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laituan245/molt5-base | 7f2da6b30fd8ddc55b7867d53ce75e09bb85f284 | 2022-05-03T18:07:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/molt5-base | 1 | null | transformers | 31,600 | ---
license: apache-2.0
---
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-base", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base')
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
PSW/half_senttrm_del_seed1 | 65b2f5ff1b2b5fbd80820678404aed35be646650 | 2022-05-03T18:08:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_del_seed1 | 1 | null | transformers | 31,601 | Entry not found |
PSW/half_senttrm_del_seed27 | 96745b96be91183d525780bdc2ad385eb94b9e8a | 2022-05-03T18:51:09.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_del_seed27 | 1 | null | transformers | 31,602 | Entry not found |
PSW/half_senttrm_del_seed42 | 7a359ec9803b59760e1b12577816aa826a5717de | 2022-05-03T19:33:45.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_del_seed42 | 1 | null | transformers | 31,603 | Entry not found |
BigSalmon/ConciseAndFormal | a154d7baa1558083e38a8c52a41f6b156230a3c2 | 2022-05-03T19:42:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | BigSalmon | null | BigSalmon/ConciseAndFormal | 1 | null | transformers | 31,604 | how to start prompt:
```
wordy:
```
example:
```
wordy: the ndp has turned into the country's darling of the young.
```
output:
```
the ndp is youth-driven.
```
OR
```
informal english:
```
example:
```
informal english: corn fields are all across illinois, visible once you leave chicago.
```
output:
```
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.
``` |
BigSalmon/InformalToFormalLincoln41 | b4745d040399f5dd7962398f705e29de4f5eda93 | 2022-05-03T20:07:25.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln41 | 1 | null | transformers | 31,605 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln41")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
``` |
PSW/min_senttrm_ins_seed27 | 26b1b9890d881251beb5edec7bc9a8c6574185f4 | 2022-05-03T20:59:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/min_senttrm_ins_seed27 | 1 | null | transformers | 31,606 | Entry not found |
Dizzykong/gpt2-quests-eos | 4fde4a91bebe5deee577e69ac24b9798b5490c03 | 2022-05-03T21:14:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Dizzykong | null | Dizzykong/gpt2-quests-eos | 1 | null | transformers | 31,607 | Entry not found |
PSW/min_senttrm_ins_seed42 | ac773f7b5cd225b3c62083c12748ffa3875e59b1 | 2022-05-03T21:42:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/min_senttrm_ins_seed42 | 1 | null | transformers | 31,608 | Entry not found |
PSW/max_senttrm_ins_seed1 | e3074fe8369467bdebb6e66226beca19e7044f3e | 2022-05-03T22:25:48.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/max_senttrm_ins_seed1 | 1 | null | transformers | 31,609 | Entry not found |
eastmountaincode/newDuneModel | b395d21190e6f2c3708ed899e658d3fac8f2b159 | 2022-05-03T23:59:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | eastmountaincode | null | eastmountaincode/newDuneModel | 1 | null | transformers | 31,610 | Entry not found |
PSW/half_senttrm_ins_seed1 | 891302a1dac6421a045fc1638c36a5d0d37f90a5 | 2022-05-04T00:28:54.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_ins_seed1 | 1 | null | transformers | 31,611 | Entry not found |
PSW/half_senttrm_ins_seed27 | 581133ed28149281224183d9f0de2829e0dd5659 | 2022-05-04T01:19:05.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_ins_seed27 | 1 | null | transformers | 31,612 | Entry not found |
emolyscheisse/DialoGPT-small-mandybot | 17038689b38acfbd135b4bb1408078f0b001a11c | 2022-05-04T01:30:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | emolyscheisse | null | emolyscheisse/DialoGPT-small-mandybot | 1 | null | transformers | 31,613 | ---
tags:
- conversational
---
# Mandy Bot DialoGPT Model |
PSW/half_senttrm_ins_seed42 | 6729c2763f73e1c0a43153fa02fef7c0bbb1daed | 2022-05-04T02:02:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/half_senttrm_ins_seed42 | 1 | null | transformers | 31,614 | Entry not found |
PSW/senttrm_swap_seed1 | 5d289452ac9372b7b11e9316206d1a7163836456 | 2022-05-04T02:45:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_swap_seed1 | 1 | null | transformers | 31,615 | Entry not found |
PSW/senttrm_swap_seed27 | 72a2765936463d1c3e5bf0398a4d51cec72339b0 | 2022-05-04T03:28:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_swap_seed27 | 1 | null | transformers | 31,616 | Entry not found |
PSW/senttrm_swap_seed42 | 8232894284e63d08db7be3c27e704b9ff5502af5 | 2022-05-04T04:11:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_swap_seed42 | 1 | null | transformers | 31,617 | Entry not found |
iis2009002/xlm-roberta-base-finetuned-panx-de | 26c2d66932ebe21a9f97c27d0dd6226fc7ba9c23 | 2022-05-12T06:54:05.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | iis2009002 | null | iis2009002/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,618 | ---
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: 0.8627004891366169
---
<!-- 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: 0.1363
- F1: 0.8627
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jordimas/pronoms-prediction | 569f1cab1b8488313de4ac50dc3738de58600de2 | 2022-05-04T10:47:54.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | jordimas | null | jordimas/pronoms-prediction | 1 | null | transformers | 31,619 | ---
license: mit
---
|
jonfrank/xlm-roberta-base-finetuned-panx-de | f76ff8103908e3d89da23d34a9f93f9402b16e11 | 2022-05-04T10:13:21.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jonfrank | null | jonfrank/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,620 | ---
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: 0.8654425558524246
---
<!-- 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: 0.1334
- F1: 0.8654
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2541 | 1.0 | 525 | 0.1596 | 0.8242 |
| 0.1284 | 2.0 | 1050 | 0.1360 | 0.8499 |
| 0.0827 | 3.0 | 1575 | 0.1334 | 0.8654 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
PSW/mixed_sim4_seed27 | 144cc24ca14b7e1f2704be82619d40e5bbf8f127 | 2022-05-04T09:58:44.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/mixed_sim4_seed27 | 1 | null | transformers | 31,621 | Entry not found |
iis2009002/xlm-roberta-base-finetuned-panx-de-fr | 4f2a40077901b59e0fda84ef224332c68338cf76 | 2022-05-12T07:03:30.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | iis2009002 | null | iis2009002/xlm-roberta-base-finetuned-panx-de-fr | 1 | null | transformers | 31,622 | ---
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: 0.1644
- F1: 0.8617
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
neelan-elucidate-ai/wav2vec2-tcrs-runtest | 306421474ec94d33f6f46ac79dab5a7bba7ce936 | 2022-05-04T16:33:48.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | neelan-elucidate-ai | null | neelan-elucidate-ai/wav2vec2-tcrs-runtest | 1 | null | transformers | 31,623 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-tcrs-runtest
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-tcrs-runtest
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: 3.1370
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- 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: 10
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 22.437 | 1.43 | 10 | 36.3252 | 1.0 |
| 14.7939 | 2.86 | 20 | 10.7441 | 1.0 |
| 4.1824 | 4.29 | 30 | 3.7354 | 1.0 |
| 3.289 | 5.71 | 40 | 3.5265 | 1.0 |
| 3.1639 | 7.14 | 50 | 3.2868 | 1.0 |
| 3.1107 | 8.57 | 60 | 3.3268 | 1.0 |
| 3.0737 | 10.0 | 70 | 3.1149 | 1.0 |
| 3.0273 | 11.43 | 80 | 3.2031 | 1.0 |
| 3.0422 | 12.86 | 90 | 3.0771 | 1.0 |
| 2.9957 | 14.29 | 100 | 3.0418 | 1.0 |
| 2.9894 | 15.71 | 110 | 3.0321 | 1.0 |
| 2.9997 | 17.14 | 120 | 3.0545 | 1.0 |
| 2.9806 | 18.57 | 130 | 2.9936 | 1.0 |
| 2.969 | 20.0 | 140 | 3.0322 | 1.0 |
| 2.9692 | 21.43 | 150 | 3.0238 | 1.0 |
| 2.9638 | 22.86 | 160 | 3.0407 | 1.0 |
| 2.969 | 24.29 | 170 | 3.2487 | 1.0 |
| 2.9783 | 25.71 | 180 | 3.1248 | 1.0 |
| 2.9576 | 27.14 | 190 | 3.0880 | 1.0 |
| 2.968 | 28.57 | 200 | 3.0962 | 1.0 |
| 2.9784 | 30.0 | 210 | 3.1370 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
PSW/mixed_sim4_seed42 | a47167b8ccf90cea2ac92352c95b78fd801f4ab2 | 2022-05-04T10:41:35.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/mixed_sim4_seed42 | 1 | null | transformers | 31,624 | Entry not found |
iis2009002/xlm-roberta-base-finetuned-panx-fr | 2b3e5a13b151f2ead122bfadc2c5260c3b8e37b3 | 2022-05-12T07:06:21.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | iis2009002 | null | iis2009002/xlm-roberta-base-finetuned-panx-fr | 1 | null | transformers | 31,625 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.835464333781965
---
<!-- 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-fr
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: 0.2867
- F1: 0.8355
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5817 | 1.0 | 191 | 0.3395 | 0.7854 |
| 0.2617 | 2.0 | 382 | 0.2856 | 0.8278 |
| 0.1708 | 3.0 | 573 | 0.2867 | 0.8355 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
PSW/senttrm_mix_seed1 | fca2cd317f0259f0285ea29361006a46532600cc | 2022-05-04T11:24:43.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_mix_seed1 | 1 | null | transformers | 31,626 | Entry not found |
PSW/senttrm_mix_seed27 | 2b5b90d6ca85bfd01ecbf35e77eaaab541910035 | 2022-05-04T12:07:34.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_mix_seed27 | 1 | null | transformers | 31,627 | Entry not found |
PSW/senttrm_mix_seed42 | 6a311f9cea0c263b653a0121008475fbd098174f | 2022-05-04T12:50:51.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/senttrm_mix_seed42 | 1 | null | transformers | 31,628 | Entry not found |
yvesconst/mt5-ftune-edu-qg-fr | b42763e959c98fc97b8365af98774425a6278c30 | 2022-05-04T13:06:05.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | yvesconst | null | yvesconst/mt5-ftune-edu-qg-fr | 1 | null | transformers | 31,629 | ---
license: apache-2.0
---
|
Danastos/dpr-question_encoder_el_custom | 5f6442b8c6fc7e5d26d9db8ab097ff4b5a6e7128 | 2022-05-04T16:04:51.000Z | [
"pytorch",
"dpr",
"transformers"
] | null | false | Danastos | null | Danastos/dpr-question_encoder_el_custom | 1 | null | transformers | 31,630 | Entry not found |
lilitket/20220504-155308 | 3f1e53dd731e59f0e670e7d5aa18765aa4b3af2a | 2022-05-04T19:07:34.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220504-155308 | 1 | null | transformers | 31,631 | Entry not found |
huggingtweets/zacksteffen_ | 5aa4b00b2c28faf674aa5ce8e39cbf9f4ca5b23a | 2022-05-04T16:16:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/zacksteffen_ | 1 | null | transformers | 31,632 | ---
language: en
thumbnail: http://www.huggingtweets.com/zacksteffen_/1651680987265/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/1509644465388105731/dErjQdWT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Zack Steffen</div>
<div style="text-align: center; font-size: 14px;">@zacksteffen_</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 Zack Steffen.
| Data | Zack Steffen |
| --- | --- |
| Tweets downloaded | 3120 |
| Retweets | 869 |
| Short tweets | 523 |
| Tweets kept | 1728 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nz1w2dd/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 @zacksteffen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lqwnrcja) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lqwnrcja/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/zacksteffen_')
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)
|
theojolliffe/bart-large-cnn-finetuned-roundup-2-1 | 62e872cf0308345ff6fe211f27fb59c27f3c14d7 | 2022-05-04T16:57:42.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-large-cnn-finetuned-roundup-2-1 | 1 | null | transformers | 31,633 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-finetuned-roundup-2-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-2-1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 167 | 1.2456 | 51.7546 | 32.4725 | 33.0461 | 49.0513 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
theojolliffe/bart-large-cnn-finetuned-roundup-2-2 | 062c5bdc340e99c59d4245e8e61fbb025b0dbe76 | 2022-05-05T14:02:16.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-large-cnn-finetuned-roundup-2-2 | 1 | null | transformers | 31,634 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-2-2
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. -->
# bart-large-cnn-finetuned-roundup-2-2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1521
- Rouge1: 52.6634
- Rouge2: 32.537
- Rougel: 33.3148
- Rougelsum: 50.148
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 167 | 1.2139 | 52.546 | 32.4912 | 32.9529 | 49.8241 | 142.0 |
| No log | 2.0 | 334 | 1.1521 | 52.6634 | 32.537 | 33.3148 | 50.148 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
lilitket/20220504-180816 | 7b4ffb7382370d89ac7e21a6b685486697ed902a | 2022-05-04T19:46:54.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220504-180816 | 1 | null | transformers | 31,635 | Entry not found |
huggingtweets/kanyewest-usmnt-zlisto | cf4bccd69aa61490fe7e80f2fdf85681e2695015 | 2022-05-04T19:29:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/kanyewest-usmnt-zlisto | 1 | null | transformers | 31,636 | ---
language: en
thumbnail: http://www.huggingtweets.com/kanyewest-usmnt-zlisto/1651692574685/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/1410587808666955776/mWkKWw1U_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/1276461929934942210/cqNhNk6v_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/1486104199763013632/uC8Ujhgj_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">USMNT & ye & Tauhid R. Zaman</div>
<div style="text-align: center; font-size: 14px;">@kanyewest-usmnt-zlisto</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 USMNT & ye & Tauhid R. Zaman.
| Data | USMNT | ye | Tauhid R. Zaman |
| --- | --- | --- | --- |
| Tweets downloaded | 3247 | 1858 | 3098 |
| Retweets | 600 | 188 | 1232 |
| Short tweets | 215 | 573 | 106 |
| Tweets kept | 2432 | 1097 | 1760 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gvuccyzi/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 @kanyewest-usmnt-zlisto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1no8s780) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1no8s780/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/kanyewest-usmnt-zlisto')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/GPT2InformalToFormalLincoln42 | d0837910633143354ad7f69d63f0983200c06a77 | 2022-05-04T20:21:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/GPT2InformalToFormalLincoln42 | 1 | null | transformers | 31,637 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
``` |
Yanhao/simcse-bert-large-uncased | 678c1ce1c4c6a31b7ade4cbb3833cbf202759a75 | 2022-05-04T22:07:20.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Yanhao | null | Yanhao/simcse-bert-large-uncased | 1 | null | transformers | 31,638 | Entry not found |
lilitket/20220504-221523 | 03ba9ccd469d9adbc9c44ef7aee9104d7684ee40 | 2022-05-05T22:11:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220504-221523 | 1 | null | transformers | 31,639 | Entry not found |
lilitket/20220504-221549 | fce063d29dcf76f0c3d8c3d656c0f14c2a0031b5 | 2022-05-05T11:34:56.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220504-221549 | 1 | null | transformers | 31,640 | Entry not found |
laituan245/t5-v1_1-small-caption2smiles | b64e522b5f456e24bf3a5908c0cb9ef31bda18cd | 2022-05-05T00:10:18.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-small-caption2smiles | 1 | null | transformers | 31,641 | ---
license: apache-2.0
---
|
laituan245/t5-v1_1-small-smiles2caption | cf731029d7ceaa6cdb5e54c3eb70e4a048af2570 | 2022-05-05T00:17:34.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-small-smiles2caption | 1 | null | transformers | 31,642 | ---
license: apache-2.0
---
|
laituan245/t5-v1_1-large-smiles2caption | 4c3afa02a8789c81f980f015d381302a27fc05f4 | 2022-05-05T01:04:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-large-smiles2caption | 1 | null | transformers | 31,643 | ---
license: apache-2.0
---
|
laituan245/t5-v1_1-small-smiles2caption-ft-from-pretrained-c4 | 5eca383ddc521f7da8879bc56b869529e835ec4c | 2022-05-05T02:16:45.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-small-smiles2caption-ft-from-pretrained-c4 | 1 | null | transformers | 31,644 | Entry not found |
laituan245/t5-v1_1-small-caption2smiles-ft-from-pretrained-c4 | 4971a4da61aaa050c98ffe87ac0f0535359526d9 | 2022-05-05T02:23:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-small-caption2smiles-ft-from-pretrained-c4 | 1 | null | transformers | 31,645 | Entry not found |
laituan245/t5-v1_1-small-smiles2caption-ft-from-pretrained-zinc | 0b7df225bac6305770a31352d2341875ef457343 | 2022-05-05T02:37:22.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/t5-v1_1-small-smiles2caption-ft-from-pretrained-zinc | 1 | null | transformers | 31,646 | Entry not found |
aaatul/xlm-roberta-large-finetuned-ner | 5c5b5b724333833ca6074be2e98e975360a993de | 2022-06-01T09:06:31.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:hi_ner_config",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | aaatul | null | aaatul/xlm-roberta-large-finetuned-ner | 1 | null | transformers | 31,647 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- hi_ner_config
model-index:
- name: xlm-roberta-large-finetuned-ner
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-large-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the hi_ner_config 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: 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: 6
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
PSW/low_resource_percent1_minmaxswap_seed27 | 55ece750e43094df71d22fffb17cc11e8398108d | 2022-05-05T07:02:47.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_minmaxswap_seed27 | 1 | null | transformers | 31,648 | Entry not found |
PSW/low_resource_percent1_randomdel_seed1 | 7cca13d828cb4ec8cf979a29098c17cd0a900fb5 | 2022-05-05T07:57:20.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomdel_seed1 | 1 | null | transformers | 31,649 | Entry not found |
PSW/low_resource_percent1_randomdel_seed27 | 1fe83ef7f49863eaf7ab7ad37b766461e7f773a2 | 2022-05-05T08:08:34.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomdel_seed27 | 1 | null | transformers | 31,650 | Entry not found |
DioLiu/distilroberta-base-wiki_shake_mask | bc370a92ecfd1afc50258a77110fcf5ce093d1fd | 2022-05-05T09:26:08.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | DioLiu | null | DioLiu/distilroberta-base-wiki_shake_mask | 1 | null | transformers | 31,651 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-wiki_shake_mask
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-wiki_shake_mask
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6528 | 1.0 | 3015 | 2.5390 |
| 2.5536 | 2.0 | 6030 | 2.4558 |
| 2.5396 | 3.0 | 9045 | 2.4464 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
PSW/low_resource_percent1_randomins_seed1 | fc3a54ae34f80bd1e0dd7f254863f489f20821a8 | 2022-05-05T08:29:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomins_seed1 | 1 | null | transformers | 31,652 | Entry not found |
PSW/low_resource_percent1_randomins_seed27 | d0c39f940ebe3ed0df6e480b52abcf94afbf6200 | 2022-05-05T08:40:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomins_seed27 | 1 | null | transformers | 31,653 | Entry not found |
PSW/low_resource_percent1_randomswap_seed1 | 24f07b1a840dd28b309e78883b1a4fedb2c06ea9 | 2022-05-05T09:01:52.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomswap_seed1 | 1 | null | transformers | 31,654 | Entry not found |
CarlCochet/trajectory-transformer-ant-medium-expert-v2 | b71b730ed68c338836de09b07fd97102785af892 | 2022-05-12T16:56:30.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-ant-medium-expert-v2 | 1 | null | transformers | 31,655 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-ant-medium-replay-v2 | 245182042697164e959373281ecee709f5769eba | 2022-05-12T16:57:17.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-ant-medium-replay-v2 | 1 | null | transformers | 31,656 | ---
license: mit
---
|
PSW/low_resource_percent10_maxsimins_seed1 | 1a40e96c99fd1c8f270ccef268196f2b1db5fa5f | 2022-05-05T10:00:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_maxsimins_seed1 | 1 | null | transformers | 31,657 | Entry not found |
adityay1221/cat.5.32 | b12f2f929f126f74333536e7276d8dc53d9c962a | 2022-05-05T09:58:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | adityay1221 | null | adityay1221/cat.5.32 | 1 | null | transformers | 31,658 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cat.5.32
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. -->
# cat.5.32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0293
- Bleu: 25.3811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 121
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
PSW/low_resource_percent10_maxsimins_seed27 | 54ac78f99d9a3510dd235c156d38ed51ba7fea1b | 2022-05-05T10:13:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_maxsimins_seed27 | 1 | null | transformers | 31,659 | Entry not found |
PSW/low_resource_percent10_maxsimins_seed42 | 5e235fc09aef85d9aac74a19bf459c9554890e7d | 2022-05-05T10:28:50.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_maxsimins_seed42 | 1 | null | transformers | 31,660 | Entry not found |
PSW/low_resource_percent10_minmaxswap_seed1 | deca3c84d93bbd7d8d16d4e3bd31d6e45ffa6dfe | 2022-05-05T10:44:01.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minmaxswap_seed1 | 1 | null | transformers | 31,661 | Entry not found |
PSW/low_resource_percent10_minmaxswap_seed27 | 8008cbd7f29520aad3541b9ba8a83e14f0953d0a | 2022-05-05T10:59:47.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minmaxswap_seed27 | 1 | null | transformers | 31,662 | Entry not found |
masakhane/afrimbart_en_lug_news | 3ad3f5ef4886f7e64aec78c5e994b7e63cb207ba | 2022-05-05T13:41:25.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimbart_en_lug_news | 1 | null | transformers | 31,663 | ---
license: afl-3.0
---
|
masakhane/afrimt5_en_lug_news | e8057600da66fc3b21c7e161f9ba4d5fc8a5440c | 2022-05-05T13:41:11.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_en_lug_news | 1 | null | transformers | 31,664 | ---
license: afl-3.0
---
|
masakhane/afribyt5_lug_en_news | d809310beaf6eeba769f3e9582e007651c7741d4 | 2022-05-05T13:50:17.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afribyt5_lug_en_news | 1 | null | transformers | 31,665 | ---
license: afl-3.0
---
|
masakhane/mt5_lug_en_news | 7a32b8c55a04711aa862cb6771c8e0637e9d3f61 | 2022-05-05T14:04:28.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_lug_en_news | 1 | null | transformers | 31,666 | ---
license: afl-3.0
---
|
aware-ai/wav2vec2-xls-r-300m-german | f1e207210170e9df66086ea0947a34eae7ac4c46 | 2022-05-28T07:50:30.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"de",
"transformers",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | aware-ai | null | aware-ai/wav2vec2-xls-r-300m-german | 1 | null | transformers | 31,667 | ---
language:
- de
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-german
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-german
This model is a fine-tuned version of [wav2vec2-xls-r-300m-german](https://huggingface.co/wav2vec2-xls-r-300m-german) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4304
- Wer: 0.4507
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3598 | 1.0 | 569 | 0.4313 | 0.4512 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.1.0
- Tokenizers 0.11.0
|
masakhane/m2m100_418M_en_lug_rel_news | 2ad7f847917340cf8e066492498b22e144b28ff8 | 2022-05-05T14:14:06.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_en_lug_rel_news | 1 | null | transformers | 31,668 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_rel_news_ft | d8cdc76faa6b2e106203a1afe3aae275b496b055 | 2022-05-05T14:22:53.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_en_lug_rel_news_ft | 1 | null | transformers | 31,669 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_ft | 633b5cda871da5871260bd112bfd21787215758b | 2022-05-05T14:23:03.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_lug_en_rel_ft | 1 | null | transformers | 31,670 | ---
license: afl-3.0
---
|
PSW/low_resource_percent10_minsimdel_seed1 | e7235bdd00b937ae13277bdad79013e4b0dca069 | 2022-05-05T11:29:24.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minsimdel_seed1 | 1 | null | transformers | 31,671 | Entry not found |
Theimisa/distilbert-base-uncased-aisera_texts | 680317f14f39dfbc4eb2cbaccf0cf97cfe07d5c4 | 2022-05-09T09:49:59.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | Theimisa | null | Theimisa/distilbert-base-uncased-aisera_texts | 1 | null | transformers | 31,672 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-aisera_texts
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-aisera_texts
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0694 | 1.0 | 7790 | 1.9868 |
| 1.9054 | 2.0 | 15580 | 1.8646 |
| 1.8701 | 3.0 | 23370 | 1.8283 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Tokenizers 0.12.1
|
PSW/low_resource_percent10_randomins_seed1 | 167fd3154c8f29327eaa71dacadcf37fefbe11c6 | 2022-05-05T12:59:30.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomins_seed1 | 1 | null | transformers | 31,673 | Entry not found |
dyyyyyyyy/xTune_panx_XLM-RoBERTa-base | 56a9a34bfa2fbe06fc3bef07d744bd3fa04858a0 | 2022-05-05T14:06:17.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
] | null | false | dyyyyyyyy | null | dyyyyyyyy/xTune_panx_XLM-RoBERTa-base | 1 | null | transformers | 31,674 | Entry not found |
PSW/low_resource_percent10_randomswap_seed27 | 27158ac7947ebfe400a239256baad98daf4fb641 | 2022-05-05T13:56:00.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomswap_seed27 | 1 | null | transformers | 31,675 | Entry not found |
tau/False_large_random_para0_sent1_span2_itFalse_sargmax_rrFalse_8_1024_0.15_1 | dccc3efe38e97f3c9f1bd1274af89ab98c698289 | 2022-05-05T14:00:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/False_large_random_para0_sent1_span2_itFalse_sargmax_rrFalse_8_1024_0.15_1 | 1 | null | transformers | 31,676 | Entry not found |
tau/False_large_rouge_para0_sent1_span2_itTrue_sargmax_rrFalse_8_1024_0.15_1 | 519692256604a734a836729ecfe9c558324db32b | 2022-05-05T13:59:39.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/False_large_rouge_para0_sent1_span2_itTrue_sargmax_rrFalse_8_1024_0.15_1 | 1 | null | transformers | 31,677 | Entry not found |
tau/False_large_pmi_para0_sent1_span2_itFalse_ssoftmax_rrFalse_8_1024_0.15_1 | 9176da21acc93eaa12fa535f2e143bd43d19e273 | 2022-05-05T18:18:45.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/False_large_pmi_para0_sent1_span2_itFalse_ssoftmax_rrFalse_8_1024_0.15_1 | 1 | null | transformers | 31,678 | Entry not found |
dyyyyyyyy/xTune_udpos_XLM-RoBERTa-base | 323bfbc798c875cf3ee9dd088ace39f1b910e83f | 2022-05-05T14:36:36.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
] | null | false | dyyyyyyyy | null | dyyyyyyyy/xTune_udpos_XLM-RoBERTa-base | 1 | null | transformers | 31,679 | Entry not found |
dyyyyyyyy/xTune_udpos_XLM-RoBERTa-large | 4c2389d32e56333a305d0b9e0d72287c212e3cdc | 2022-05-05T14:37:44.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
] | null | false | dyyyyyyyy | null | dyyyyyyyy/xTune_udpos_XLM-RoBERTa-large | 1 | null | transformers | 31,680 | Entry not found |
PSW/low_resource_percent20_maxsimins_seed27 | 5ce799f4cc188d3b9ee45a4e28684d4016a83b91 | 2022-05-05T15:43:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_maxsimins_seed27 | 1 | null | transformers | 31,681 | Entry not found |
PSW/low_resource_percent20_minmaxswap_seed1 | fb6e79b3f7350b8e4c48874e5606ef52c52725c5 | 2022-05-05T16:16:14.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_minmaxswap_seed1 | 1 | null | transformers | 31,682 | Entry not found |
PSW/low_resource_percent20_minmaxswap_seed27 | ba569c0279f4a9e57c995e1c9de6bcd5b5a0fc74 | 2022-05-05T16:32:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_minmaxswap_seed27 | 1 | null | transformers | 31,683 | Entry not found |
PSW/low_resource_percent20_minsimdel_seed1 | 4c5e5fc19d7d68a550efb47dc9ba813c3ccad491 | 2022-05-05T16:59:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_minsimdel_seed1 | 1 | null | transformers | 31,684 | Entry not found |
PSW/low_resource_percent20_randomdel_seed27 | 2ca3b3959a209f2021a96421f25d56cf369e1436 | 2022-05-05T17:53:21.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomdel_seed27 | 1 | null | transformers | 31,685 | Entry not found |
zhanxw/test | 869dd8d5d376d04647347c7d3919d0211cce6cd6 | 2022-05-05T18:25:47.000Z | [
"pytorch",
"swin",
"image-classification",
"transformers",
"license:mit"
] | image-classification | false | zhanxw | null | zhanxw/test | 1 | null | transformers | 31,686 | ---
license: mit
---
|
PSW/low_resource_percent20_randomdel_seed42 | c670f15d134e796df8070cc30e0bdbca76121e0c | 2022-05-05T18:09:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomdel_seed42 | 1 | null | transformers | 31,687 | Entry not found |
PSW/low_resource_percent20_randomins_seed1 | 14194b8863cabba8207337bf849bbebf45d02d53 | 2022-05-05T18:26:16.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomins_seed1 | 1 | null | transformers | 31,688 | Entry not found |
dyyyyyyyy/MVR_panx_XLM-RoBERTa-large | 217412927476626ba425b04cd99a03bb5b5deea4 | 2022-05-06T05:21:29.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_panx_XLM-RoBERTa-large | 1 | null | transformers | 31,689 | Entry not found |
dyyyyyyyy/MVR_squad_XLM-RoBERTa-large | cc3035aeeb7c7540b795bfc1802b4bff3351305a | 2022-05-06T06:52:25.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_squad_XLM-RoBERTa-large | 1 | null | transformers | 31,690 | Entry not found |
dyyyyyyyy/MVR_squad_XLM-RoBERTa-base | ad3b7537877269c5d2bdff20af0f248c0ea000a0 | 2022-05-06T06:45:06.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_squad_XLM-RoBERTa-base | 1 | null | transformers | 31,691 | Entry not found |
dyyyyyyyy/MVR_panx_BERT-base-multilingual-cased | b603a82267d3780c14f17b67605d08e900a4d81a | 2022-05-06T05:13:48.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_panx_BERT-base-multilingual-cased | 1 | null | transformers | 31,692 | Entry not found |
PSW/low_resource_percent20_randomins_seed27 | 431c4c5a5b81786d4ecb9b0c723a8c06c424acb2 | 2022-05-05T18:42:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomins_seed27 | 1 | null | transformers | 31,693 | Entry not found |
PSW/low_resource_percent20_randomins_seed42 | a7598ba30f063deadb78c227e7198c8957ee96b5 | 2022-05-05T18:59:36.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomins_seed42 | 1 | null | transformers | 31,694 | Entry not found |
PSW/low_resource_percent20_randomswap_seed27 | 9eee3ab0fc4b6cb6eaeff8504fd00c33c8f83e48 | 2022-05-05T19:32:46.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomswap_seed27 | 1 | null | transformers | 31,695 | Entry not found |
abhilashawasthi/bert-base-uncased-issues-128 | 1e3e2439ffb321596fb1ff06d4499ae7ea082445 | 2022-05-05T20:17:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | abhilashawasthi | null | abhilashawasthi/bert-base-uncased-issues-128 | 1 | null | transformers | 31,696 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2520
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0949 | 1.0 | 291 | 1.7072 |
| 1.649 | 2.0 | 582 | 1.4409 |
| 1.4835 | 3.0 | 873 | 1.4099 |
| 1.3938 | 4.0 | 1164 | 1.3858 |
| 1.3326 | 5.0 | 1455 | 1.2004 |
| 1.2949 | 6.0 | 1746 | 1.2955 |
| 1.2451 | 7.0 | 2037 | 1.2682 |
| 1.1992 | 8.0 | 2328 | 1.1938 |
| 1.1784 | 9.0 | 2619 | 1.1686 |
| 1.1397 | 10.0 | 2910 | 1.2050 |
| 1.1293 | 11.0 | 3201 | 1.2058 |
| 1.1006 | 12.0 | 3492 | 1.1680 |
| 1.0835 | 13.0 | 3783 | 1.2414 |
| 1.0757 | 14.0 | 4074 | 1.1522 |
| 1.062 | 15.0 | 4365 | 1.1176 |
| 1.0535 | 16.0 | 4656 | 1.2520 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
PSW/low_resource_percent20_randomswap_seed42 | 563d0f60db190197222e6b095feccf8bd946ef79 | 2022-05-05T19:49:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomswap_seed42 | 1 | null | transformers | 31,697 | Entry not found |
PSW/low_resource_percent20_seed42 | 1c7caf5f364434c1cd5b6abeac1037f4a9225d0c | 2022-05-05T20:30:53.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_seed42 | 1 | null | transformers | 31,698 | Entry not found |
huggingtweets/mikedolanvevo | b9dcd25ba01b78c7221ee23ffad1e27aebdc3e4e | 2022-05-05T20:52:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/mikedolanvevo | 1 | null | transformers | 31,699 | ---
language: en
thumbnail: http://www.huggingtweets.com/mikedolanvevo/1651783946409/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/1500475522291277831/EmO4IU6D_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">lil venice bitch</div>
<div style="text-align: center; font-size: 14px;">@mikedolanvevo</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 lil venice bitch.
| Data | lil venice bitch |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 426 |
| Short tweets | 311 |
| Tweets kept | 2447 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jhq37i4/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 @mikedolanvevo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1emwhhe4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1emwhhe4/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/mikedolanvevo')
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)
|
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