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RAJESHNEMANI/Chatbot_AI | f73492d2bfec4da7479fbc8560dceed945caf072 | 2022-04-05T21:04:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | RAJESHNEMANI | null | RAJESHNEMANI/Chatbot_AI | 1 | null | transformers | 31,100 | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1LtVm-VHvDnfNy7SsbZAqhh49ikBwh1un?usp=sharing)
|
Danastos/newsqa_bert_el | 9e78e8ae708a231d826d667042974119290a4227 | 2022-04-06T03:31:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:Danastos/newsqa_el_custom",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Danastos | null | Danastos/newsqa_bert_el | 1 | null | transformers | 31,101 | ---
tags:
- generated_from_trainer
datasets:
- Danastos/newsqa_el_custom
model-index:
- name: newsqa_bert_el
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. -->
# newsqa_bert_el
This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on the Danastos/newsqa_el_custom 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.0.0
- Tokenizers 0.11.6
|
kumachan/another-dummy-model | 2637ebe8036233db2353954735969b16a070fb3b | 2022-04-06T00:54:02.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kumachan | null | kumachan/another-dummy-model | 1 | null | transformers | 31,102 | Entry not found |
Jiyang/EditModel | fb7bc218a447f170ed660b3bd006f664ef1b8c58 | 2022-04-06T03:22:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiyang | null | Jiyang/EditModel | 1 | null | transformers | 31,103 | Entry not found |
Kuray107/ls-timit-wsj0-100percent-supervised-aug | 49f5668e1e975413809d288224d3647abd5fe9df | 2022-04-06T14:26:52.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | Kuray107 | null | Kuray107/ls-timit-wsj0-100percent-supervised-aug | 1 | null | transformers | 31,104 | ---
tags:
- generated_from_trainer
model-index:
- name: ls-timit-wsj0-100percent-supervised-aug
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. -->
# ls-timit-wsj0-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0489
- Wer: 0.0275
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3491 | 4.57 | 1000 | 0.0470 | 0.0416 |
| 0.1088 | 9.13 | 2000 | 0.0582 | 0.0343 |
| 0.0702 | 13.7 | 3000 | 0.0471 | 0.0271 |
| 0.0532 | 18.26 | 4000 | 0.0489 | 0.0275 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
husnu/wav2vec2-large-xls-r-300m-turkish-colab | f93da5738fa37b6fbbb6d94757dc06c67e396476 | 2022-04-12T16:30:52.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | husnu | null | husnu/wav2vec2-large-xls-r-300m-turkish-colab | 1 | null | transformers | 31,105 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_6.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4380
- Wer: 0.3508
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8764 | 3.67 | 400 | 0.7239 | 0.7221 |
| 0.4526 | 7.34 | 800 | 0.5009 | 0.5345 |
| 0.2169 | 11.01 | 1200 | 0.4728 | 0.4693 |
| 0.1438 | 14.68 | 1600 | 0.4648 | 0.4669 |
| 0.1095 | 18.35 | 2000 | 0.4642 | 0.4094 |
| 0.0893 | 22.02 | 2400 | 0.4749 | 0.3879 |
| 0.0701 | 25.69 | 2800 | 0.4410 | 0.3665 |
| 0.056 | 29.36 | 3200 | 0.4380 | 0.3508 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Siddique/wav2vec2-large-xls-r-300m-turkish-colab | 51a60b28c78401df5b09926bd234c84669d21e9c | 2022-04-06T05:42:59.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Siddique | null | Siddique/wav2vec2-large-xls-r-300m-turkish-colab | 1 | null | transformers | 31,106 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- 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
|
chiba/distilbert-base-japanese-finetuned-squad | 6ea73e0dd5fc657ac7b5eace2fbc4ceaf7040ce1 | 2022-04-06T07:12:12.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | chiba | null | chiba/distilbert-base-japanese-finetuned-squad | 1 | null | transformers | 31,107 | Entry not found |
birgermoell/psst-fairseq-time-shift | 068e822daa3fda6609a32f85ac1d441fa91d48bb | 2022-04-06T08:50:40.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/psst-fairseq-time-shift | 1 | null | transformers | 31,108 | Entry not found |
edangx100/t5-small-finetuned-wikisql | d4227a7534353c2912e5e6abb6fb8814a47ff715 | 2022-04-06T10:23:39.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wiki_sql",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | edangx100 | null | edangx100/t5-small-finetuned-wikisql | 1 | null | transformers | 31,109 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wiki_sql
model-index:
- name: t5-small-finetuned-wikisql
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikisql
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wiki_sql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Rouge2 Precision: 0.8187
- Rouge2 Recall: 0.7269
- Rouge2 Fmeasure: 0.7629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.1952 | 1.0 | 4049 | 0.1567 | 0.7948 | 0.7057 | 0.7406 |
| 0.167 | 2.0 | 8098 | 0.1382 | 0.8092 | 0.7171 | 0.7534 |
| 0.1517 | 3.0 | 12147 | 0.1296 | 0.8145 | 0.7228 | 0.7589 |
| 0.1433 | 4.0 | 16196 | 0.1260 | 0.8175 | 0.7254 | 0.7617 |
| 0.1414 | 5.0 | 20245 | 0.1246 | 0.8187 | 0.7269 | 0.7629 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
xxr/bert-base-chinese-complaint-128 | f2e5bb39d5ab44dd12014fc7a49169613805060f | 2022-04-06T11:06:31.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | xxr | null | xxr/bert-base-chinese-complaint-128 | 1 | null | transformers | 31,110 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-chinese-complaint-128
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-complaint-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: 1.3004
## 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3735 | 1.0 | 1250 | 2.4628 |
| 2.2412 | 2.0 | 2500 | 2.0378 |
| 1.9251 | 3.0 | 3750 | 1.8368 |
| 1.7407 | 4.0 | 5000 | 1.6972 |
| 1.6137 | 5.0 | 6250 | 1.5937 |
| 1.5365 | 6.0 | 7500 | 1.5315 |
| 1.4662 | 7.0 | 8750 | 1.4921 |
| 1.3985 | 8.0 | 10000 | 1.4517 |
| 1.3509 | 9.0 | 11250 | 1.4308 |
| 1.3047 | 10.0 | 12500 | 1.3906 |
| 1.2745 | 11.0 | 13750 | 1.3467 |
| 1.2377 | 12.0 | 15000 | 1.3306 |
| 1.2139 | 13.0 | 16250 | 1.3205 |
| 1.2027 | 14.0 | 17500 | 1.3098 |
| 1.1722 | 15.0 | 18750 | 1.2845 |
| 1.1697 | 16.0 | 20000 | 1.3004 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
NbAiLab/nb-mt5-base | ccec5c6f77b685377b297905f3d7a0618a8a7a4f | 2022-04-06T13:53:23.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | NbAiLab | null | NbAiLab/nb-mt5-base | 1 | null | transformers | 31,111 | ---
license: apache-2.0
---
|
gary109/wav2vec2-base-timit-demo-colab | 2674f41ac7b7605c9191786d989c36c8855bad35 | 2022-04-12T07:51:46.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | gary109 | null | gary109/wav2vec2-base-timit-demo-colab | 1 | null | transformers | 31,112 | ---
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: 0.4707
- Wer: 0.3411
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4575 | 4.0 | 500 | 1.3367 | 0.9724 |
| 0.594 | 8.0 | 1000 | 0.4365 | 0.4390 |
| 0.2195 | 12.0 | 1500 | 0.4438 | 0.3955 |
| 0.1246 | 16.0 | 2000 | 0.4741 | 0.3707 |
| 0.082 | 20.0 | 2500 | 0.4766 | 0.3564 |
| 0.0605 | 24.0 | 3000 | 0.4657 | 0.3475 |
| 0.0458 | 28.0 | 3500 | 0.4707 | 0.3411 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
binay1999/distilbert-finetuned-ner | 8a13cfb03b554f18afb73dd1e7c4a186fcec7bb9 | 2022-04-06T16:19:17.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | binay1999 | null | binay1999/distilbert-finetuned-ner | 1 | null | transformers | 31,113 | Entry not found |
KrishnaAgarwal16/607-project-adversarial | 20258d0dab0f3e39b54043e3f6f5f27753d2504b | 2022-04-06T18:43:49.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | KrishnaAgarwal16 | null | KrishnaAgarwal16/607-project-adversarial | 1 | null | transformers | 31,114 | Model trained for 1 epoch on 1000 examples from the `adversarial_qa` dataset
|
ucl-snlp-group-11/byt5-base-cryptic-crosswords | b07bf2dc20d130ba731094cc712792cf8c4636c9 | 2022-04-06T20:58:39.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ucl-snlp-group-11 | null | ucl-snlp-group-11/byt5-base-cryptic-crosswords | 1 | null | transformers | 31,115 | Entry not found |
ucl-snlp-group-11/t5-large-cryptic-crosswords | ba3431170ddc6fbbbc96b14d17a8b5e89e60fad2 | 2022-04-06T21:08:27.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ucl-snlp-group-11 | null | ucl-snlp-group-11/t5-large-cryptic-crosswords | 1 | null | transformers | 31,116 | Entry not found |
Splend1dchan/t5lephone200000-small-squad1024 | 8003e23f5d0f891390dca0d2a8c83f975fa46b5b | 2022-04-07T06:47:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Splend1dchan | null | Splend1dchan/t5lephone200000-small-squad1024 | 1 | null | transformers | 31,117 | Entry not found |
gary109/wav2vec2-base-MIR_ST500-demo-colab | ad94e9163c9950b5511229d3410170ea86860f79 | 2022-04-08T07:48:19.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | gary109 | null | gary109/wav2vec2-base-MIR_ST500-demo-colab | 1 | null | transformers | 31,118 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-MIR_ST500-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-MIR_ST500-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: 2.7360
- Wer: 0.9837
## 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: 16
- 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: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 101.0917 | 16.67 | 100 | 18.8979 | 0.8208 |
| 15.5054 | 33.33 | 200 | 10.9184 | 0.8208 |
| 10.1879 | 50.0 | 300 | 7.6480 | 0.8208 |
| 6.777 | 66.67 | 400 | 3.5386 | 1.0 |
| 3.0546 | 83.33 | 500 | 2.8794 | 1.0 |
| 2.8661 | 100.0 | 600 | 2.8405 | 1.0 |
| 2.847 | 116.67 | 700 | 2.8554 | 1.0 |
| 2.7661 | 133.33 | 800 | 2.6343 | 1.0 |
| 2.3474 | 150.0 | 900 | 2.7464 | 1.0 |
| 2.2464 | 166.67 | 1000 | 2.3565 | 1.0 |
| 2.207 | 183.33 | 1100 | 2.8854 | 1.0 |
| 2.3138 | 200.0 | 1200 | 2.5868 | 1.0 |
| 2.259 | 216.67 | 1300 | 2.6530 | 1.0 |
| 2.1667 | 233.33 | 1400 | 2.4921 | 1.0 |
| 2.1268 | 250.0 | 1500 | 2.5435 | 1.0 |
| 2.1089 | 266.67 | 1600 | 2.5444 | 1.0 |
| 2.0845 | 283.33 | 1700 | 2.6796 | 1.0 |
| 2.0672 | 300.0 | 1800 | 2.5824 | 1.0 |
| 2.055 | 316.67 | 1900 | 2.4631 | 1.0 |
| 2.0317 | 333.33 | 2000 | 2.5751 | 1.0 |
| 2.0141 | 350.0 | 2100 | 2.5627 | 1.0 |
| 1.9914 | 366.67 | 2200 | 2.6132 | 1.0 |
| 1.9489 | 383.33 | 2300 | 2.7527 | 1.0 |
| 1.9146 | 400.0 | 2400 | 2.6121 | 0.9935 |
| 1.893 | 416.67 | 2500 | 2.7110 | 0.9902 |
| 1.845 | 433.33 | 2600 | 2.7410 | 0.9967 |
| 1.8095 | 450.0 | 2700 | 2.7013 | 0.9935 |
| 1.7708 | 466.67 | 2800 | 2.7719 | 0.9935 |
| 1.7224 | 483.33 | 2900 | 2.7740 | 0.9837 |
| 1.6961 | 500.0 | 3000 | 2.7360 | 0.9837 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
deepspeechvision/wav2vec2hindiasr_thefinal | 50e13d217ecf125b60e3f27fe5aca269d4c399bf | 2022-04-07T06:13:23.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | deepspeechvision | null | deepspeechvision/wav2vec2hindiasr_thefinal | 1 | null | transformers | 31,119 | Entry not found |
tau/false_large_rouge_paraNone_sentNone_span0_5_1024_0.3_epoch1 | 84bcd2a68a5120f64b655a1de4007e621388a8cd | 2022-04-07T05:25:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/false_large_rouge_paraNone_sentNone_span0_5_1024_0.3_epoch1 | 1 | null | transformers | 31,120 | Entry not found |
tau/false_large_random_paraNone_sent0_spanNone_5_1024_0.3_epoch1 | d4726bc3db2fffebab965e8519df75aa5811906d | 2022-04-07T05:38:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/false_large_random_paraNone_sent0_spanNone_5_1024_0.3_epoch1 | 1 | null | transformers | 31,121 | Entry not found |
jeremykke/albert-base-v2-finetuned-swag | fe78c6d9cae555bb32e2bc89234ca6693ce069c5 | 2022-04-07T08:03:49.000Z | [
"pytorch",
"tensorboard",
"albert",
"multiple-choice",
"transformers"
] | multiple-choice | false | jeremykke | null | jeremykke/albert-base-v2-finetuned-swag | 1 | null | transformers | 31,122 | Entry not found |
shunxing1234/GLM | 791fac9f109297959f8cdeefd23ba1725152b2fd | 2022-04-29T07:34:50.000Z | [
"pytorch",
"transformers"
] | null | false | shunxing1234 | null | shunxing1234/GLM | 1 | null | transformers | 31,123 | Entry not found |
guillaumegg/wav2vec2-base-timit-demo | 6d56ede107abb5c8d566a775d566ddae87dd233f | 2022-04-07T07:18:24.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | guillaumegg | null | guillaumegg/wav2vec2-base-timit-demo | 1 | null | transformers | 31,124 | Entry not found |
huggingtweets/enginemode11-phoenixstk19-scarbstech | 22a84927c46566fde3551f87f67828bbc53905eb | 2022-04-07T08:18:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/enginemode11-phoenixstk19-scarbstech | 1 | null | transformers | 31,125 | ---
language: en
thumbnail: http://www.huggingtweets.com/enginemode11-phoenixstk19-scarbstech/1649319522056/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/456005573/scarbs_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/1507753713288589318/5wpnOWkx_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/1509794479691157514/u9JrmBtO_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">Craig Scarborough & Alpine F1 Team technical updates & EngineMode11</div>
<div style="text-align: center; font-size: 14px;">@enginemode11-phoenixstk19-scarbstech</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 Craig Scarborough & Alpine F1 Team technical updates & EngineMode11.
| Data | Craig Scarborough | Alpine F1 Team technical updates | EngineMode11 |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 2389 | 1555 |
| Retweets | 387 | 39 | 65 |
| Short tweets | 646 | 334 | 288 |
| Tweets kept | 2217 | 2016 | 1202 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vojhtxh0/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 @enginemode11-phoenixstk19-scarbstech's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28cxey7a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28cxey7a/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/enginemode11-phoenixstk19-scarbstech')
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)
|
Brokette/wav2vec2-base-timit-test3 | a94f63e89723b967d18f7f402d4fbc3e4915e4e1 | 2022-04-07T09:47:29.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Brokette | null | Brokette/wav2vec2-base-timit-test3 | 1 | null | transformers | 31,126 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-test3
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-test3
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: 5
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
|
Brokette/wav2vec2-base-timit-test5 | c532434d8470c3cf076b6e5276bb5a356dd0f56e | 2022-04-07T10:29:10.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Brokette | null | Brokette/wav2vec2-base-timit-test5 | 1 | null | transformers | 31,127 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-test5
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-test5
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: 5
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
|
notexist/tttw | 7a90edb23a96e751f74fc5c20418f444b6247d3d | 2022-04-07T10:21:27.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | notexist | null | notexist/tttw | 1 | null | transformers | 31,128 | Entry not found |
swagat-panda/multilingual-pos-tagger-language-detection-indian-context-muril | c283acca0ace877f95e650e92e21e2852e77065f | 2022-04-07T14:55:12.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | swagat-panda | null | swagat-panda/multilingual-pos-tagger-language-detection-indian-context-muril | 1 | null | transformers | 31,129 | Entry not found |
vocab-transformers/distilbert-word2vec_256k-MLM_250k | 136548ac0a4e1b1436c0dec90609fce938276798 | 2022-04-07T12:46:40.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vocab-transformers | null | vocab-transformers/distilbert-word2vec_256k-MLM_250k | 1 | null | transformers | 31,130 | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 250k steps (batch size 64). The token embeddings were NOT updated.
|
vocab-transformers/distilbert-word2vec_256k-MLM_1M | 7bbf38d7e54685bc016a289b4238de7dfb8e2d95 | 2022-04-07T13:00:01.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vocab-transformers | null | vocab-transformers/distilbert-word2vec_256k-MLM_1M | 1 | null | transformers | 31,131 | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1M steps (batch size 64). The token embeddings were NOT updated.
|
BigSalmon/MediumInformalToFormalLincoln2 | b7556b742280633ed9c767d4654adff4ada20915 | 2022-04-07T17:15:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/MediumInformalToFormalLincoln2 | 1 | null | transformers | 31,132 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln2")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln2")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln35")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
(makes one sentence, two sentences) (probably will not work all that well)
```
entry: an upsurge in public interest in astronomy accompanied nasa's stellar picture of a starry night.
extended: public interest in astronomy soared. not coincidentally, this was concurrent with nasa's release of a phenomenal image of a starry night.
***
entry:
```
(makes two sentences, one sentence) (probably will not work all that well)
```
initial: phone books used to be everywhere. they have been replaced by the internet.
combined: once ubiquitous, phone books have been supplanted by the internet.
***
initial:
```
```
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
Keywords to sentences or sentence. |
ali-issa/wav2vec2-Arabizi | c2304f7109c5e05cdb6f1f12a3407f42e7c4e2c6 | 2022-04-07T18:51:18.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ali-issa | null | ali-issa/wav2vec2-Arabizi | 1 | null | transformers | 31,133 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-Arabizi
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-Arabizi
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: 1.6433
- Wer: 0.8331
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7105 | 10.0 | 200 | 2.9462 | 1.0 |
| 1.9532 | 20.0 | 400 | 1.4871 | 0.8887 |
| 0.3542 | 30.0 | 600 | 1.6433 | 0.8331 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
lucypallent/distilbert-base-uncased-finetuned-imdb | ed86ef0ffb0ad60b8369dd2763b97d7553aec7a4 | 2022-04-07T18:27:42.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | lucypallent | null | lucypallent/distilbert-base-uncased-finetuned-imdb | 1 | null | transformers | 31,134 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4718
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.707 | 1.0 | 157 | 2.4883 |
| 2.572 | 2.0 | 314 | 2.4240 |
| 2.5377 | 3.0 | 471 | 2.4355 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
cj-mills/xlm-roberta-base-finetuned-panx-de | 1ebdc3c9051a980588be5a495ad96896f330932c | 2022-04-08T01:29:12.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | cj-mills | null | cj-mills/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,135 | ---
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: 0.8575809199318569
---
<!-- 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: 0.1319
- F1: 0.8576
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3264 | 1.0 | 197 | 0.1623 | 0.8139 |
| 0.136 | 2.0 | 394 | 0.1331 | 0.8451 |
| 0.096 | 3.0 | 591 | 0.1319 | 0.8576 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Bistolero/german_all | 3e20912fd78f18ac878c372af5bec898ea71a02f | 2022-04-07T21:56:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Bistolero | null | Bistolero/german_all | 1 | null | transformers | 31,136 | Entry not found |
srmukundb/distilbert-base-uncased-finetuned-squad | 2d379798cb3754ed0b9b9ff5ae913bce5d9afd98 | 2022-04-08T12:08:02.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | srmukundb | null | srmukundb/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 31,137 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2182 | 1.0 | 8235 | 1.2318 |
| 0.9451 | 2.0 | 16470 | 1.2693 |
| 0.7554 | 3.0 | 24705 | 1.4104 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
cj-mills/xlm-roberta-base-finetuned-panx-de-fr | 1c43332ab7b11485f33f1b022bce0388311963ab | 2022-04-08T01:41:15.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | cj-mills | null | cj-mills/xlm-roberta-base-finetuned-panx-de-fr | 1 | null | transformers | 31,138 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1580
- F1: 0.8547
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3718 | 1.0 | 269 | 0.1761 | 0.8223 |
| 0.1535 | 2.0 | 538 | 0.1608 | 0.8404 |
| 0.1074 | 3.0 | 807 | 0.1580 | 0.8547 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
cj-mills/xlm-roberta-base-finetuned-panx-en | 100d385d3ee5acb6ced4732d37d5611f0040d3c2 | 2022-04-08T02:04:02.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | cj-mills | null | cj-mills/xlm-roberta-base-finetuned-panx-en | 1 | null | transformers | 31,139 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.5793693212185996
---
<!-- 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-en
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: 0.5084
- F1: 0.5794
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7119 | 1.0 | 19 | 1.0009 | 0.2266 |
| 0.891 | 2.0 | 38 | 0.6405 | 0.5281 |
| 0.6023 | 3.0 | 57 | 0.5084 | 0.5794 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
cj-mills/xlm-roberta-base-finetuned-panx-all | 3e2e6513ddfcdf6caa7fa00f0c68cdfdba0be13e | 2022-04-08T02:13:57.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | cj-mills | null | cj-mills/xlm-roberta-base-finetuned-panx-all | 1 | null | transformers | 31,140 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1674
- F1: 0.8477
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3701 | 1.0 | 313 | 0.2000 | 0.8054 |
| 0.1629 | 2.0 | 626 | 0.1680 | 0.8378 |
| 0.1156 | 3.0 | 939 | 0.1674 | 0.8477 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
MrYiRen/DialoGPT-small-ZC | 52bdf0ea19d337236e4a4625a18afc554f891c40 | 2022-04-08T02:35:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | MrYiRen | null | MrYiRen/DialoGPT-small-ZC | 1 | null | transformers | 31,141 | ---
tags:
- conversational
---
# Harry Potter2 DialoGPT Model |
Pisit/wave2vec2-front | 89544fb15a938a894c903c1f3230debe2d1a120a | 2022-04-08T06:04:26.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Pisit | null | Pisit/wave2vec2-front | 1 | null | transformers | 31,142 | Entry not found |
guzelgun/dummy-model | b208975d9899e19a6b52874c74dabdc6f5e4715f | 2022-04-08T05:47:40.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | guzelgun | null | guzelgun/dummy-model | 1 | null | transformers | 31,143 | Entry not found |
chiba/electra-small-japanese-generator_test | 9cc3505df203c44d50e4b140725b10c9ce05226c | 2022-04-12T04:55:24.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | chiba | null | chiba/electra-small-japanese-generator_test | 1 | null | transformers | 31,144 | Entry not found |
Falia/wav2vec2-xlsr-300m-vox_mg | 7abba62506d6c21da994343c507ea88ef2f92e90 | 2022-06-11T12:31:13.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Falia | null | Falia/wav2vec2-xlsr-300m-vox_mg | 1 | null | transformers | 31,145 | Entry not found |
kiana/distilbert-base-uncased-finetuned-squad | 03357077abb5110045e1e8c53c3cc5eaf607e166 | 2022-04-23T11:54:15.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | kiana | null | kiana/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 31,146 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2545 | 1.0 | 8235 | 1.2770 |
| 0.9861 | 2.0 | 16470 | 1.3071 |
| 0.8098 | 3.0 | 24705 | 1.4088 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
edwardjross/xlm-roberta-base-finetuned-recipe-ar | f72a9b9b1bbf8805e4e32bb496bf131ed81202af | 2022-04-09T02:14:30.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | edwardjross | null | edwardjross/xlm-roberta-base-finetuned-recipe-ar | 1 | null | transformers | 31,147 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-recipe-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-recipe-ar
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0529
- F1: 0.9856
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4605 | 1.0 | 74 | 0.1084 | 0.9609 |
| 0.1105 | 2.0 | 148 | 0.0563 | 0.9809 |
| 0.0696 | 3.0 | 222 | 0.0500 | 0.9851 |
| 0.0512 | 4.0 | 296 | 0.0529 | 0.9856 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
edwardjross/xlm-roberta-base-finetuned-recipe-gk | c55bb323f8ee5a74d00746cf62608ce17a18d5f8 | 2022-04-09T02:23:00.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | edwardjross | null | edwardjross/xlm-roberta-base-finetuned-recipe-gk | 1 | null | transformers | 31,148 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-recipe-gk
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-recipe-gk
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- F1: 0.9536
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.292 | 1.0 | 258 | 0.1525 | 0.9565 |
| 0.1231 | 2.0 | 516 | 0.1348 | 0.9619 |
| 0.0787 | 3.0 | 774 | 0.1408 | 0.9607 |
| 0.0655 | 4.0 | 1032 | 0.1505 | 0.9536 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
johnpaulbin/skript-1m-gpt-neo350m | a55020c5ea0277f617f70334023f82ac279a266b | 2022-04-08T14:21:41.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | johnpaulbin | null | johnpaulbin/skript-1m-gpt-neo350m | 1 | null | transformers | 31,149 | Entry not found |
AvengingPrime/Change-My-View-Model-1 | 4986625c803a87733af9c0badacd9b32f65fd317 | 2022-04-08T16:40:51.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | AvengingPrime | null | AvengingPrime/Change-My-View-Model-1 | 1 | null | transformers | 31,150 | Entry not found |
bmichele/poetry-generation-nextline-mbart-gut-en-single | ae22a9b7d6dffbe689b7adfeba971f079cd5622e | 2022-04-08T19:13:43.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | bmichele | null | bmichele/poetry-generation-nextline-mbart-gut-en-single | 1 | null | transformers | 31,151 | # poetry-generation-nextline-mbart-gut-en-single
* `nextline`: generates a poem line from previous line(s)
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `gut`: trained on Project Gutenberg data
* `en`: English language
* `single`: uses only last poem line as input for generation |
Danastos/squad_bert_el | a583b29f3ccbec575c54195166932f9f8ef2ece3 | 2022-04-09T02:25:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:Danastos/squad_el_custom",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Danastos | null | Danastos/squad_bert_el | 1 | null | transformers | 31,152 | ---
tags:
- generated_from_trainer
datasets:
- Danastos/squad_el_custom
model-index:
- name: squad_bert_el
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. -->
# squad_bert_el
This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on the Danastos/squad_el_custom dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Wizounovziki/t5-small-finetuned-xsum | b85212c03bbdb72dc6d30a55ca7f23659c9b0335 | 2022-04-09T09:24:06.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Wizounovziki | null | Wizounovziki/t5-small-finetuned-xsum | 1 | null | transformers | 31,153 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 13 | 2.9185 | 20.6059 | 0.7473 | 20.5288 | 20.5999 | 18.87 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Wizounovziki/t5-small-ipad-sum | a958114c4068f04a4b8b9875c3e96da382b26a0d | 2022-04-09T10:44:23.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Wizounovziki | null | Wizounovziki/t5-small-ipad-sum | 1 | null | transformers | 31,154 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-ipad-sum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-ipad-sum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3632
- Rouge1: 90.6
- Rouge2: 29.6667
- Rougel: 90.8667
- Rougelsum: 90.6667
- Gen Len: 4.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 13 | 2.7713 | 20.7123 | 0.7601 | 20.6467 | 20.6954 | 18.85 |
| No log | 2.0 | 26 | 1.9722 | 23.2307 | 1.3571 | 23.263 | 23.2952 | 18.25 |
| No log | 3.0 | 39 | 1.2886 | 46.3724 | 8.0862 | 46.5163 | 46.4406 | 13.7 |
| No log | 4.0 | 52 | 0.8267 | 78.4825 | 14.1975 | 78.6464 | 78.3548 | 7.38 |
| No log | 5.0 | 65 | 0.6405 | 81.8222 | 15.7532 | 81.8856 | 81.88 | 6.3 |
| No log | 6.0 | 78 | 0.5210 | 83.2111 | 17.5 | 83.2931 | 83.1583 | 5.46 |
| No log | 7.0 | 91 | 0.4425 | 87.154 | 21.7917 | 87.2008 | 87.169 | 4.99 |
| No log | 8.0 | 104 | 0.3974 | 89.7619 | 27.6667 | 89.8571 | 89.8817 | 4.85 |
| No log | 9.0 | 117 | 0.3735 | 90.4 | 29.6667 | 90.5706 | 90.4635 | 4.87 |
| No log | 10.0 | 130 | 0.3632 | 90.6 | 29.6667 | 90.8667 | 90.6667 | 4.79 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
bhoppenstedt/js-fakes-4bars | 4e04484fe39e6bf890dc52efd081670dbd20e430 | 2022-04-09T12:36:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | bhoppenstedt | null | bhoppenstedt/js-fakes-4bars | 1 | null | transformers | 31,155 | Entry not found |
Bogula/js-fakes-4bars | 93205512bf3a3fc45059f5e71f06cd40c07ec4f9 | 2022-04-09T12:39:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Bogula | null | Bogula/js-fakes-4bars | 1 | null | transformers | 31,156 | Entry not found |
DarrellTimothy/DialoGPT-small-harrypotter | 89cf5831d8d40ea09e0902227fc0276b19f631ac | 2022-04-09T12:50:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | DarrellTimothy | null | DarrellTimothy/DialoGPT-small-harrypotter | 1 | null | transformers | 31,157 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
tau/tavbert-tr | e5cc769220f4bd9e2bd353839c157a0355cb1fd7 | 2022-04-09T12:55:55.000Z | [
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"transformers",
"language model",
"autotrain_compatible"
] | fill-mask | false | tau | null | tau/tavbert-tr | 1 | 1 | transformers | 31,158 | ---
language: tr
tags:
- roberta
- language model
datasets:
- oscar
---
# TavBERT base model
A Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
```python
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-tr")
tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-tr")
def mask_sentence(sent, span_len=5):
start_pos = np.random.randint(0, len(sent) - span_len)
masked_sent = sent[:start_pos] + '[MASK]' * span_len + sent[start_pos + span_len:]
print("Masked sentence:", masked_sent)
output = model(**tokenizer.encode_plus(masked_sent,
return_tensors='pt'))['logits'][0][1:-1]
preds = [int(x) for x in torch.argmax(torch.softmax(output, axis=1), axis=1)[start_pos:start_pos + span_len]]
pred_sent = sent[:start_pos] + ''.join(tokenizer.convert_ids_to_tokens(preds)) + sent[start_pos + span_len:]
print("Model's prediction:", pred_sent)
```
## Training data
OSCAR (Ortiz, 2019) Turkish section (27 GB text, 77 million sentences).
|
tau/tavbert-ar | 96b589cd0539801fb7680d11837a65deefc9b0e8 | 2022-04-09T13:27:47.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ar",
"dataset:oscar",
"transformers",
"language model",
"autotrain_compatible"
] | fill-mask | false | tau | null | tau/tavbert-ar | 1 | null | transformers | 31,159 | ---
language: ar
tags:
- roberta
- language model
datasets:
- oscar
---
# TavBERT base model
An Arabic BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
```python
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-ar")
tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-ar")
def mask_sentence(sent, span_len=5):
start_pos = np.random.randint(0, len(sent) - span_len)
masked_sent = sent[:start_pos] + '[MASK]' * span_len + sent[start_pos + span_len:]
print("Masked sentence:", masked_sent)
output = model(**tokenizer.encode_plus(masked_sent,
return_tensors='pt'))['logits'][0][1:-1]
preds = [int(x) for x in torch.argmax(torch.softmax(output, axis=1), axis=1)[start_pos:start_pos + span_len]]
pred_sent = sent[:start_pos] + ''.join(tokenizer.convert_ids_to_tokens(preds)) + sent[start_pos + span_len:]
print("Model's prediction:", pred_sent)
```
## Training data
OSCAR (Ortiz, 2019) Arabic section (32 GB text, 67 million sentences).
|
masakhane/afrimbart_bam_fr_news | 30c034a9bdf8fcb161f17f387b426b024dde4f99 | 2022-04-11T13:18:47.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimbart_bam_fr_news | 1 | null | transformers | 31,160 | ---
license: afl-3.0
---
|
masakhane/afrimbart_fr_bam_news | 5d7cc3646cb00468a56e331a11ad8b577addaf0e | 2022-04-11T13:20:11.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimbart_fr_bam_news | 1 | null | transformers | 31,161 | ---
license: afl-3.0
---
|
masakhane/afrimt5_bam_fr_news | 71ed3412f7c5245ba308a9faee38fb6d9257a48f | 2022-04-11T13:27:50.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_bam_fr_news | 1 | null | transformers | 31,162 | ---
license: afl-3.0
---
|
masakhane/afrimt5_fr_bam_news | 237547917e1534d1d76b8b301bce30416d8dfd66 | 2022-04-11T13:27:55.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_fr_bam_news | 1 | null | transformers | 31,163 | ---
license: afl-3.0
---
|
masakhane/afribyt5_fr_bam_news | 2d686120482b50aa44623bcfb51ceddd610ff057 | 2022-04-11T13:34:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afribyt5_fr_bam_news | 1 | null | transformers | 31,164 | ---
license: afl-3.0
---
|
masakhane/byt5_fr_bam_news | 7e9db3c1dff8a40ba10e8187bb645bf4485b9449 | 2022-04-11T13:41:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_fr_bam_news | 1 | null | transformers | 31,165 | ---
license: afl-3.0
---
|
gemasphi/laprador-query-encoder | a6f20bd4d1426059939be467bd9520d4288d0201 | 2022-04-09T18:28:12.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | gemasphi | null | gemasphi/laprador-query-encoder | 1 | null | sentence-transformers | 31,166 | ---
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})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel
(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 --> |
masakhane/m2m100_418M_bam_fr_rel_news_ft | bc982be439d141352a317b7e672df752a61f4486 | 2022-04-11T15:12:35.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_bam_fr_rel_news_ft | 1 | null | transformers | 31,167 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_news | 96eca872cd0d4750fbe83908c4c8f27dd5197d73 | 2022-04-11T14:31:00.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bam_news | 1 | null | transformers | 31,168 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_news | 28666b71f8480b1819acdfdd9ecbbce7992407b2 | 2022-04-11T14:30:55.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_bam_fr_news | 1 | null | transformers | 31,169 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel | 56249c7552b4077af74741bb810f042cf2294179 | 2022-04-11T15:21:09.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bam_rel | 1 | null | transformers | 31,170 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_rel_ft | e57c20fb6673fa66327d194ab1d16f41c6948452 | 2022-04-11T16:34:16.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_bam_fr_rel_ft | 1 | null | transformers | 31,171 | ---
license: afl-3.0
---
|
masakhane/mbart50_fr_bam_news | 97ed4b3c7233b63a53f9c50f75ac1e28b179bc81 | 2022-04-11T14:22:35.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mbart50_fr_bam_news | 1 | null | transformers | 31,172 | ---
license: afl-3.0
---
|
masakhane/mt5_bam_fr_news | 35329817cb58a179ac855c72d0a81afcb29a92f1 | 2022-04-11T13:53:53.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_bam_fr_news | 1 | null | transformers | 31,173 | ---
license: afl-3.0
---
|
Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 | 2b5b759528e7769d1f5c42aa4b16f0dd764bc746 | 2022-04-10T23:42:14.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wikihow",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Chikashi | null | Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 | 1 | null | transformers | 31,174 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 26.1071
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b4_lr3e-5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4351
- Rouge1: 26.1071
- Rouge2: 9.3627
- Rougel: 22.0825
- Rougelsum: 25.4514
- Gen Len: 18.474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9216 | 0.13 | 5000 | 2.6385 | 23.8039 | 7.8863 | 20.0109 | 23.0802 | 18.3481 |
| 2.8158 | 0.25 | 10000 | 2.5884 | 24.2567 | 8.2003 | 20.438 | 23.5325 | 18.3833 |
| 2.7743 | 0.38 | 15000 | 2.5623 | 24.8471 | 8.3768 | 20.8711 | 24.1114 | 18.2901 |
| 2.7598 | 0.51 | 20000 | 2.5368 | 25.1566 | 8.6721 | 21.1896 | 24.4558 | 18.3561 |
| 2.7192 | 0.64 | 25000 | 2.5220 | 25.3477 | 8.8106 | 21.3799 | 24.6742 | 18.3108 |
| 2.7207 | 0.76 | 30000 | 2.5114 | 25.5912 | 8.998 | 21.5508 | 24.9344 | 18.3445 |
| 2.7041 | 0.89 | 35000 | 2.4993 | 25.457 | 8.8644 | 21.4516 | 24.7965 | 18.4354 |
| 2.687 | 1.02 | 40000 | 2.4879 | 25.5886 | 8.9766 | 21.6794 | 24.9512 | 18.4035 |
| 2.6652 | 1.14 | 45000 | 2.4848 | 25.7367 | 9.078 | 21.7096 | 25.0924 | 18.4328 |
| 2.6536 | 1.27 | 50000 | 2.4761 | 25.7368 | 9.1609 | 21.729 | 25.0866 | 18.3117 |
| 2.6589 | 1.4 | 55000 | 2.4702 | 25.7738 | 9.1413 | 21.7492 | 25.114 | 18.4862 |
| 2.6384 | 1.53 | 60000 | 2.4620 | 25.7433 | 9.1356 | 21.8198 | 25.0896 | 18.489 |
| 2.6337 | 1.65 | 65000 | 2.4595 | 26.0919 | 9.2605 | 21.9447 | 25.4065 | 18.4083 |
| 2.6375 | 1.78 | 70000 | 2.4557 | 26.0912 | 9.3469 | 22.0182 | 25.4428 | 18.4133 |
| 2.6441 | 1.91 | 75000 | 2.4502 | 26.1366 | 9.3143 | 22.058 | 25.4673 | 18.4972 |
| 2.6276 | 2.03 | 80000 | 2.4478 | 25.9929 | 9.2464 | 21.9271 | 25.3263 | 18.469 |
| 2.6062 | 2.16 | 85000 | 2.4467 | 26.0465 | 9.3166 | 22.0342 | 25.3998 | 18.3777 |
| 2.6126 | 2.29 | 90000 | 2.4407 | 26.1953 | 9.3848 | 22.1148 | 25.5161 | 18.467 |
| 2.6182 | 2.42 | 95000 | 2.4397 | 26.1331 | 9.3626 | 22.1076 | 25.4627 | 18.4413 |
| 2.6041 | 2.54 | 100000 | 2.4375 | 26.1301 | 9.3567 | 22.0869 | 25.465 | 18.4929 |
| 2.5996 | 2.67 | 105000 | 2.4367 | 26.0956 | 9.3314 | 22.063 | 25.4242 | 18.5074 |
| 2.6144 | 2.8 | 110000 | 2.4355 | 26.1764 | 9.4157 | 22.1231 | 25.5175 | 18.4729 |
| 2.608 | 2.93 | 115000 | 2.4351 | 26.1071 | 9.3627 | 22.0825 | 25.4514 | 18.474 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
cbgbcbcg/DialoGPT-small-joshua | d09854cee8296ee7800f7c93822b796deb5bc30e | 2022-04-10T01:42:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | cbgbcbcg | null | cbgbcbcg/DialoGPT-small-joshua | 1 | null | transformers | 31,175 | Test |
ivalig94/Robertweet-large | 31161fe9bc995e9a399b5e565e5baf6c65f3ee35 | 2022-05-05T19:47:05.000Z | [
"pytorch",
"roberta",
"transformers",
"license:afl-3.0"
] | null | false | ivalig94 | null | ivalig94/Robertweet-large | 1 | null | transformers | 31,176 | ---
license: afl-3.0
---
from transformers import AutoTokenizer, ROBERTAClassifier
tokenizer = AutoTokenizer.from_pretrained("ivalig94/Robertweet-large")
model = ROBERTAClassifier.from_pretrained("ivalig94/Robertweet-large") |
Wizounovziki/t5-base-devices-sum-ver2 | d2408c2341b8ba37b46f2804efb498570795a0f8 | 2022-04-10T02:32:23.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Wizounovziki | null | Wizounovziki/t5-base-devices-sum-ver2 | 1 | null | transformers | 31,177 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-devices-sum-ver2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-devices-sum-ver2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1919
- Rouge1: 95.2959
- Rouge2: 72.5788
- Rougel: 95.292
- Rougelsum: 95.3437
- Gen Len: 4.5992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 91 | 0.4308 | 87.5009 | 61.4165 | 87.6082 | 87.6628 | 4.3897 |
| No log | 2.0 | 182 | 0.2945 | 91.7111 | 66.9023 | 91.706 | 91.7348 | 4.4965 |
| No log | 3.0 | 273 | 0.2515 | 93.0416 | 68.8046 | 93.063 | 93.0907 | 4.516 |
| No log | 4.0 | 364 | 0.2259 | 94.2097 | 70.862 | 94.2438 | 94.2767 | 4.6283 |
| No log | 5.0 | 455 | 0.2148 | 94.7732 | 71.4693 | 94.78 | 94.8274 | 4.5936 |
| 0.4603 | 6.0 | 546 | 0.2030 | 95.0207 | 71.7789 | 95.0212 | 95.0887 | 4.5798 |
| 0.4603 | 7.0 | 637 | 0.1964 | 95.1482 | 72.3333 | 95.1651 | 95.202 | 4.6227 |
| 0.4603 | 8.0 | 728 | 0.1929 | 95.3279 | 72.551 | 95.3459 | 95.3972 | 4.5825 |
| 0.4603 | 9.0 | 819 | 0.1935 | 95.2413 | 72.5801 | 95.2372 | 95.3121 | 4.5992 |
| 0.4603 | 10.0 | 910 | 0.1919 | 95.2959 | 72.5788 | 95.292 | 95.3437 | 4.5992 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Splend1dchan/t5-small-squad | 9abfef429f6010755bfc512399e03f2d4549c2a3 | 2022-04-10T07:14:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Splend1dchan | null | Splend1dchan/t5-small-squad | 1 | null | transformers | 31,178 | Entry not found |
V3RX2000/xlm-roberta-base-finetuned-panx-de | 3efb3aacfb4bf59870a2c83591537b7f5ed3d02f | 2022-04-10T15:13:04.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | V3RX2000 | null | V3RX2000/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,179 | ---
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: 0.8590909090909091
---
<!-- 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: 0.1380
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2642 | 1.0 | 525 | 0.1624 | 0.8251 |
| 0.1315 | 2.0 | 1050 | 0.1445 | 0.8508 |
| 0.0832 | 3.0 | 1575 | 0.1380 | 0.8591 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
linyi/chirowm | f4c45b547a26b70ff294fd2085658e55e8bd87a4 | 2022-04-11T03:56:49.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | linyi | null | linyi/chirowm | 1 | null | transformers | 31,180 | Entry not found |
krinal214/bert-all-squad_ben_tel_context | 64cbb44696f7f6dd111f95cd6cc6dff63bc937e3 | 2022-04-10T15:06:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | krinal214 | null | krinal214/bert-all-squad_ben_tel_context | 1 | null | transformers | 31,181 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-all-squad_ben_tel_context
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-all-squad_ben_tel_context
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5393
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.996 | 1.0 | 12676 | 0.5393 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
V3RX2000/xlm-roberta-base-finetuned-panx-de-fr | 33eab6b1472fff3584f12bbad5ff6d7302f958fc | 2022-04-10T15:31:08.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | V3RX2000 | null | V3RX2000/xlm-roberta-base-finetuned-panx-de-fr | 1 | null | transformers | 31,182 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1667
- F1: 0.8582
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 |
| 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 |
| 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
V3RX2000/xlm-roberta-base-finetuned-panx-it | ed2c1ea485348a0265dfed973f135be7b2f9f3b8 | 2022-04-10T15:46:48.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | V3RX2000 | null | V3RX2000/xlm-roberta-base-finetuned-panx-it | 1 | null | transformers | 31,183 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.822805578342904
---
<!-- 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-it
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: 0.2323
- F1: 0.8228
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8126 | 1.0 | 70 | 0.3361 | 0.7231 |
| 0.2995 | 2.0 | 140 | 0.2526 | 0.8079 |
| 0.1865 | 3.0 | 210 | 0.2323 | 0.8228 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
V3RX2000/xlm-roberta-base-finetuned-panx-en | df16f72245d69c537487e494cbbc475edfe70e0e | 2022-04-10T15:53:36.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | V3RX2000 | null | V3RX2000/xlm-roberta-base-finetuned-panx-en | 1 | null | transformers | 31,184 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7075365579302588
---
<!-- 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-en
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: 0.3925
- F1: 0.7075
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1493 | 1.0 | 50 | 0.5884 | 0.4748 |
| 0.5135 | 2.0 | 100 | 0.4088 | 0.6623 |
| 0.3558 | 3.0 | 150 | 0.3925 | 0.7075 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
danhsf/xlm-roberta-base-finetuned-panx-de-fr | 0d554c12dfbad4306898f0bd1bae092841918dbe | 2022-04-10T18:21:26.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | danhsf | null | danhsf/xlm-roberta-base-finetuned-panx-de-fr | 1 | null | transformers | 31,185 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1667
- F1: 0.8582
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 |
| 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 |
| 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tonyalves/local_dataset | 2ac3dcd83b26dbac0c4d94a9e99d984a8a1bcaa9 | 2022-04-10T22:23:55.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | tonyalves | null | tonyalves/local_dataset | 1 | null | transformers | 31,186 | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
model-index:
- name: local_dataset
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. -->
# local_dataset
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 | d0f70b4b9a08c69a23626df6f2b87f163843f6ff | 2022-04-11T08:17:07.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wikihow",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Chikashi | null | Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 | 1 | null | transformers | 31,187 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_b8_lr3e-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 27.1711
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b8_lr3e-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3163
- Rouge1: 27.1711
- Rouge2: 10.6296
- Rougel: 23.206
- Rougelsum: 26.4801
- Gen Len: 18.5433
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.0734 | 0.25 | 5000 | 2.7884 | 22.4825 | 7.2492 | 19.243 | 21.9167 | 18.0616 |
| 2.9201 | 0.51 | 10000 | 2.7089 | 24.0869 | 8.0348 | 20.4814 | 23.4541 | 18.5994 |
| 2.8403 | 0.76 | 15000 | 2.6390 | 24.62 | 8.3776 | 20.8736 | 23.9784 | 18.4676 |
| 2.7764 | 1.02 | 20000 | 2.5943 | 24.1504 | 8.3933 | 20.8271 | 23.5382 | 18.4078 |
| 2.6641 | 1.27 | 25000 | 2.5428 | 25.6574 | 9.2371 | 21.8576 | 24.9558 | 18.4249 |
| 2.6369 | 1.53 | 30000 | 2.5042 | 25.5208 | 9.254 | 21.6673 | 24.8589 | 18.6467 |
| 2.6 | 1.78 | 35000 | 2.4637 | 26.094 | 9.7003 | 22.3097 | 25.4695 | 18.5065 |
| 2.5562 | 2.03 | 40000 | 2.4285 | 26.5374 | 9.9222 | 22.5291 | 25.8836 | 18.5553 |
| 2.4322 | 2.29 | 45000 | 2.3858 | 26.939 | 10.3555 | 23.0211 | 26.2834 | 18.5614 |
| 2.4106 | 2.54 | 50000 | 2.3537 | 26.7423 | 10.2816 | 22.7986 | 26.083 | 18.5792 |
| 2.3731 | 2.8 | 55000 | 2.3163 | 27.1711 | 10.6296 | 23.206 | 26.4801 | 18.5433 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
FabsCool/autotrain-T5Base1_1-728922203 | 713a20a79f05a412221db9a629dae712f031d5cf | 2022-04-11T10:31:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"unk",
"dataset:FabsCool/autotrain-data-T5Base1_1",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | FabsCool | null | FabsCool/autotrain-T5Base1_1-728922203 | 1 | null | transformers | 31,188 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- FabsCool/autotrain-data-T5Base1_1
co2_eq_emissions: 583.728921803621
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 728922203
- CO2 Emissions (in grams): 583.728921803621
## Validation Metrics
- Loss: 1.2922444343566895
- Rouge1: 54.3928
- Rouge2: 31.666
- RougeL: 50.3552
- RougeLsum: 50.3694
- Gen Len: 13.3425
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/FabsCool/autotrain-T5Base1_1-728922203
``` |
Yingda/dummy-model | 2fa5a10f8b17e971332163773a303ec56e3b9d2c | 2022-04-11T07:10:47.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Yingda | null | Yingda/dummy-model | 1 | null | transformers | 31,189 | Entry not found |
benjaminbeilharz/baseline | d33d31d14b9e81a6cbb678647def0817251bb696 | 2022-04-11T08:23:16.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | benjaminbeilharz | null | benjaminbeilharz/baseline | 1 | null | transformers | 31,190 | Entry not found |
Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-4 | 3701d4cee6882a06af0d40a125d69b8d9360f82d | 2022-04-11T17:20:49.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wikihow",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Chikashi | null | Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-4 | 1 | null | transformers | 31,191 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_b8_lr3e-4
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 27.3718
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b8_lr3e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3136
- Rouge1: 27.3718
- Rouge2: 10.6235
- Rougel: 23.3396
- Rougelsum: 26.6889
- Gen Len: 18.5194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.8029 | 0.25 | 5000 | 2.5368 | 25.2267 | 8.9048 | 21.2588 | 24.5804 | 18.4303 |
| 2.6924 | 0.51 | 10000 | 2.4725 | 25.6553 | 9.1904 | 21.7633 | 24.9807 | 18.5549 |
| 2.6369 | 0.76 | 15000 | 2.4332 | 26.2895 | 9.7203 | 22.3286 | 25.6009 | 18.4185 |
| 2.5994 | 1.02 | 20000 | 2.4051 | 26.1779 | 9.5708 | 22.3531 | 25.5357 | 18.561 |
| 2.521 | 1.27 | 25000 | 2.3805 | 26.7558 | 10.0411 | 22.7252 | 26.0476 | 18.304 |
| 2.5091 | 1.53 | 30000 | 2.3625 | 26.6439 | 10.0698 | 22.6662 | 25.9537 | 18.5437 |
| 2.4941 | 1.78 | 35000 | 2.3498 | 26.9322 | 10.2817 | 23.0002 | 26.2604 | 18.4953 |
| 2.4848 | 2.03 | 40000 | 2.3424 | 27.0381 | 10.3452 | 22.9749 | 26.3407 | 18.5749 |
| 2.4268 | 2.29 | 45000 | 2.3272 | 27.2386 | 10.4595 | 23.1866 | 26.5541 | 18.4954 |
| 2.4263 | 2.54 | 50000 | 2.3226 | 27.1489 | 10.532 | 23.1428 | 26.4657 | 18.5583 |
| 2.4161 | 2.8 | 55000 | 2.3136 | 27.3718 | 10.6235 | 23.3396 | 26.6889 | 18.5194 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Danastos/qacombination_bert_el | 8c42a4474e69452a67f9dbf72b8bfc0ba0466be2 | 2022-04-11T17:55:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:Danastos/qacombination_el_custom",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Danastos | null | Danastos/qacombination_bert_el | 1 | null | transformers | 31,192 | ---
tags:
- generated_from_trainer
datasets:
- Danastos/qacombination_el_custom
model-index:
- name: qacombination_bert_el
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. -->
# qacombination_bert_el
This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on the Danastos/qacombination_el_custom dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
theojolliffe/opus-mt-en-ro-finetuned-en-to-ro | d4a8c3a177d57a80c48a0b41bb3e672d8f0448d6 | 2022-04-11T18:45:14.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/opus-mt-en-ro-finetuned-en-to-ro | 1 | null | transformers | 31,193 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-en-ro-finetuned-en-to-ro
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 27.9273
---
<!-- 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2915
- Bleu: 27.9273
- Gen Len: 34.0935
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.7448 | 1.0 | 38145 | 1.2915 | 27.9273 | 34.0935 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
irenelizihui/MarianMT_UFAL_en_fr | ddcb6d1fa27a995d64d1e52d0b8726138a926d2f | 2022-04-11T23:03:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"license:other",
"autotrain_compatible"
] | text2text-generation | false | irenelizihui | null | irenelizihui/MarianMT_UFAL_en_fr | 1 | 1 | transformers | 31,194 | ---
license: other
---
UFAL English to French Machine Translation Model based on MarianMT model. |
mT0/mt0_xl_default_mixture_ckpt_1012500 | e54eda54cc373ffa15b83bf7e823b5b2ee2c9216 | 2022-04-11T19:43:52.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mT0 | null | mT0/mt0_xl_default_mixture_ckpt_1012500 | 1 | null | transformers | 31,195 | Entry not found |
tonyalves/ft-pt-br-local-2 | c0fa039872c4eb110b7a5f1c1a3a2ef7ed18d69a | 2022-04-11T20:57:03.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | tonyalves | null | tonyalves/ft-pt-br-local-2 | 1 | null | transformers | 31,196 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
model-index:
- name: ft-pt-br-local-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. -->
# ft-pt-br-local-2
This model is a fine-tuned version of [tonyalves/output](https://huggingface.co/tonyalves/output) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 1.18.4
- Tokenizers 0.11.6
|
BigSalmon/MediumInformalToFormalLincoln3 | 552bea2c447a6c03237c6503a4630bf6070359d4 | 2022-04-11T20:58:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/MediumInformalToFormalLincoln3 | 1 | null | transformers | 31,197 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln3")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln35")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
(makes two sentences, one sentence) (probably will not work all that well)
```
initial: phone books used to be everywhere. they have been replaced by the internet.
combined: once ubiquitous, phone books have been supplanted by the internet.
***
initial:
```
```
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
Keywords to sentences or sentence. |
dapang/gpt2-medium | 611e9cd93f135ff673a5d35d5fe121d4c2632cbf | 2022-04-12T03:52:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | dapang | null | dapang/gpt2-medium | 1 | null | transformers | 31,198 | Entry not found |
taile/xlm-roberta-large-finetuned-conll03-english | 4d1f95e7eec1065bc04e53f1bd0b08ff6c291c56 | 2022-04-12T03:58:03.000Z | [
"pytorch",
"rust",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | taile | null | taile/xlm-roberta-large-finetuned-conll03-english | 1 | null | transformers | 31,199 | Entry not found |
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