651 | ali2066/finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
652 | ali2066/finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
653 | ali2066/finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
654 | ali2066/finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
655 | ali2066/finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
656 | ali2066/finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
657 | ali2066/finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
658 | ali2066/finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
659 | ali2066/finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
660 | ali2066/finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
661 | ali2066/finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
662 | ali2066/finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
663 | ali2066/finetuned_sentence_itr3_2e-05_all_26_02_2022-04_14_37 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_2e-05_all_26_02_2022-04_14_37
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
664 | ali2066/finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
665 | ali2066/finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofre... |
666 | ali2066/finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
667 | ali2066/finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... |
668 | ali2066/finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
669 | ali2066/finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
670 | ali2066/finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
671 | ali2066/finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
672 | ali2066/finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... |
674 | allenai/longformer-scico | [
"child",
"coref",
"not related",
"parent"
] | ---
language: en
tags:
- longformer
- longformer-scico
license: apache-2.0
datasets:
- allenai/scico
inference: false
---
# Longformer for SciCo
This model is the `unified` model discussed in the paper [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021)](https://openreview.net/forum?i... |
675 | alperiox/autonlp-user-review-classification-536415182 | [
"CONTENT",
"INTERFACE",
"SUBSCRIPTION",
"USER_EXPERIENCE"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- alperiox/autonlp-data-user-review-classification
co2_eq_emissions: 1.268309634217171
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171
## Valid... |
676 | alvp/autonlp-alberti-stanza-names-34318169 | [
"cantar",
"chamberga",
"copla_arte_mayor",
"copla_arte_menor",
"copla_castellana",
"copla_mixta",
"copla_real",
"couplet",
"cuaderna_vía",
"cuarteta",
"cuarteto",
"cuarteto_lira",
"décima_antigua",
"endecha_real",
"espinela",
"estrofa_francisco_de_la_torre",
"estrofa_manriqueña",
"... | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- alvp/autonlp-data-alberti-stanza-names
co2_eq_emissions: 8.612473981829835
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 34318169
- CO2 Emissions (in grams): 8.612473981829835
## Validation Metr... |
677 | am4nsolanki/autonlp-text-hateful-memes-36789092 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- am4nsolanki/autonlp-data-text-hateful-memes
co2_eq_emissions: 1.4280361775467445
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445
## Validation Met... |
678 | amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 | [
"negative",
"neutral",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- amansolanki/autonlp-data-Tweet-Sentiment-Extraction
co2_eq_emissions: 3.651199395353127
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 20114061
- CO2 Emissions (in grams): 3.651199395353127
## Val... |
679 | amazon-sagemaker-community/xlm-roberta-en-ru-emoji-v2 | [
"☀",
"☹️",
"✨",
"❤",
"🇺🇸",
"🎄",
"💕",
"💙",
"💜",
"💢",
"💯",
"📷",
"📸",
"🔥",
"😁",
"😂",
"😉",
"😊",
"😍",
"😎",
"😔",
"😘",
"😜",
"😠",
"😡",
"😤",
"😩",
"😭",
"😳",
"🙃",
"🙄",
"🙈"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-en-ru-emoji-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-robe... |
681 | amirhossein1376/pft-clf-finetuned | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: apache-2.0
language: fa
widget:
- text: "امروز دربی دو تیم پرسپولیس و استقلال در ورزشگاه آزادی تهران برگزار میشود."
- text: "وزیر امور خارجه اردن تاکید کرد که همه کشورهای عربی خواهان روابط خوب با ایران هستند.
به گزارش ایسنا به نقل از شبکه فرانس ۲۴، ایمن الصفدی معاون نخستوزیر و وزیر امور خارجه اردن پس ... |
682 | andi611/distilbert-base-uncased-ner-agnews | [
"Business",
"Sci/Tech",
"Sports",
"World"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-agnews
results:
- dataset:
name: ag_news
type: ag_news
args: default
metric:
name: Accuracy
type: accuracy
value: 0.94736... |
683 | andi611/distilbert-base-uncased-qa-boolq | [
"False",
"True"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-boolq
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: boolq
type: boolq
args: default
metri... |
684 | anditya/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... |
687 | anel/autonlp-cml-412010597 | [
"misleading",
"news"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- anel/autonlp-data-cml
co2_eq_emissions: 10.411685187181709
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585... |
688 | anelnurkayeva/autonlp-covid-432211280 | [
"misleading",
"news"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- anelnurkayeva/autonlp-data-covid
co2_eq_emissions: 8.898145050355591
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss... |
689 | anindabitm/sagemaker-distilbert-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... |
714 | ans/vaccinating-covid-tweets | [
"false",
"misleading",
"true"
] | ---
language: en
license: apache-2.0
datasets:
- tweets
widget:
- text: "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
---
# Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
# Vacc... |
716 | citizenlab/distilbert-base-multilingual-cased-toxicity | [
"not_toxic",
"toxic"
] | ---
pipeline_type: "text-classification"
widget:
- text: "this is a lovely message"
example_title: "Example 1"
multi_class: false
- text: "you are an idiot and you and your family should go back to your country"
example_title: "Example 2"
multi_class: false
language:
- en
- nl
- fr
- pt
- it
- e... |
717 | arianpasquali/distilbert-base-uncased-finetuned-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... |
718 | citizenlab/twitter-xlm-roberta-base-sentiment-finetunned | [
"Negative",
"Neutral",
"Positive"
] | ---
pipeline_type: "text-classification"
widget:
- text: "this is a lovely message"
example_title: "Example 1"
multi_class: false
- text: "you are an idiot and you and your family should go back to your country"
example_title: "Example 2"
multi_class: false
language:
- en
- nl
- fr
- pt
- it
- e... |
719 | aristotletan/roberta-base-finetuned-sst2 | [
"analogous event",
"appointment of receiver",
"assets",
"breach of obligations",
"cessation of business",
"composition and arrangement",
"creditor control",
"cross default",
"disposal",
"event or events",
"insolvency",
"invalidity",
"jeopardy",
"judgement",
"legal proceedings",
"misrep... | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scim
metrics:
- accuracy
model_index:
- name: roberta-base-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: scim
type: scim
args: eod
metric:
name: Accuracy
... |
720 | arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2 | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-sentiment-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
ty... |
721 | arpanghoshal/EmoRoBERTa | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language: en
tags:
- text-classification
- tensorflow
- roberta
datasets:
- go_emotions
license: mit
---
Connect me on LinkedIn
- [linkedin.com/in/arpanghoshal](https://www.linkedin.com/in/arpanghoshal)
## What is GoEmotions
Dataset labelled 58000 Reddit comments with 28 emotions
- admiration, amusement, anger... |
722 | asalics/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... |
723 | ashish-chouhan/xlm-roberta-base-finetuned-marc | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc
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 remov... |
724 | ashraq/dv-electra-small-news-classification | [
"ރާއްޖެ",
"ކުޅިވަރު",
"ވިޔަފާރި",
"މުނިފޫހިފިލުވުން",
"ދީނީ",
"ދުނިޔެ",
"ސިޔާސީ",
"ޓެކްނޮލޮޖީ"
] | ---
widget:
- text: 'ގޫގަލް ޕިކްސަލް 6 ގެ ކެމެރާ، އޭއައި ގެ ޖާދޫއިން ފުރިފައި'
---
# The [ELECTRA-small](https://huggingface.co/ashraq/dv-electra-small) fine-tuned for news classification in Dhivehi |
728 | astarostap/autonlp-antisemitism-2-21194454 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "the jews have a lot of power"
datasets:
- astarostap/autonlp-data-antisemitism-2
co2_eq_emissions: 2.0686690092905224
---
# Description
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
Training data:
This model was trained on 4k ... |
731 | aubmindlab/aragpt2-mega-detector-long | [
"human-written",
"machine-generated"
] | ---
language: ar
widget:
- text: "وإذا كان هناك من لا يزال يعتقد أن لبنان هو سويسرا الشرق ، فهو مخطئ إلى حد بعيد . فلبنان ليس سويسرا ، ولا يمكن أن يكون كذلك . لقد عاش اللبنانيون في هذا البلد منذ ما يزيد عن ألف وخمسمئة عام ، أي منذ تأسيس الإمارة الشهابية التي أسسها الأمير فخر الدين المعني الثاني ( 1697 - 1742 )"
---
... |
732 | avichr/heBERT_sentiment_analysis | [
"neutral",
"negative",
"positive"
] | ## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>
HeBert was trained on three datasets:
1. A Hebrew version of OSCA... |
743 | ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa | [
"Positive",
"Neutral",
"Negative"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: s... |
744 | ayameRushia/indobert-base-uncased-finetuned-indonlu-smsa | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: indobert-base-uncased-finetuned-indonlu-smsa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
... |
745 | ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa | [
"POSITIVE",
"NEUTRAL",
"NEGATIVE"
] | ---
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: roberta-base-indonesian-1.5G-sentiment-analysis-smsa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metrics:
... |
746 | ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa | [
"POSITIVE",
"NEUTRAL",
"NEGATIVE"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: roberta-base-indonesian-sentiment-analysis-smsa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metr... |
747 | aychang/bert-base-cased-trec-coarse | [
"ABBR",
"DESC",
"ENTY",
"HUM",
"LOC",
"NUM"
] | ---
language:
- en
license: mit
tags:
- text-classification
datasets:
- trec
model-index:
- name: aychang/bert-base-cased-trec-coarse
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: trec
type: trec
config: default
split: test
metrics:
... |
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