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| library_name
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chrisjay/afrospeech-wav2vec-kua
|
chrisjay
| 2022-10-10T02:16:10Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"afro-digits-speech",
"dataset:crowd-speech-africa",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-10-04T22:25:55Z |
---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-kua
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 0.9921875
---
# afrospeech-wav2vec-kua
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Oshiwambo (`kua`).
- Size of training set: 1376
- Size of validation set: 345
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_oshiwambo_kua_audio_data.csv):
- F1: 0.9913480945477086
- Accuracy: 0.9921875
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.0096 | 1 | 0.9843 |
| 0.2555 | 50 | 0.9843 |
| 0.00145 | 100 | 0.98177 |
| 0.00053 | 150 | 0.97770 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1
|
chrisjay/afrospeech-wav2vec-ibo
|
chrisjay
| 2022-10-10T02:15:57Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"afro-digits-speech",
"dataset:crowd-speech-africa",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-10-04T19:23:32Z |
---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-ibo
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 1.0
---
# afrospeech-wav2vec-ibo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Igbo (`ibo`).
- Size of training set: 109
- Size of validation set: 28
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_igbo_ibo_audio_data.csv):
- F1: 1.0
- Accuracy: 1.0
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.1415 | 1 | 1.0 |
| 0.0241 | 50 | 1.0 |
| 0.0019 | 100 | 0.929 |
| 0.0012 | 150 | 0.892 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1
|
BigSalmon/Infill
|
BigSalmon
| 2022-10-10T00:57:07Z | 185 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-26T23:06:22Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
Infill / Infilling / Masking / Phrase Masking
```
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
```
|
BigSalmon/InformalToFormalLincoln84Paraphrase
|
BigSalmon
| 2022-10-10T00:36:04Z | 182 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-08T22:35:14Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
Phrase Mask
Infill
Infilling
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln84Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln84Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
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:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
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 "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
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:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Suffix> trae young <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Suffix> honor <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
<Suffix> of internationality <Prefix> there are examples of strategies that have <Middle> withstood the test <Middle>
```
```
test: the movie's success has ..... the producer's earlier failure. (a) overshadowed, (b) obscured, (c) outshone, (d) offset
```
```
complete: as sales figures soared, so too did [hiring openings] -> employment opportunities. just as noteworthy was [wages increased] -> the effect on wage growth.
***
complete: in exchange for a small uptick in the labor bill, they were able to [get holiday season most money possible] -> ( wring the most out of the holiday season / maximize the proceeds of the holiday season / milk the holiday season for all its worth ).
```
```
h: of all the obligations of adulthood, there is none that elicits more scorn than paying taxes. indeed, any exchange where money is taken and not received in return is bound to be the source of frustration. understandably, the impulse is to want to hold on to every dollar earned. yet, this urge must be tempered by a realization that tax revenue is essential to a functional society.
question: what does “this urge” mean in the above context?
(a) anti-tax sentiment
(b) libertarian disposition
(c) yearning to retain every dollar
(d) resistance to redistribution
(e) misanthropic attitude
```
|
BigSalmon/Infill05
|
BigSalmon
| 2022-10-10T00:35:47Z | 198 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-10T00:29:02Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill05")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill05")
```
```
Try it out here:
https://huggingface.co/spaces/BigSalmon/TestAnyGPTModel
```
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
Phrase Mask
Infill / Infilling / Masking / Phrase Masking
```
His contention [blank] by the evidence [sep] was refuted [answer]
***
Few sights are as [blank] New York City as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
When rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
The library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
```
```
original: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the [MASK] star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently [MASK] the big screen in Garden State, which he also directed. Farrell is pencilled in to [MASK] of Crockett in a film version of 1980s police [MASK] Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
infill: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the show. The film star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently been seen on the big screen in Garden State, which he also directed. Farrell is pencilled in to play the role of Crockett in a film version of 1980s police drama Miami Vice. Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
```
```
<Suffix> of internationality <Prefix> there are examples of strategies that have <Middle> withstood the test <Middle>
```
|
huggingtweets/cl207
|
huggingtweets
| 2022-10-10T00:30:08Z | 129 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-24T06:56:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/cl207/1665361801897/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/1565608502793367552/PgTC0Bk8_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">CL</div>
<div style="text-align: center; font-size: 14px;">@cl207</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 CL.
| Data | CL |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 165 |
| Short tweets | 495 |
| Tweets kept | 2580 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kd7xeiw/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 @cl207's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bcsycgu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bcsycgu/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/cl207')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/thisislux
|
huggingtweets
| 2022-10-09T23:38:08Z | 128 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-09T23:36:49Z |
---
language: en
thumbnail: http://www.huggingtweets.com/thisislux/1665358684361/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/1358092565862637571/I8IpAB-v_400x400.png')">
</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">Lux</div>
<div style="text-align: center; font-size: 14px;">@thisislux</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 Lux.
| Data | Lux |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 662 |
| Short tweets | 470 |
| Tweets kept | 2118 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1369ctkf/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 @thisislux's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/djry9bsi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/djry9bsi/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/thisislux')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/bittynox
|
huggingtweets
| 2022-10-09T23:23:59Z | 128 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-09T21:58:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bittynox/1665357835052/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/1557896466252840961/UJXL2x8V_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">nox</div>
<div style="text-align: center; font-size: 14px;">@bittynox</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 nox.
| Data | nox |
| --- | --- |
| Tweets downloaded | 1681 |
| Retweets | 190 |
| Short tweets | 184 |
| Tweets kept | 1307 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cafbxcq5/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 @bittynox's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ctlbcy2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ctlbcy2/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/bittynox')
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)
|
ai-forever/RUDOLPH-350M
|
ai-forever
| 2022-10-09T23:22:34Z | 0 | 9 | null |
[
"pytorch",
"RUDOLPH",
"text-image",
"image-text",
"decoder",
"dataset:sberquad",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- RUDOLPH
- text-image
- image-text
- decoder
datasets:
- sberquad
---
# RUDOLPH-350M (Small)
RUDOLPH: One Hyper-Tasking Transformer Сan be Сreative as DALL-E and GPT-3 and Smart as CLIP
<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/RUDOLPH.png" width=60% border="2"/>
Model was trained by [Sber AI](https://github.com/ai-forever) team.
# Model Description
**RU**ssian **D**ecoder **O**n **L**anguage **P**icture **H**yper-tasking (**RUDOLPH**) **350M** is a fast and light text-image-text transformer designed for a quick and easy fine-tuning for a range of tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-tasking Transformers.
*Hyper-tasking model is a generalized multi-tasking model, i.e., the model that can solve almost all tasks within supported modalities, mandatory including mutual pairwise translations between modalities (two modalities in case of RUDOLPH: images and Russian texts).*
* Tasks: ` text2image generation, self reranking, text ranking, image ranking, image2text generation, zero-shot image classification, text2text generation, and so on`
* Language: ` Russian`
* Type: ` decoder`
* Num Parameters: ` 350M`
* Training Data Volume: `141 million text-image pairs, 7.6 million text paragraphs`
# Details of architecture
<img src=https://raw.githubusercontent.com/ai-forever/ru-dolph/master/pics/scheme-rudolph_350m.jpg height="20" border="2"/>
The maximum sequence length that this model may be used with depends on the modality and stands for 64 - 256 - 64 for the left text tokens, image tokens, and right text tokens, respectively.
RUDOLPH 350M is a Transformer-based decoder model with the following parameters:
* num\_layers (24) — Number of hidden layers in the Transformer decoder.
* hidden\_size (1024) — Dimensionality of the hidden layers.
* num\_attention\_heads (16) — Number of attention heads for each attention layer.
# Sparse Attention Masks
The primary proposed method is to modify the sparse transformer's attention mask to better control modalities. It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with both image and left text condition.
<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/attention_masks_350m.png" height="40" border="2"/>
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Michael Konstantinov: [Mishin Learning](https://t.me/mishin_learning), [Transformer Community](https://transformer.community/)
|
huggingtweets/eeriemachine
|
huggingtweets
| 2022-10-09T22:04:18Z | 129 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-15T21:04:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/eeriemachine/1665353005078/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/1579097527982460934/-x9lVWzx_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">Alea 🃏</div>
<div style="text-align: center; font-size: 14px;">@eeriemachine</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 Alea 🃏.
| Data | Alea 🃏 |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 68 |
| Short tweets | 181 |
| Tweets kept | 2991 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/15ucae0z/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 @eeriemachine's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1smqz4yt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1smqz4yt/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/eeriemachine')
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)
|
halflings/agricultural_yield
|
halflings
| 2022-10-09T21:51:23Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"en",
"license:unknown",
"region:us"
] | null | 2022-10-09T21:51:20Z |
---
license: unknown
language:
- en
tags:
- mlconsole
library_name: mlconsole
metrics:
- mae
- loss
model-index:
- name: agricultural_yield
results:
- task:
type: regression
name: regression
dataset:
type: agricultural yield
name: Agricultural yield
metrics:
- type: mae
name: Mean absolute error
value: 7.280202388763428
- type: loss
name: Model loss
value: 128.23114013671875
---
# Agricultural yield (#2)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/agricultural_yield).
|
halflings/diabetes_detection
|
halflings
| 2022-10-09T21:50:54Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"en",
"license:unknown",
"region:us"
] | null | 2022-10-09T21:50:51Z |
---
license: unknown
language:
- en
tags:
- mlconsole
library_name: mlconsole
metrics:
- accuracy
- loss
model-index:
- name: diabetes_detection
results:
- task:
type: classification
name: classification
dataset:
type: diabetes detection
name: Diabetes detection
metrics:
- type: accuracy
name: Accuracy
value: 0.765625
- type: loss
name: Model loss
value: 0.5329774022102356
---
# Diabetes detection (#1)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/diabetes_detection).
|
halflings/house_price_prediction
|
halflings
| 2022-10-09T21:50:29Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"en",
"license:unknown",
"region:us"
] | null | 2022-10-09T21:50:27Z |
---
license: unknown
language:
- en
tags:
- mlconsole
library_name: mlconsole
metrics:
- mae
- loss
model-index:
- name: house_price_prediction
results:
- task:
type: regression
name: regression
dataset:
type: house price prediction
name: House price prediction
metrics:
- type: mae
name: Mean absolute error
value: 5.42720365524292
- type: loss
name: Model loss
value: 50.44972229003906
---
# House price prediction (#0)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/house_price_prediction).
|
anas-awadalla/t5-base-finetuned-squad-infilling-lr-5e-5
|
anas-awadalla
| 2022-10-09T19:46:30Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:15:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-finetuned-squad-infilling-lr-5e-5
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-finetuned-squad-infilling-lr-5e-5
This model is a fine-tuned version of [anas-awadalla/t5-base-finetuned-squad-infilling-lr-5e-5](https://huggingface.co/anas-awadalla/t5-base-finetuned-squad-infilling-lr-5e-5) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 2.0
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
saefro991/m-ailabs_en-us_judy_phn_tacotron2
|
saefro991
| 2022-10-09T18:10:01Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:m_ailabs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-10-09T17:24:47Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- m_ailabs
license: cc-by-4.0
---
## ESPnet2 TTS model
### `saefro991/m-ailabs_en-us_judy_phn_tacotron2`
This model was trained by Takaaki-Saeki using m_ailabs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 32b0f75b4491b71e88deac62bfc431cfcc9d7143
pip install -e .
cd egs2/m_ailabs/tts1
./run.sh --skip_data_prep false --skip_train true --download_model saefro991/m-ailabs_en-us_judy_phn_tacotron2
```
## TTS config
<details><summary>expand</summary>
```
config: conf/train.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_raw_phn_tacotron_g2p_en
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 5120000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en/train/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en/valid/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - dump/raw/tr_no_dev/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - dump/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ''
- AH0
- T
- N
- D
- R
- S
- L
- DH
- IH1
- K
- EH1
- M
- Z
- AE1
- W
- IH0
- AH1
- ','
- B
- IY1
- ER0
- UW1
- P
- HH
- AY1
- F
- V
- AA1
- AO1
- .
- EY1
- IY0
- OW1
- NG
- G
- Y
- AW1
- SH
- CH
- ER1
- UH1
- TH
- JH
- OW0
- OY1
- '?'
- '!'
- EH0
- EY2
- IH2
- ''''
- AY2
- AA0
- EH2
- UW0
- AA2
- AH2
- AE0
- OW2
- AO2
- UW2
- AE2
- ZH
- AW2
- AY0
- IY2
- AO0
- UH0
- UH2
- OY2
- AW0
- ER2
- EY0
- OY0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 16000
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en/train/feats_stats.npz
tts: tacotron2
tts_conf:
embed_dim: 512
elayers: 1
eunits: 512
econv_layers: 3
econv_chans: 512
econv_filts: 5
atype: location
adim: 512
aconv_chans: 32
aconv_filts: 15
cumulate_att_w: true
dlayers: 2
dunits: 1024
prenet_layers: 2
prenet_units: 256
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
output_activation: null
use_batch_norm: true
use_concate: true
use_residual: false
dropout_rate: 0.5
zoneout_rate: 0.1
reduction_factor: 1
spk_embed_dim: null
use_masking: true
bce_pos_weight: 5.0
use_guided_attn_loss: true
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202209'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
sd-concepts-library/toy-bonnie-plush
|
sd-concepts-library
| 2022-10-09T17:38:28Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-09T17:38:22Z |
---
license: mit
---
### Toy Bonnie plush on Stable Diffusion
This is the `<toy-bonnie-plush>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
osanseviero/us-patents
|
osanseviero
| 2022-10-09T16:28:05Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-09T16:05:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: us-patents
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. -->
# us-patents
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0229
- Pearson: 0.8317
## 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: 8e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 214 | 0.0289 | 0.7929 |
| No log | 2.0 | 428 | 0.0242 | 0.8211 |
| 0.0359 | 3.0 | 642 | 0.0232 | 0.8304 |
| 0.0359 | 4.0 | 856 | 0.0229 | 0.8317 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
hezzze/ppo-LunarLander-v2
|
hezzze
| 2022-10-09T15:52:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-09T15:36:30Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.33 +/- 17.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
din0s/t5-base_ro-finetuned-en-to-it
|
din0s
| 2022-10-09T15:40:49Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T13:16:59Z |
---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-base_ro-finetuned-en-to-it
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 19.6396
---
<!-- 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_ro-finetuned-en-to-it
This model is a fine-tuned version of [j0hngou/t5-base-finetuned-en-to-ro](https://huggingface.co/j0hngou/t5-base-finetuned-en-to-ro) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4669
- Bleu: 19.6396
- Gen Len: 52.4247
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 282 | 2.0942 | 5.6875 | 73.434 |
| 2.5108 | 2.0 | 564 | 1.9725 | 6.6631 | 72.6607 |
| 2.5108 | 3.0 | 846 | 1.9010 | 7.9227 | 67.01 |
| 2.1659 | 4.0 | 1128 | 1.8452 | 8.9935 | 65.1027 |
| 2.1659 | 5.0 | 1410 | 1.7979 | 9.4164 | 64.9827 |
| 2.0288 | 6.0 | 1692 | 1.7590 | 9.6035 | 66.6933 |
| 2.0288 | 7.0 | 1974 | 1.7264 | 10.7658 | 62.068 |
| 1.9238 | 8.0 | 2256 | 1.6955 | 11.5779 | 59.472 |
| 1.8435 | 9.0 | 2538 | 1.6729 | 12.7588 | 56.584 |
| 1.8435 | 10.0 | 2820 | 1.6541 | 13.3086 | 56.1153 |
| 1.775 | 11.0 | 3102 | 1.6337 | 13.8543 | 55.3307 |
| 1.775 | 12.0 | 3384 | 1.6148 | 14.3566 | 55.2853 |
| 1.7204 | 13.0 | 3666 | 1.5994 | 14.693 | 55.6607 |
| 1.7204 | 14.0 | 3948 | 1.5838 | 15.1284 | 55.5327 |
| 1.6705 | 15.0 | 4230 | 1.5742 | 15.6125 | 55.0087 |
| 1.632 | 16.0 | 4512 | 1.5600 | 15.9616 | 54.052 |
| 1.632 | 17.0 | 4794 | 1.5526 | 16.495 | 53.562 |
| 1.5868 | 18.0 | 5076 | 1.5392 | 16.4252 | 54.4613 |
| 1.5868 | 19.0 | 5358 | 1.5311 | 16.753 | 54.1853 |
| 1.5656 | 20.0 | 5640 | 1.5262 | 17.0308 | 54.2473 |
| 1.5656 | 21.0 | 5922 | 1.5186 | 17.3553 | 53.396 |
| 1.529 | 22.0 | 6204 | 1.5121 | 17.6177 | 53.472 |
| 1.529 | 23.0 | 6486 | 1.5058 | 17.6409 | 53.6847 |
| 1.5071 | 24.0 | 6768 | 1.5038 | 18.2009 | 53.2327 |
| 1.4903 | 25.0 | 7050 | 1.4962 | 18.4838 | 52.9587 |
| 1.4903 | 26.0 | 7332 | 1.4935 | 18.5545 | 52.688 |
| 1.4686 | 27.0 | 7614 | 1.4879 | 18.62 | 53.5 |
| 1.4686 | 28.0 | 7896 | 1.4850 | 19.0099 | 52.34 |
| 1.4511 | 29.0 | 8178 | 1.4813 | 19.0538 | 52.474 |
| 1.4511 | 30.0 | 8460 | 1.4787 | 18.89 | 53.0753 |
| 1.4364 | 31.0 | 8742 | 1.4756 | 19.2582 | 52.3587 |
| 1.4279 | 32.0 | 9024 | 1.4739 | 19.2973 | 52.69 |
| 1.4279 | 33.0 | 9306 | 1.4725 | 19.3624 | 52.694 |
| 1.4172 | 34.0 | 9588 | 1.4704 | 19.5421 | 52.1667 |
| 1.4172 | 35.0 | 9870 | 1.4689 | 19.4807 | 52.5487 |
| 1.4141 | 36.0 | 10152 | 1.4685 | 19.5972 | 52.2733 |
| 1.4141 | 37.0 | 10434 | 1.4676 | 19.5835 | 52.374 |
| 1.4058 | 38.0 | 10716 | 1.4674 | 19.6374 | 52.3447 |
| 1.4058 | 39.0 | 10998 | 1.4671 | 19.6105 | 52.5273 |
| 1.4027 | 40.0 | 11280 | 1.4669 | 19.6396 | 52.4247 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
jamesesguerra/distilbart-cnn-12-6-finetuned-1.2.1
|
jamesesguerra
| 2022-10-09T15:28:36Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T14:47:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-1.2.1
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. -->
# distilbart-cnn-12-6-finetuned-1.2.1
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9404
- Rouge1: 30.4308
- Rouge2: 13.2594
- Rougel: 25.8203
- Rougelsum: 25.9617
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.5124 | 1.0 | 1171 | 2.0753 | 29.493 | 12.3563 | 24.8091 | 24.9317 |
| 1.7628 | 2.0 | 2342 | 1.9404 | 30.4308 | 13.2594 | 25.8203 | 25.9617 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
din0s/t5-small-finetuned-en-to-it-b32
|
din0s
| 2022-10-09T15:15:47Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T13:59:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-it-b32
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 9.6816
---
<!-- 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-en-to-it-b32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1496
- Bleu: 9.6816
- Gen Len: 56.5347
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 282 | 2.9409 | 2.6764 | 69.2487 |
| 3.3809 | 2.0 | 564 | 2.8277 | 2.4974 | 87.428 |
| 3.3809 | 3.0 | 846 | 2.7483 | 2.6851 | 89.7887 |
| 3.1255 | 4.0 | 1128 | 2.6831 | 3.1801 | 85.6927 |
| 3.1255 | 5.0 | 1410 | 2.6293 | 3.6949 | 79.9467 |
| 2.9965 | 6.0 | 1692 | 2.5809 | 4.0149 | 76.852 |
| 2.9965 | 7.0 | 1974 | 2.5403 | 4.3463 | 74.6487 |
| 2.9002 | 8.0 | 2256 | 2.5033 | 4.838 | 72.6053 |
| 2.8229 | 9.0 | 2538 | 2.4694 | 5.2829 | 67.984 |
| 2.8229 | 10.0 | 2820 | 2.4421 | 5.4964 | 68.986 |
| 2.76 | 11.0 | 3102 | 2.4135 | 5.8118 | 66.528 |
| 2.76 | 12.0 | 3384 | 2.3897 | 6.1966 | 65.052 |
| 2.7051 | 13.0 | 3666 | 2.3667 | 6.452 | 64.2273 |
| 2.7051 | 14.0 | 3948 | 2.3465 | 6.6428 | 63.516 |
| 2.6568 | 15.0 | 4230 | 2.3265 | 6.9467 | 61.8673 |
| 2.6183 | 16.0 | 4512 | 2.3101 | 7.2029 | 60.7393 |
| 2.6183 | 17.0 | 4794 | 2.2954 | 7.4982 | 60.0327 |
| 2.5757 | 18.0 | 5076 | 2.2799 | 7.7555 | 59.968 |
| 2.5757 | 19.0 | 5358 | 2.2660 | 7.8406 | 60.0307 |
| 2.5534 | 20.0 | 5640 | 2.2558 | 8.0679 | 59.0793 |
| 2.5534 | 21.0 | 5922 | 2.2426 | 8.3325 | 58.5367 |
| 2.5159 | 22.0 | 6204 | 2.2324 | 8.3538 | 58.6893 |
| 2.5159 | 23.0 | 6486 | 2.2217 | 8.5867 | 57.7627 |
| 2.4983 | 24.0 | 6768 | 2.2135 | 8.8324 | 56.7367 |
| 2.4791 | 25.0 | 7050 | 2.2052 | 8.8113 | 57.4373 |
| 2.4791 | 26.0 | 7332 | 2.1981 | 9.0909 | 57.0173 |
| 2.4529 | 27.0 | 7614 | 2.1908 | 9.0056 | 57.802 |
| 2.4529 | 28.0 | 7896 | 2.1856 | 9.2696 | 56.9773 |
| 2.4395 | 29.0 | 8178 | 2.1780 | 9.2824 | 57.0007 |
| 2.4395 | 30.0 | 8460 | 2.1722 | 9.2106 | 56.9893 |
| 2.4277 | 31.0 | 8742 | 2.1685 | 9.4668 | 56.406 |
| 2.4181 | 32.0 | 9024 | 2.1646 | 9.4992 | 56.2327 |
| 2.4181 | 33.0 | 9306 | 2.1616 | 9.5054 | 56.3033 |
| 2.4071 | 34.0 | 9588 | 2.1578 | 9.5093 | 56.548 |
| 2.4071 | 35.0 | 9870 | 2.1554 | 9.5227 | 56.7807 |
| 2.3991 | 36.0 | 10152 | 2.1532 | 9.5762 | 56.756 |
| 2.3991 | 37.0 | 10434 | 2.1518 | 9.6659 | 56.5913 |
| 2.3955 | 38.0 | 10716 | 2.1506 | 9.7199 | 56.5753 |
| 2.3955 | 39.0 | 10998 | 2.1498 | 9.6715 | 56.558 |
| 2.3913 | 40.0 | 11280 | 2.1496 | 9.6816 | 56.5347 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
jannatul17/squad-bn-qgen-banglat5-v1
|
jannatul17
| 2022-10-09T14:43:36Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T04:29:56Z |
---
tags:
- generated_from_trainer
model-index:
- name: final-squad-bn-qgen-banglat5-all-metric-v3
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. -->
# final-squad-bn-qgen-banglat5-all-metric-v3
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4231
- Rouge1 Precision: 39.5263
- Rouge1 Recall: 37.168
- Rouge1 Fmeasure: 37.1806
- Rouge2 Precision: 17.7035
- Rouge2 Recall: 16.508
- Rouge2 Fmeasure: 16.5336
- Rougel Precision: 37.135
- Rougel Recall: 34.9177
- Rougel Fmeasure: 34.9266
- Rougelsum Precision: 37.1205
- Rougelsum Recall: 34.8982
- Rougelsum Fmeasure: 34.9129
- Bleu-1: 36.4356
- Bleu-2: 22.3217
- Bleu-3: 14.7682
- Bleu-4: 10.0865
- Meteor: 0.2051
## 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: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:-------:|:-------:|:-------:|:-------:|:------:|
| 0.5901 | 1.0 | 6769 | 0.4756 | 32.2563 | 31.3211 | 30.7652 | 12.2914 | 11.9567 | 11.6739 | 29.1321 | 28.3925 | 27.84 | 29.1291 | 28.3832 | 27.8378 | 31.9366 | 17.9528 | 10.9479 | 6.8658 | 0.1715 |
| 0.5094 | 2.0 | 13538 | 0.4343 | 37.5727 | 35.6711 | 35.4661 | 16.3104 | 15.4196 | 15.3046 | 35.2059 | 33.4452 | 33.2559 | 35.1882 | 33.4395 | 33.24 | 35.1532 | 21.1183 | 13.7297 | 9.2128 | 0.1955 |
| 0.4866 | 3.0 | 20307 | 0.4267 | 38.6402 | 36.2947 | 36.2796 | 16.8569 | 15.7129 | 15.7114 | 36.2902 | 34.0855 | 34.0734 | 36.2733 | 34.0723 | 34.0661 | 35.6286 | 21.554 | 14.098 | 9.5506 | 0.1996 |
| 0.4732 | 4.0 | 27076 | 0.4235 | 39.3469 | 36.7357 | 36.8598 | 17.4835 | 16.2062 | 16.2677 | 36.9883 | 34.5422 | 34.6543 | 36.9783 | 34.5352 | 34.6594 | 35.9917 | 21.9745 | 14.4922 | 9.884 | 0.203 |
| 0.4646 | 5.0 | 33845 | 0.4224 | 39.4223 | 37.0956 | 37.0893 | 17.6277 | 16.4682 | 16.4692 | 37.0994 | 34.896 | 34.8991 | 37.0885 | 34.8691 | 34.8811 | 36.3637 | 22.2704 | 14.7068 | 10.021 | 0.2049 |
| 0.4517 | 6.0 | 40614 | 0.4231 | 39.5263 | 37.168 | 37.1806 | 17.7035 | 16.508 | 16.5336 | 37.135 | 34.9177 | 34.9266 | 37.1205 | 34.8982 | 34.9129 | 36.4356 | 22.3217 | 14.7682 | 10.0865 | 0.2051 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tomthekkan/dummy-model
|
tomthekkan
| 2022-10-09T14:40:40Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-09T14:10:20Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: tmphgqi7q16
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmphgqi7q16
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/roblox-avatar
|
sd-concepts-library
| 2022-10-09T13:22:25Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-09T13:08:46Z |
---
license: mit
---
### Roblox avatar on Stable Diffusion
why am i spending time making these?, anyways.
This is the `<roblox-avatar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
photos were taken from pinterest.
Here is the new concept you will be able to use as an `object`:
|
tejas23/autotrain-amx2-1702259725
|
tejas23
| 2022-10-09T13:10:48Z | 4 | 0 |
transformers
|
[
"transformers",
"joblib",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:tejas23/autotrain-data-amx2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
tabular-classification
| 2022-10-09T13:03:31Z |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 7.7048287301375975
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259725
- CO2 Emissions (in grams): 7.7048
## Validation Metrics
- Loss: 0.421
- Accuracy: 0.827
- Macro F1: 0.530
- Micro F1: 0.827
- Weighted F1: 0.805
- Macro Precision: 0.579
- Micro Precision: 0.827
- Weighted Precision: 0.795
- Macro Recall: 0.513
- Micro Recall: 0.827
- Weighted Recall: 0.827
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
tejas23/autotrain-amx2-1702259728
|
tejas23
| 2022-10-09T13:08:34Z | 5 | 0 |
transformers
|
[
"transformers",
"joblib",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:tejas23/autotrain-data-amx2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
tabular-classification
| 2022-10-09T13:03:38Z |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 0.00824689737605251
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259728
- CO2 Emissions (in grams): 0.0082
## Validation Metrics
- Loss: 0.434
- Accuracy: 0.831
- Macro F1: 0.521
- Micro F1: 0.831
- Weighted F1: 0.803
- Macro Precision: 0.590
- Micro Precision: 0.831
- Weighted Precision: 0.794
- Macro Recall: 0.507
- Micro Recall: 0.831
- Weighted Recall: 0.831
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
tejas23/autotrain-amx2-1702259729
|
tejas23
| 2022-10-09T13:05:26Z | 2 | 0 |
transformers
|
[
"transformers",
"joblib",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:tejas23/autotrain-data-amx2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
tabular-classification
| 2022-10-09T13:03:45Z |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 0.002766545033914285
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259729
- CO2 Emissions (in grams): 0.0028
## Validation Metrics
- Loss: 6.095
- Accuracy: 0.824
- Macro F1: 0.543
- Micro F1: 0.824
- Weighted F1: 0.808
- Macro Precision: 0.572
- Micro Precision: 0.824
- Weighted Precision: 0.801
- Macro Recall: 0.543
- Micro Recall: 0.824
- Weighted Recall: 0.824
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
theojolliffe/bart-model2-0910
|
theojolliffe
| 2022-10-09T12:52:02Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T11:53:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-model2-0910
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-model2-0910
This model is a fine-tuned version of [theojolliffe/bart-model2-1409](https://huggingface.co/theojolliffe/bart-model2-1409) 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: 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 | 48 | 0.7670 | 38.9302 | 29.5801 | 37.9912 | 35.6634 | 20.0 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
fathyshalaby/emailclassifier
|
fathyshalaby
| 2022-10-09T11:43:30Z | 130 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-08T21:21:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: emailclassifier
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. -->
# emailclassifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
theojolliffe/bart-model2-0810
|
theojolliffe
| 2022-10-09T10:28:37Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T15:20:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-model2-0810
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-model2-0810
This model is a fine-tuned version of [theojolliffe/bart-model2-1409](https://huggingface.co/theojolliffe/bart-model2-1409) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2627
- Rouge1: 58.8322
- Rouge2: 56.2696
- Rougel: 58.8934
- Rougelsum: 58.3106
- Gen Len: 19.2222
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 169 | 0.3839 | 53.2948 | 45.0992 | 52.1785 | 53.8143 | 18.0 |
| No log | 2.0 | 338 | 0.3099 | 55.227 | 49.17 | 55.1602 | 55.6483 | 17.8889 |
| 0.3831 | 3.0 | 507 | 0.2566 | 56.6535 | 52.9359 | 56.1953 | 56.0607 | 18.8889 |
| 0.3831 | 4.0 | 676 | 0.2627 | 58.8322 | 56.2696 | 58.8934 | 58.3106 | 19.2222 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
whyperr/stable-dreamfusion
|
whyperr
| 2022-10-09T09:02:43Z | 0 | 4 | null |
[
"region:us"
] | null | 2022-10-03T15:09:53Z |
You can use Stable Diffusion with Dream's face on it!
The object name is: dream-youtuber, and the class name is male.
You can use it in the prompts like:
photo of dream-youtuber male, digital painting
Now to use this model:
In AUTOMATIC1111's notebook (https://github.com/AUTOMATIC1111/stable-diffusion-webui) just below the Normal 1.4 Model block. Insert this block:
````
#@title Stable-Dreamfusion Model
# get a token from https://huggingface.co/settings/tokens
user_token = "" #@param {type:"string"}
user_header = f"\"Authorization: Bearer {user_token}\""
!wget --header={user_header} https://huggingface.co/whyperr/stable-dreamfusion/resolve/main/model.ckpt -O models/sd-v1-4.ckpt
````
Run the next steps normally, and you should be able to generate juicy dream faces!
|
sd-concepts-library/fox-purple
|
sd-concepts-library
| 2022-10-09T08:43:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-09T08:29:08Z |
---
license: mit
---
### fox purple on Stable Diffusion
This is the `<foxi-purple>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
XerOpred/twitter-climate-sentiment-model
|
XerOpred
| 2022-10-09T08:32:29Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-09T06:24:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: twitter-climate-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-climate-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2779
- eval_accuracy: 0.8941
- eval_f1: 0.9372
- eval_runtime: 135.2041
- eval_samples_per_second: 39.873
- eval_steps_per_second: 2.493
- epoch: 1.0
- step: 1348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cpu
- Datasets 2.5.2
- Tokenizers 0.12.1
|
ptrsxu/chinese-bert-wwm-ext
|
ptrsxu
| 2022-10-09T08:24:21Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-09T07:19:12Z |
---
language:
- zh
license: "apache-2.0"
---
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
ksahi03/afi
|
ksahi03
| 2022-10-09T08:01:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-09T08:01:48Z |
---
license: creativeml-openrail-m
---
|
chucyj/123
|
chucyj
| 2022-10-09T07:29:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-09T07:29:19Z |
---
license: creativeml-openrail-m
---
|
fumi13/vit-base-beans
|
fumi13
| 2022-10-09T06:46:29Z | 220 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-09T06:31:17Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- Accuracy: 0.9925
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3039 | 1.0 | 130 | 0.2474 | 0.9624 |
| 0.1299 | 2.0 | 260 | 0.1007 | 0.9925 |
| 0.0885 | 3.0 | 390 | 0.0824 | 0.9925 |
| 0.0976 | 4.0 | 520 | 0.1179 | 0.9699 |
| 0.1284 | 5.0 | 650 | 0.0832 | 0.9774 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
XerOpred/sentiment-model
|
XerOpred
| 2022-10-09T06:23:13Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-09T03:37:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment-model
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:
- eval_loss: 0.4302
- eval_accuracy: 0.8337
- eval_f1: 0.0
- eval_runtime: 25.9665
- eval_samples_per_second: 30.809
- eval_steps_per_second: 1.926
- epoch: 1.0
- step: 200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cpu
- Tokenizers 0.12.1
|
g30rv17ys/ddpm-geeve-cnv-10k-1000ep
|
g30rv17ys
| 2022-10-09T06:14:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-08T18:04:24Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-geeve-cnv-10k-1000ep
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-10k-1000ep/tensorboard?#scalars)
|
everyl12/crisis_sentiment_roberta
|
everyl12
| 2022-10-09T03:56:23Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-08T18:52:36Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: crisis_sentiment_roberta
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# crisis_sentiment_roberta
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on an unknown dataset.
It achieves the following results on the testing set:
- Accuracy: 0.83
- Macro accuracy: 0.76
- Weighted accuracy: 0.83
## Model description
0. Negative
1. Positive
2. Neutral
Sentiment classification using 9,300 tweets of the Flint Water Crisis
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4781 | 1.0 | 349 | 0.4452 | 0.8366 |
| 0.2074 | 2.0 | 698 | 0.5010 | 0.8237 |
| 0.047 | 3.0 | 1047 | 0.5772 | 0.8199 |
| 0.0114 | 4.0 | 1396 | 0.7793 | 0.8226 |
| 0.007 | 5.0 | 1745 | 0.8584 | 0.8188 |
| 0.0144 | 6.0 | 2094 | 0.9517 | 0.8070 |
| 0.0017 | 7.0 | 2443 | 1.0054 | 0.8231 |
| 0.0013 | 8.0 | 2792 | 1.1297 | 0.8172 |
| 0.0008 | 9.0 | 3141 | 1.1622 | 0.8263 |
| 0.001 | 10.0 | 3490 | 1.2313 | 0.8204 |
| 0.0006 | 11.0 | 3839 | 1.2360 | 0.8220 |
| 0.0007 | 12.0 | 4188 | 1.2687 | 0.8161 |
| 0.0004 | 13.0 | 4537 | 1.2940 | 0.8204 |
| 0.0451 | 14.0 | 4886 | 1.3163 | 0.8194 |
| 0.0004 | 15.0 | 5235 | 1.2991 | 0.8242 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.13.0.dev20220917+cu117
- Datasets 2.4.0
- Tokenizers 0.12.1
|
anas-awadalla/t5-base-finetuned-squad-infilling-lr-3e-5
|
anas-awadalla
| 2022-10-09T02:58:00Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T23:06:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-finetuned-squad-infilling-lr-3e-5
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-finetuned-squad-infilling-lr-3e-5
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-09T02:19:11Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-09T01:51:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-09T01:18:25Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T22:25:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-1024-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
nobadreams/onoriegab
|
nobadreams
| 2022-10-08T23:35:04Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-10-08T23:13:02Z |
---
license: cc-by-nc-sa-4.0
---
|
din0s/t5-base_fr-finetuned-en-to-it
|
din0s
| 2022-10-08T22:58:23Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:29:28Z |
---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-base_fr-finetuned-en-to-it
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 20.3152
---
<!-- 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_fr-finetuned-en-to-it
This model is a fine-tuned version of [j0hngou/t5-base-finetuned-en-to-fr](https://huggingface.co/j0hngou/t5-base-finetuned-en-to-fr) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4677
- Bleu: 20.3152
- Gen Len: 51.4433
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 282 | 2.0344 | 6.8826 | 64.574 |
| 2.3997 | 2.0 | 564 | 1.9371 | 7.9377 | 64.274 |
| 2.3997 | 3.0 | 846 | 1.8740 | 9.2364 | 59.8673 |
| 2.145 | 4.0 | 1128 | 1.8240 | 9.8068 | 60.566 |
| 2.145 | 5.0 | 1410 | 1.7813 | 10.3961 | 60.106 |
| 2.0183 | 6.0 | 1692 | 1.7476 | 11.2005 | 59.032 |
| 2.0183 | 7.0 | 1974 | 1.7152 | 11.8127 | 58.1673 |
| 1.9185 | 8.0 | 2256 | 1.6872 | 12.4843 | 57.5787 |
| 1.8414 | 9.0 | 2538 | 1.6643 | 13.4338 | 55.502 |
| 1.8414 | 10.0 | 2820 | 1.6459 | 13.7847 | 55.6753 |
| 1.7755 | 11.0 | 3102 | 1.6273 | 14.6959 | 53.838 |
| 1.7755 | 12.0 | 3384 | 1.6121 | 15.2948 | 53.4127 |
| 1.7224 | 13.0 | 3666 | 1.5967 | 15.878 | 53.0733 |
| 1.7224 | 14.0 | 3948 | 1.5809 | 16.3788 | 52.778 |
| 1.6751 | 15.0 | 4230 | 1.5689 | 16.7415 | 52.8 |
| 1.6358 | 16.0 | 4512 | 1.5580 | 17.0318 | 52.854 |
| 1.6358 | 17.0 | 4794 | 1.5509 | 17.6302 | 52.0947 |
| 1.5921 | 18.0 | 5076 | 1.5389 | 17.4239 | 52.71 |
| 1.5921 | 19.0 | 5358 | 1.5317 | 17.9003 | 52.3427 |
| 1.5696 | 20.0 | 5640 | 1.5253 | 17.769 | 52.928 |
| 1.5696 | 21.0 | 5922 | 1.5172 | 18.2974 | 51.8173 |
| 1.5344 | 22.0 | 6204 | 1.5117 | 18.5755 | 52.012 |
| 1.5344 | 23.0 | 6486 | 1.5046 | 18.5362 | 52.1447 |
| 1.5136 | 24.0 | 6768 | 1.5034 | 18.7394 | 51.9887 |
| 1.4968 | 25.0 | 7050 | 1.4968 | 19.1622 | 51.736 |
| 1.4968 | 26.0 | 7332 | 1.4947 | 19.1636 | 51.8467 |
| 1.472 | 27.0 | 7614 | 1.4886 | 19.3845 | 51.774 |
| 1.472 | 28.0 | 7896 | 1.4844 | 19.5481 | 51.458 |
| 1.4575 | 29.0 | 8178 | 1.4827 | 19.739 | 51.4593 |
| 1.4575 | 30.0 | 8460 | 1.4791 | 19.818 | 51.62 |
| 1.4435 | 31.0 | 8742 | 1.4763 | 19.904 | 51.5167 |
| 1.4336 | 32.0 | 9024 | 1.4750 | 19.9507 | 51.3787 |
| 1.4336 | 33.0 | 9306 | 1.4742 | 20.0704 | 51.3527 |
| 1.4236 | 34.0 | 9588 | 1.4717 | 20.2553 | 51.1967 |
| 1.4236 | 35.0 | 9870 | 1.4705 | 20.3014 | 51.156 |
| 1.4188 | 36.0 | 10152 | 1.4697 | 20.2263 | 51.4173 |
| 1.4188 | 37.0 | 10434 | 1.4687 | 20.244 | 51.394 |
| 1.412 | 38.0 | 10716 | 1.4681 | 20.2699 | 51.5993 |
| 1.412 | 39.0 | 10998 | 1.4676 | 20.2758 | 51.4473 |
| 1.4087 | 40.0 | 11280 | 1.4677 | 20.3152 | 51.4433 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
din0s/t5-base-finetuned-en-to-it
|
din0s
| 2022-10-08T22:57:01Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:28:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-base-finetuned-en-to-it
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 20.1194
---
<!-- 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-finetuned-en-to-it
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4830
- Bleu: 20.1194
- Gen Len: 51.456
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 282 | 2.0137 | 6.5621 | 69.0227 |
| 2.4006 | 2.0 | 564 | 1.9278 | 7.2684 | 70.0333 |
| 2.4006 | 3.0 | 846 | 1.8712 | 8.6643 | 64.654 |
| 2.1423 | 4.0 | 1128 | 1.8223 | 9.3778 | 63.4453 |
| 2.1423 | 5.0 | 1410 | 1.7836 | 10.0151 | 63.778 |
| 2.0248 | 6.0 | 1692 | 1.7515 | 10.9865 | 62.224 |
| 2.0248 | 7.0 | 1974 | 1.7208 | 11.5089 | 61.2 |
| 1.9316 | 8.0 | 2256 | 1.6936 | 12.3755 | 60.1047 |
| 1.8584 | 9.0 | 2538 | 1.6731 | 12.8765 | 59.4427 |
| 1.8584 | 10.0 | 2820 | 1.6535 | 13.7278 | 57.6253 |
| 1.7949 | 11.0 | 3102 | 1.6360 | 14.2498 | 56.3913 |
| 1.7949 | 12.0 | 3384 | 1.6222 | 14.8795 | 55.346 |
| 1.7461 | 13.0 | 3666 | 1.6064 | 15.017 | 55.7473 |
| 1.7461 | 14.0 | 3948 | 1.5926 | 15.3093 | 56.0067 |
| 1.6998 | 15.0 | 4230 | 1.5803 | 15.6934 | 55.366 |
| 1.6635 | 16.0 | 4512 | 1.5707 | 16.3604 | 54.5413 |
| 1.6635 | 17.0 | 4794 | 1.5633 | 16.8086 | 53.824 |
| 1.621 | 18.0 | 5076 | 1.5515 | 17.1319 | 53.5927 |
| 1.621 | 19.0 | 5358 | 1.5450 | 17.5039 | 53.5167 |
| 1.6008 | 20.0 | 5640 | 1.5389 | 17.8012 | 53.6527 |
| 1.6008 | 21.0 | 5922 | 1.5314 | 17.7305 | 53.342 |
| 1.5656 | 22.0 | 6204 | 1.5259 | 18.1609 | 53.4033 |
| 1.5656 | 23.0 | 6486 | 1.5200 | 18.6506 | 52.226 |
| 1.5466 | 24.0 | 6768 | 1.5185 | 18.9433 | 52.2173 |
| 1.53 | 25.0 | 7050 | 1.5120 | 19.0978 | 52.022 |
| 1.53 | 26.0 | 7332 | 1.5083 | 19.1326 | 52.0527 |
| 1.5072 | 27.0 | 7614 | 1.5044 | 19.0854 | 52.2447 |
| 1.5072 | 28.0 | 7896 | 1.5002 | 19.372 | 51.7687 |
| 1.4926 | 29.0 | 8178 | 1.4977 | 19.5798 | 52.0327 |
| 1.4926 | 30.0 | 8460 | 1.4941 | 19.5161 | 51.9893 |
| 1.478 | 31.0 | 8742 | 1.4911 | 19.7821 | 51.534 |
| 1.47 | 32.0 | 9024 | 1.4897 | 19.7207 | 51.4787 |
| 1.47 | 33.0 | 9306 | 1.4888 | 19.8066 | 51.5407 |
| 1.4603 | 34.0 | 9588 | 1.4869 | 19.9036 | 51.398 |
| 1.4603 | 35.0 | 9870 | 1.4856 | 19.9575 | 51.352 |
| 1.4558 | 36.0 | 10152 | 1.4845 | 19.9513 | 51.4833 |
| 1.4558 | 37.0 | 10434 | 1.4840 | 20.0177 | 51.3027 |
| 1.4486 | 38.0 | 10716 | 1.4833 | 20.0644 | 51.484 |
| 1.4486 | 39.0 | 10998 | 1.4830 | 20.1001 | 51.5747 |
| 1.4452 | 40.0 | 11280 | 1.4830 | 20.1194 | 51.456 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
yohila/distilbert-base-uncased-finetuned-emotion
|
yohila
| 2022-10-08T22:27:15Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-08T22:14:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9220501325456948
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2235
- Accuracy: 0.922
- F1: 0.9221
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8211 | 1.0 | 250 | 0.3228 | 0.898 | 0.8943 |
| 0.2485 | 2.0 | 500 | 0.2235 | 0.922 | 0.9221 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
waifu-research-department/Nishikigi-Chisato
|
waifu-research-department
| 2022-10-08T21:39:58Z | 0 | 5 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-27T20:33:21Z |
---
license: mit
---
# Description
Trainer: ChrisC
Chisato from Lycoris Recoil
# Dataset
>Training: 22 images
>Regularization: 400 images
# Info
>chisato_3k_WD1-3.ckpt
>Model Used: Waifu Diffusion 1.3
>Steps: 3000
>Keyword: chisato nishikigi (Use this in the prompt)
>Class Phrase: lycoreco
>Chisato_3k.ckpt is based on Waifu Diffusion 1.2 (Keyword: chisato)
|
anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T21:22:59Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T21:10:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-2
|
anas-awadalla
| 2022-10-08T21:08:23Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:56:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-256-finetuned-squad-infilling-seed-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. -->
# t5-base-few-shot-k-256-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
sd-concepts-library/meze-audio-elite-headphones
|
sd-concepts-library
| 2022-10-08T21:07:36Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-08T21:07:31Z |
---
license: mit
---
### Meze Audio Elite headphones on Stable Diffusion
This is the `<meze-elite>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T20:53:56Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:42:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-256-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-256-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-512-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T20:44:17Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:20:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-512-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-512-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-infilling-seed-2
|
anas-awadalla
| 2022-10-08T20:28:26Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:19:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-128-finetuned-squad-infilling-seed-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. -->
# t5-base-few-shot-k-128-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T20:16:51Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T20:07:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-128-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-128-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-256-finetuned-squad-infilling-seed-2
|
anas-awadalla
| 2022-10-08T19:56:11Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:48:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-256-finetuned-squad-infilling-seed-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-few-shot-k-256-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-256-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T19:46:30Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:37:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-256-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-256-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-64-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T19:42:01Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:33:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-64-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-64-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-128-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T19:35:59Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:31:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-128-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-128-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-32-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T19:31:22Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:22:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-32-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-32-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-128-finetuned-squad-infilling-seed-2
|
anas-awadalla
| 2022-10-08T19:29:16Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:24:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-128-finetuned-squad-infilling-seed-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-few-shot-k-128-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-small-finetuned-squad-infilling-lr-5e-5
|
anas-awadalla
| 2022-10-08T19:15:51Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:49:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-finetuned-squad-infilling-lr-5e-5
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-squad-infilling-lr-5e-5
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 48
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-32-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T19:09:18Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T19:00:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-32-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-32-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-64-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T19:03:17Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:58:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-64-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-64-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-16-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T18:57:56Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:48:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-16-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-16-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-32-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T18:56:55Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:45:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-32-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-32-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-small-finetuned-squad-infilling-lr-1e-4
|
anas-awadalla
| 2022-10-08T18:31:58Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:06:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-finetuned-squad-infilling-lr-1e-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-squad-infilling-lr-1e-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 48
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-32-finetuned-squad-infilling-seed-0
|
anas-awadalla
| 2022-10-08T18:25:49Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T18:11:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-32-finetuned-squad-infilling-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-32-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-few-shot-k-16-finetuned-squad-infilling-seed-4
|
anas-awadalla
| 2022-10-08T18:09:10Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T17:54:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-16-finetuned-squad-infilling-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-16-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
diegofernandezc/intropln-setfit-model
|
diegofernandezc
| 2022-10-08T17:56:59Z | 14 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-08T15:55:57Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5683 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 5683,
"warmup_steps": 569,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(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 -->
|
anas-awadalla/t5-small-finetuned-squad-infilling-lr-3e-5
|
anas-awadalla
| 2022-10-08T17:55:38Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T17:23:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-finetuned-squad-infilling-lr-3e-5
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-squad-infilling-lr-3e-5
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 48
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
huggingtweets/uneventual
|
huggingtweets
| 2022-10-08T17:41:26Z | 128 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-08T17:40:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/uneventual/1665250882179/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/1558240262190764032/oj46u7bD_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">lucy 🐧</div>
<div style="text-align: center; font-size: 14px;">@uneventual</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 lucy 🐧.
| Data | lucy 🐧 |
| --- | --- |
| Tweets downloaded | 2876 |
| Retweets | 175 |
| Short tweets | 362 |
| Tweets kept | 2339 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oviz9fj/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 @uneventual's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37fweuku) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37fweuku/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/uneventual')
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)
|
monakth/bert-base-uncased-finetuned-squad
|
monakth
| 2022-10-08T17:18:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-06T00:16:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-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. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0964
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0664 | 1.0 | 5533 | 1.0170 |
| 0.7946 | 2.0 | 11066 | 1.0367 |
| 0.5758 | 3.0 | 16599 | 1.0964 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
sd-concepts-library/slm
|
sd-concepts-library
| 2022-10-08T17:04:50Z | 0 | 5 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-08T17:04:46Z |
---
license: mit
---
### slm on Stable Diffusion
This is the `<c-w388>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
tommctdhi/distilbert-base-uncased-finetuned-imdb
|
tommctdhi
| 2022-10-08T17:02:14Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-07T21:12:27Z |
---
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: 2.4442
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6985 | 1.0 | 157 | 2.5612 |
| 2.562 | 2.0 | 314 | 2.4226 |
| 2.5316 | 3.0 | 471 | 2.4218 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1
|
anas-awadalla/bart-large-finetuned-squad-infilling-lr-5e-6-decay-01
|
anas-awadalla
| 2022-10-08T16:27:25Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T15:05:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-5e-6-decay-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-squad-infilling-lr-5e-6-decay-01
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Shaier/BERT_MC_OpenBookQA_from_scratch
|
Shaier
| 2022-10-08T16:21:42Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-10-08T00:57:12Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT_MC_OpenBookQA_from_scratch
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_MC_OpenBookQA_from_scratch
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3863
- Accuracy: 0.268
## 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: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:------:|:---------------:|:--------:|
| No log | 1.0 | 310 | 1.2995 | 0.366 |
| 1.206 | 2.0 | 620 | 1.2607 | 0.462 |
| 1.206 | 3.0 | 930 | 1.2848 | 0.474 |
| 0.9058 | 4.0 | 1240 | 1.6050 | 0.472 |
| 0.6435 | 5.0 | 1550 | 1.5843 | 0.47 |
| 0.6435 | 6.0 | 1860 | 2.1529 | 0.452 |
| 0.444 | 7.0 | 2170 | 2.7774 | 0.444 |
| 0.444 | 8.0 | 2480 | 2.0983 | 0.454 |
| 0.3005 | 9.0 | 2790 | 2.8257 | 0.46 |
| 0.1875 | 10.0 | 3100 | 4.0687 | 0.45 |
| 0.1875 | 11.0 | 3410 | 4.8267 | 0.424 |
| 0.1508 | 12.0 | 3720 | 4.2751 | 0.426 |
| 0.1015 | 13.0 | 4030 | 5.0416 | 0.44 |
| 0.1015 | 14.0 | 4340 | 6.0999 | 0.436 |
| 0.0758 | 15.0 | 4650 | 5.8698 | 0.44 |
| 0.0758 | 16.0 | 4960 | 4.6333 | 0.426 |
| 0.0609 | 17.0 | 5270 | 6.0075 | 0.398 |
| 0.0415 | 18.0 | 5580 | 6.3479 | 0.392 |
| 0.0415 | 19.0 | 5890 | 5.7519 | 0.412 |
| 0.0451 | 20.0 | 6200 | 6.7130 | 0.412 |
| 0.0273 | 21.0 | 6510 | 5.2695 | 0.414 |
| 0.0273 | 22.0 | 6820 | 7.4730 | 0.414 |
| 0.0288 | 23.0 | 7130 | 5.4541 | 0.42 |
| 0.0288 | 24.0 | 7440 | 6.4990 | 0.418 |
| 0.0211 | 25.0 | 7750 | 8.1350 | 0.428 |
| 0.0276 | 26.0 | 8060 | 7.2577 | 0.41 |
| 0.0276 | 27.0 | 8370 | 4.0796 | 0.232 |
| 0.3193 | 28.0 | 8680 | 5.9266 | 0.412 |
| 0.3193 | 29.0 | 8990 | 7.7326 | 0.396 |
| 0.34 | 30.0 | 9300 | 5.6181 | 0.418 |
| 0.0298 | 31.0 | 9610 | 8.2390 | 0.43 |
| 0.0298 | 32.0 | 9920 | 7.7127 | 0.408 |
| 0.039 | 33.0 | 10230 | 5.9863 | 0.438 |
| 0.0262 | 34.0 | 10540 | 7.2023 | 0.44 |
| 0.0262 | 35.0 | 10850 | 7.0562 | 0.43 |
| 0.0136 | 36.0 | 11160 | 8.2601 | 0.426 |
| 0.0136 | 37.0 | 11470 | 7.8976 | 0.428 |
| 0.0104 | 38.0 | 11780 | 6.1737 | 0.428 |
| 0.0094 | 39.0 | 12090 | 7.8364 | 0.42 |
| 0.0094 | 40.0 | 12400 | 6.5378 | 0.43 |
| 0.099 | 41.0 | 12710 | 6.8616 | 0.412 |
| 0.0195 | 42.0 | 13020 | 7.3664 | 0.418 |
| 0.0195 | 43.0 | 13330 | 7.5156 | 0.416 |
| 0.0145 | 44.0 | 13640 | 7.0715 | 0.408 |
| 0.0145 | 45.0 | 13950 | 8.1128 | 0.41 |
| 0.0143 | 46.0 | 14260 | 8.5357 | 0.424 |
| 0.0126 | 47.0 | 14570 | 8.8933 | 0.412 |
| 0.0126 | 48.0 | 14880 | 6.4196 | 0.42 |
| 0.0073 | 49.0 | 15190 | 6.6502 | 0.402 |
| 0.0314 | 50.0 | 15500 | 6.1884 | 0.368 |
| 0.0314 | 51.0 | 15810 | 8.1288 | 0.392 |
| 0.0544 | 52.0 | 16120 | 5.5377 | 0.372 |
| 0.0544 | 53.0 | 16430 | 6.7587 | 0.402 |
| 0.0366 | 54.0 | 16740 | 6.8984 | 0.396 |
| 0.0168 | 55.0 | 17050 | 6.9548 | 0.398 |
| 0.0168 | 56.0 | 17360 | 8.0131 | 0.402 |
| 0.0117 | 57.0 | 17670 | 8.2451 | 0.412 |
| 0.0117 | 58.0 | 17980 | 6.8791 | 0.4 |
| 0.0077 | 59.0 | 18290 | 7.4470 | 0.388 |
| 0.0194 | 60.0 | 18600 | 6.5445 | 0.402 |
| 0.0194 | 61.0 | 18910 | 7.1259 | 0.402 |
| 0.013 | 62.0 | 19220 | 6.5935 | 0.414 |
| 0.0116 | 63.0 | 19530 | 6.0180 | 0.398 |
| 0.0116 | 64.0 | 19840 | 7.2279 | 0.414 |
| 0.2207 | 65.0 | 20150 | 1.3863 | 0.33 |
| 0.2207 | 66.0 | 20460 | 1.3863 | 0.276 |
| 1.4004 | 67.0 | 20770 | 1.3863 | 0.27 |
| 1.4211 | 68.0 | 21080 | 1.3863 | 0.312 |
| 1.4211 | 69.0 | 21390 | 1.3863 | 0.276 |
| 1.4202 | 70.0 | 21700 | 1.3863 | 0.282 |
| 1.4147 | 71.0 | 22010 | 1.3863 | 0.288 |
| 1.4147 | 72.0 | 22320 | 1.3863 | 0.298 |
| 1.4096 | 73.0 | 22630 | 1.3863 | 0.302 |
| 1.4096 | 74.0 | 22940 | 1.3863 | 0.262 |
| 1.4083 | 75.0 | 23250 | 1.3863 | 0.296 |
| 1.4116 | 76.0 | 23560 | 1.3863 | 0.288 |
| 1.4116 | 77.0 | 23870 | 1.3863 | 0.294 |
| 1.408 | 78.0 | 24180 | 1.3863 | 0.252 |
| 1.408 | 79.0 | 24490 | 1.3863 | 0.284 |
| 1.4093 | 80.0 | 24800 | 1.3863 | 0.3 |
| 1.4103 | 81.0 | 25110 | 1.3863 | 0.274 |
| 1.4103 | 82.0 | 25420 | 1.3863 | 0.276 |
| 1.4062 | 83.0 | 25730 | 1.3863 | 0.28 |
| 1.4111 | 84.0 | 26040 | 1.3863 | 0.304 |
| 1.4111 | 85.0 | 26350 | 1.3863 | 0.338 |
| 1.4036 | 86.0 | 26660 | 1.3863 | 0.314 |
| 1.4036 | 87.0 | 26970 | 1.3863 | 0.278 |
| 1.4272 | 88.0 | 27280 | 1.3863 | 0.278 |
| 1.404 | 89.0 | 27590 | 1.3863 | 0.276 |
| 1.404 | 90.0 | 27900 | 1.3863 | 0.274 |
| 1.4004 | 91.0 | 28210 | 1.3863 | 0.276 |
| 1.4017 | 92.0 | 28520 | 1.3863 | 0.276 |
| 1.4017 | 93.0 | 28830 | 1.3863 | 0.276 |
| 1.4009 | 94.0 | 29140 | 1.3863 | 0.284 |
| 1.4009 | 95.0 | 29450 | 1.3863 | 0.284 |
| 1.3997 | 96.0 | 29760 | 1.3863 | 0.286 |
| 1.399 | 97.0 | 30070 | 1.3863 | 0.264 |
| 1.399 | 98.0 | 30380 | 1.3863 | 0.278 |
| 1.399 | 99.0 | 30690 | 1.3863 | 0.276 |
| 1.4002 | 100.0 | 31000 | 1.3863 | 0.276 |
| 1.4002 | 101.0 | 31310 | 1.3863 | 0.276 |
| 1.4013 | 102.0 | 31620 | 1.3863 | 0.276 |
| 1.4013 | 103.0 | 31930 | 1.3863 | 0.276 |
| 1.3984 | 104.0 | 32240 | 1.3863 | 0.276 |
| 1.3997 | 105.0 | 32550 | 1.3863 | 0.288 |
| 1.3997 | 106.0 | 32860 | 1.3863 | 0.276 |
| 1.3951 | 107.0 | 33170 | 1.3863 | 0.276 |
| 1.3951 | 108.0 | 33480 | 1.3863 | 0.262 |
| 1.3953 | 109.0 | 33790 | 1.3863 | 0.27 |
| 1.3936 | 110.0 | 34100 | 1.3863 | 0.26 |
| 1.3936 | 111.0 | 34410 | 1.3863 | 0.276 |
| 1.3937 | 112.0 | 34720 | 1.3863 | 0.278 |
| 1.3925 | 113.0 | 35030 | 1.3863 | 0.282 |
| 1.3925 | 114.0 | 35340 | 1.3863 | 0.276 |
| 1.3959 | 115.0 | 35650 | 1.3863 | 0.28 |
| 1.3959 | 116.0 | 35960 | 1.3863 | 0.276 |
| 1.393 | 117.0 | 36270 | 1.3863 | 0.282 |
| 1.3922 | 118.0 | 36580 | 1.3863 | 0.27 |
| 1.3922 | 119.0 | 36890 | 1.3863 | 0.256 |
| 1.392 | 120.0 | 37200 | 1.3863 | 0.276 |
| 1.3936 | 121.0 | 37510 | 1.3863 | 0.252 |
| 1.3936 | 122.0 | 37820 | 1.3863 | 0.276 |
| 1.394 | 123.0 | 38130 | 1.3863 | 0.276 |
| 1.394 | 124.0 | 38440 | 1.3863 | 0.276 |
| 1.3939 | 125.0 | 38750 | 1.3863 | 0.276 |
| 1.3922 | 126.0 | 39060 | 1.3863 | 0.276 |
| 1.3922 | 127.0 | 39370 | 1.3863 | 0.26 |
| 1.3899 | 128.0 | 39680 | 1.3863 | 0.276 |
| 1.3899 | 129.0 | 39990 | 1.3863 | 0.272 |
| 1.3909 | 130.0 | 40300 | 1.3863 | 0.276 |
| 1.3912 | 131.0 | 40610 | 1.3863 | 0.276 |
| 1.3912 | 132.0 | 40920 | 1.3863 | 0.276 |
| 1.3907 | 133.0 | 41230 | 1.3863 | 0.286 |
| 1.39 | 134.0 | 41540 | 1.3863 | 0.276 |
| 1.39 | 135.0 | 41850 | 1.3863 | 0.276 |
| 1.3919 | 136.0 | 42160 | 1.3863 | 0.294 |
| 1.3919 | 137.0 | 42470 | 1.3863 | 0.268 |
| 1.3887 | 138.0 | 42780 | 1.3863 | 0.276 |
| 1.3897 | 139.0 | 43090 | 1.3863 | 0.276 |
| 1.3897 | 140.0 | 43400 | 1.3863 | 0.276 |
| 1.3899 | 141.0 | 43710 | 1.3863 | 0.274 |
| 1.3911 | 142.0 | 44020 | 1.3863 | 0.276 |
| 1.3911 | 143.0 | 44330 | 1.3863 | 0.286 |
| 1.3901 | 144.0 | 44640 | 1.3863 | 0.276 |
| 1.3901 | 145.0 | 44950 | 1.3863 | 0.264 |
| 1.3883 | 146.0 | 45260 | 1.3863 | 0.26 |
| 1.3906 | 147.0 | 45570 | 1.3863 | 0.276 |
| 1.3906 | 148.0 | 45880 | 1.3863 | 0.276 |
| 1.39 | 149.0 | 46190 | 1.3863 | 0.274 |
| 1.3889 | 150.0 | 46500 | 1.3863 | 0.276 |
| 1.3889 | 151.0 | 46810 | 1.3863 | 0.26 |
| 1.39 | 152.0 | 47120 | 1.3863 | 0.266 |
| 1.39 | 153.0 | 47430 | 1.3863 | 0.278 |
| 1.3889 | 154.0 | 47740 | 1.3863 | 0.284 |
| 1.3901 | 155.0 | 48050 | 1.3863 | 0.268 |
| 1.3901 | 156.0 | 48360 | 1.3863 | 0.27 |
| 1.4048 | 157.0 | 48670 | 1.3863 | 0.284 |
| 1.4048 | 158.0 | 48980 | 1.3863 | 0.262 |
| 1.3883 | 159.0 | 49290 | 1.3863 | 0.272 |
| 1.389 | 160.0 | 49600 | 1.3863 | 0.276 |
| 1.389 | 161.0 | 49910 | 1.3863 | 0.276 |
| 1.3891 | 162.0 | 50220 | 1.3863 | 0.266 |
| 1.3868 | 163.0 | 50530 | 1.3863 | 0.296 |
| 1.3868 | 164.0 | 50840 | 1.3863 | 0.276 |
| 1.3886 | 165.0 | 51150 | 1.3863 | 0.276 |
| 1.3886 | 166.0 | 51460 | 1.3863 | 0.276 |
| 1.388 | 167.0 | 51770 | 1.3863 | 0.276 |
| 1.3888 | 168.0 | 52080 | 1.3863 | 0.276 |
| 1.3888 | 169.0 | 52390 | 1.3863 | 0.276 |
| 1.3879 | 170.0 | 52700 | 1.3863 | 0.288 |
| 1.3883 | 171.0 | 53010 | 1.3863 | 0.298 |
| 1.3883 | 172.0 | 53320 | 1.3863 | 0.276 |
| 1.3885 | 173.0 | 53630 | 1.3863 | 0.274 |
| 1.3885 | 174.0 | 53940 | 1.3863 | 0.26 |
| 1.3874 | 175.0 | 54250 | 1.3863 | 0.276 |
| 1.3873 | 176.0 | 54560 | 1.3863 | 0.272 |
| 1.3873 | 177.0 | 54870 | 1.3863 | 0.272 |
| 1.3886 | 178.0 | 55180 | 1.3863 | 0.276 |
| 1.3886 | 179.0 | 55490 | 1.3863 | 0.276 |
| 1.3869 | 180.0 | 55800 | 1.3863 | 0.276 |
| 1.3872 | 181.0 | 56110 | 1.3863 | 0.276 |
| 1.3872 | 182.0 | 56420 | 1.3863 | 0.278 |
| 1.3876 | 183.0 | 56730 | 1.3863 | 0.274 |
| 1.3877 | 184.0 | 57040 | 1.3863 | 0.276 |
| 1.3877 | 185.0 | 57350 | 1.3863 | 0.276 |
| 1.3873 | 186.0 | 57660 | 1.3863 | 0.276 |
| 1.3873 | 187.0 | 57970 | 1.3863 | 0.276 |
| 1.3871 | 188.0 | 58280 | 1.3863 | 0.276 |
| 1.3869 | 189.0 | 58590 | 1.3863 | 0.284 |
| 1.3869 | 190.0 | 58900 | 1.3863 | 0.272 |
| 1.387 | 191.0 | 59210 | 1.3863 | 0.266 |
| 1.3875 | 192.0 | 59520 | 1.3863 | 0.276 |
| 1.3875 | 193.0 | 59830 | 1.3863 | 0.278 |
| 1.3871 | 194.0 | 60140 | 1.3863 | 0.276 |
| 1.3871 | 195.0 | 60450 | 1.3863 | 0.276 |
| 1.3878 | 196.0 | 60760 | 1.3863 | 0.27 |
| 1.3875 | 197.0 | 61070 | 1.3863 | 0.258 |
| 1.3875 | 198.0 | 61380 | 1.3863 | 0.272 |
| 1.3878 | 199.0 | 61690 | 1.3863 | 0.258 |
| 1.3871 | 200.0 | 62000 | 1.3863 | 0.276 |
| 1.3871 | 201.0 | 62310 | 1.3863 | 0.278 |
| 1.3874 | 202.0 | 62620 | 1.3863 | 0.262 |
| 1.3874 | 203.0 | 62930 | 1.3863 | 0.276 |
| 1.3871 | 204.0 | 63240 | 1.3863 | 0.278 |
| 1.3876 | 205.0 | 63550 | 1.3863 | 0.274 |
| 1.3876 | 206.0 | 63860 | 1.3863 | 0.276 |
| 1.387 | 207.0 | 64170 | 1.3863 | 0.276 |
| 1.387 | 208.0 | 64480 | 1.3863 | 0.258 |
| 1.3884 | 209.0 | 64790 | 1.3863 | 0.266 |
| 1.3869 | 210.0 | 65100 | 1.3863 | 0.276 |
| 1.3869 | 211.0 | 65410 | 1.3863 | 0.276 |
| 1.3881 | 212.0 | 65720 | 1.3863 | 0.28 |
| 1.3878 | 213.0 | 66030 | 1.3863 | 0.27 |
| 1.3878 | 214.0 | 66340 | 1.3863 | 0.276 |
| 1.3871 | 215.0 | 66650 | 1.3863 | 0.276 |
| 1.3871 | 216.0 | 66960 | 1.3863 | 0.276 |
| 1.3869 | 217.0 | 67270 | 1.3863 | 0.276 |
| 1.3874 | 218.0 | 67580 | 1.3863 | 0.276 |
| 1.3874 | 219.0 | 67890 | 1.3863 | 0.276 |
| 1.3876 | 220.0 | 68200 | 1.3863 | 0.276 |
| 1.3871 | 221.0 | 68510 | 1.3863 | 0.276 |
| 1.3871 | 222.0 | 68820 | 1.3863 | 0.276 |
| 1.3869 | 223.0 | 69130 | 1.3863 | 0.276 |
| 1.3869 | 224.0 | 69440 | 1.3863 | 0.276 |
| 1.3867 | 225.0 | 69750 | 1.3863 | 0.266 |
| 1.3874 | 226.0 | 70060 | 1.3863 | 0.262 |
| 1.3874 | 227.0 | 70370 | 1.3863 | 0.272 |
| 1.3869 | 228.0 | 70680 | 1.3863 | 0.274 |
| 1.3869 | 229.0 | 70990 | 1.3863 | 0.276 |
| 1.3865 | 230.0 | 71300 | 1.3863 | 0.29 |
| 1.3868 | 231.0 | 71610 | 1.3863 | 0.274 |
| 1.3868 | 232.0 | 71920 | 1.3863 | 0.264 |
| 1.3868 | 233.0 | 72230 | 1.3863 | 0.276 |
| 1.3868 | 234.0 | 72540 | 1.3863 | 0.276 |
| 1.3868 | 235.0 | 72850 | 1.3863 | 0.276 |
| 1.3867 | 236.0 | 73160 | 1.3863 | 0.284 |
| 1.3867 | 237.0 | 73470 | 1.3863 | 0.276 |
| 1.3878 | 238.0 | 73780 | 1.3863 | 0.276 |
| 1.3871 | 239.0 | 74090 | 1.3863 | 0.276 |
| 1.3871 | 240.0 | 74400 | 1.3863 | 0.276 |
| 1.387 | 241.0 | 74710 | 1.3863 | 0.264 |
| 1.3874 | 242.0 | 75020 | 1.3863 | 0.264 |
| 1.3874 | 243.0 | 75330 | 1.3863 | 0.276 |
| 1.3872 | 244.0 | 75640 | 1.3863 | 0.276 |
| 1.3872 | 245.0 | 75950 | 1.3863 | 0.276 |
| 1.3875 | 246.0 | 76260 | 1.3863 | 0.276 |
| 1.3873 | 247.0 | 76570 | 1.3863 | 0.276 |
| 1.3873 | 248.0 | 76880 | 1.3863 | 0.28 |
| 1.3867 | 249.0 | 77190 | 1.3863 | 0.266 |
| 1.3871 | 250.0 | 77500 | 1.3863 | 0.276 |
| 1.3871 | 251.0 | 77810 | 1.3863 | 0.276 |
| 1.3876 | 252.0 | 78120 | 1.3863 | 0.276 |
| 1.3876 | 253.0 | 78430 | 1.3863 | 0.276 |
| 1.3871 | 254.0 | 78740 | 1.3863 | 0.246 |
| 1.3867 | 255.0 | 79050 | 1.3863 | 0.274 |
| 1.3867 | 256.0 | 79360 | 1.3863 | 0.252 |
| 1.3866 | 257.0 | 79670 | 1.3863 | 0.276 |
| 1.3866 | 258.0 | 79980 | 1.3863 | 0.276 |
| 1.3869 | 259.0 | 80290 | 1.3863 | 0.276 |
| 1.3868 | 260.0 | 80600 | 1.3863 | 0.278 |
| 1.3868 | 261.0 | 80910 | 1.3863 | 0.28 |
| 1.3867 | 262.0 | 81220 | 1.3863 | 0.29 |
| 1.3868 | 263.0 | 81530 | 1.3863 | 0.252 |
| 1.3868 | 264.0 | 81840 | 1.3863 | 0.258 |
| 1.3873 | 265.0 | 82150 | 1.3863 | 0.284 |
| 1.3873 | 266.0 | 82460 | 1.3863 | 0.266 |
| 1.3864 | 267.0 | 82770 | 1.3863 | 0.26 |
| 1.3874 | 268.0 | 83080 | 1.3863 | 0.276 |
| 1.3874 | 269.0 | 83390 | 1.3863 | 0.276 |
| 1.3875 | 270.0 | 83700 | 1.3863 | 0.276 |
| 1.3872 | 271.0 | 84010 | 1.3863 | 0.256 |
| 1.3872 | 272.0 | 84320 | 1.3863 | 0.26 |
| 1.3864 | 273.0 | 84630 | 1.3863 | 0.272 |
| 1.3864 | 274.0 | 84940 | 1.3863 | 0.242 |
| 1.3868 | 275.0 | 85250 | 1.3863 | 0.276 |
| 1.3871 | 276.0 | 85560 | 1.3863 | 0.276 |
| 1.3871 | 277.0 | 85870 | 1.3863 | 0.28 |
| 1.3871 | 278.0 | 86180 | 1.3863 | 0.276 |
| 1.3871 | 279.0 | 86490 | 1.3863 | 0.27 |
| 1.387 | 280.0 | 86800 | 1.3863 | 0.256 |
| 1.3864 | 281.0 | 87110 | 1.3863 | 0.276 |
| 1.3864 | 282.0 | 87420 | 1.3863 | 0.278 |
| 1.3867 | 283.0 | 87730 | 1.3863 | 0.276 |
| 1.3871 | 284.0 | 88040 | 1.3863 | 0.254 |
| 1.3871 | 285.0 | 88350 | 1.3863 | 0.276 |
| 1.3868 | 286.0 | 88660 | 1.3863 | 0.276 |
| 1.3868 | 287.0 | 88970 | 1.3863 | 0.268 |
| 1.3871 | 288.0 | 89280 | 1.3863 | 0.282 |
| 1.3863 | 289.0 | 89590 | 1.3863 | 0.28 |
| 1.3863 | 290.0 | 89900 | 1.3863 | 0.276 |
| 1.3874 | 291.0 | 90210 | 1.3863 | 0.272 |
| 1.3869 | 292.0 | 90520 | 1.3863 | 0.27 |
| 1.3869 | 293.0 | 90830 | 1.3863 | 0.27 |
| 1.3865 | 294.0 | 91140 | 1.3863 | 0.27 |
| 1.3865 | 295.0 | 91450 | 1.3863 | 0.276 |
| 1.387 | 296.0 | 91760 | 1.3863 | 0.288 |
| 1.3868 | 297.0 | 92070 | 1.3863 | 0.268 |
| 1.3868 | 298.0 | 92380 | 1.3863 | 0.268 |
| 1.387 | 299.0 | 92690 | 1.3863 | 0.276 |
| 1.3869 | 300.0 | 93000 | 1.3863 | 0.258 |
| 1.3869 | 301.0 | 93310 | 1.3863 | 0.276 |
| 1.387 | 302.0 | 93620 | 1.3863 | 0.276 |
| 1.387 | 303.0 | 93930 | 1.3863 | 0.272 |
| 1.3869 | 304.0 | 94240 | 1.3863 | 0.276 |
| 1.3862 | 305.0 | 94550 | 1.3863 | 0.268 |
| 1.3862 | 306.0 | 94860 | 1.3863 | 0.26 |
| 1.387 | 307.0 | 95170 | 1.3863 | 0.268 |
| 1.387 | 308.0 | 95480 | 1.3863 | 0.262 |
| 1.3868 | 309.0 | 95790 | 1.3863 | 0.264 |
| 1.3869 | 310.0 | 96100 | 1.3863 | 0.276 |
| 1.3869 | 311.0 | 96410 | 1.3863 | 0.264 |
| 1.387 | 312.0 | 96720 | 1.3863 | 0.28 |
| 1.3869 | 313.0 | 97030 | 1.3863 | 0.266 |
| 1.3869 | 314.0 | 97340 | 1.3863 | 0.276 |
| 1.3866 | 315.0 | 97650 | 1.3863 | 0.288 |
| 1.3866 | 316.0 | 97960 | 1.3863 | 0.272 |
| 1.3868 | 317.0 | 98270 | 1.3863 | 0.266 |
| 1.3866 | 318.0 | 98580 | 1.3863 | 0.286 |
| 1.3866 | 319.0 | 98890 | 1.3863 | 0.272 |
| 1.3865 | 320.0 | 99200 | 1.3863 | 0.278 |
| 1.3872 | 321.0 | 99510 | 1.3863 | 0.276 |
| 1.3872 | 322.0 | 99820 | 1.3863 | 0.272 |
| 1.3863 | 323.0 | 100130 | 1.3863 | 0.258 |
| 1.3863 | 324.0 | 100440 | 1.3863 | 0.282 |
| 1.3867 | 325.0 | 100750 | 1.3863 | 0.254 |
| 1.3867 | 326.0 | 101060 | 1.3863 | 0.29 |
| 1.3867 | 327.0 | 101370 | 1.3863 | 0.238 |
| 1.3874 | 328.0 | 101680 | 1.3863 | 0.276 |
| 1.3874 | 329.0 | 101990 | 1.3863 | 0.276 |
| 1.3866 | 330.0 | 102300 | 1.3863 | 0.268 |
| 1.3869 | 331.0 | 102610 | 1.3863 | 0.266 |
| 1.3869 | 332.0 | 102920 | 1.3863 | 0.274 |
| 1.387 | 333.0 | 103230 | 1.3863 | 0.282 |
| 1.3866 | 334.0 | 103540 | 1.3863 | 0.286 |
| 1.3866 | 335.0 | 103850 | 1.3863 | 0.262 |
| 1.3874 | 336.0 | 104160 | 1.3863 | 0.274 |
| 1.3874 | 337.0 | 104470 | 1.3863 | 0.26 |
| 1.3868 | 338.0 | 104780 | 1.3863 | 0.258 |
| 1.3871 | 339.0 | 105090 | 1.3863 | 0.272 |
| 1.3871 | 340.0 | 105400 | 1.3863 | 0.276 |
| 1.3871 | 341.0 | 105710 | 1.3863 | 0.282 |
| 1.3868 | 342.0 | 106020 | 1.3863 | 0.288 |
| 1.3868 | 343.0 | 106330 | 1.3863 | 0.266 |
| 1.3868 | 344.0 | 106640 | 1.3863 | 0.28 |
| 1.3868 | 345.0 | 106950 | 1.3863 | 0.292 |
| 1.3869 | 346.0 | 107260 | 1.3863 | 0.282 |
| 1.3864 | 347.0 | 107570 | 1.3863 | 0.286 |
| 1.3864 | 348.0 | 107880 | 1.3863 | 0.254 |
| 1.3871 | 349.0 | 108190 | 1.3863 | 0.254 |
| 1.3869 | 350.0 | 108500 | 1.3863 | 0.258 |
| 1.3869 | 351.0 | 108810 | 1.3863 | 0.286 |
| 1.3864 | 352.0 | 109120 | 1.3863 | 0.248 |
| 1.3864 | 353.0 | 109430 | 1.3863 | 0.276 |
| 1.3863 | 354.0 | 109740 | 1.3863 | 0.294 |
| 1.3872 | 355.0 | 110050 | 1.3863 | 0.25 |
| 1.3872 | 356.0 | 110360 | 1.3863 | 0.282 |
| 1.3865 | 357.0 | 110670 | 1.3863 | 0.25 |
| 1.3865 | 358.0 | 110980 | 1.3863 | 0.29 |
| 1.3872 | 359.0 | 111290 | 1.3863 | 0.274 |
| 1.3871 | 360.0 | 111600 | 1.3863 | 0.272 |
| 1.3871 | 361.0 | 111910 | 1.3863 | 0.282 |
| 1.3865 | 362.0 | 112220 | 1.3863 | 0.276 |
| 1.3865 | 363.0 | 112530 | 1.3863 | 0.276 |
| 1.3865 | 364.0 | 112840 | 1.3863 | 0.268 |
| 1.3867 | 365.0 | 113150 | 1.3863 | 0.262 |
| 1.3867 | 366.0 | 113460 | 1.3863 | 0.28 |
| 1.3865 | 367.0 | 113770 | 1.3863 | 0.296 |
| 1.387 | 368.0 | 114080 | 1.3863 | 0.286 |
| 1.387 | 369.0 | 114390 | 1.3863 | 0.28 |
| 1.3865 | 370.0 | 114700 | 1.3863 | 0.276 |
| 1.3865 | 371.0 | 115010 | 1.3863 | 0.278 |
| 1.3865 | 372.0 | 115320 | 1.3863 | 0.26 |
| 1.3867 | 373.0 | 115630 | 1.3863 | 0.294 |
| 1.3867 | 374.0 | 115940 | 1.3863 | 0.278 |
| 1.3868 | 375.0 | 116250 | 1.3863 | 0.272 |
| 1.3871 | 376.0 | 116560 | 1.3863 | 0.268 |
| 1.3871 | 377.0 | 116870 | 1.3863 | 0.272 |
| 1.3868 | 378.0 | 117180 | 1.3863 | 0.268 |
| 1.3868 | 379.0 | 117490 | 1.3863 | 0.276 |
| 1.3867 | 380.0 | 117800 | 1.3863 | 0.27 |
| 1.3864 | 381.0 | 118110 | 1.3863 | 0.264 |
| 1.3864 | 382.0 | 118420 | 1.3863 | 0.276 |
| 1.3869 | 383.0 | 118730 | 1.3863 | 0.276 |
| 1.3865 | 384.0 | 119040 | 1.3863 | 0.254 |
| 1.3865 | 385.0 | 119350 | 1.3863 | 0.276 |
| 1.3869 | 386.0 | 119660 | 1.3863 | 0.284 |
| 1.3869 | 387.0 | 119970 | 1.3863 | 0.28 |
| 1.3872 | 388.0 | 120280 | 1.3863 | 0.278 |
| 1.3873 | 389.0 | 120590 | 1.3863 | 0.276 |
| 1.3873 | 390.0 | 120900 | 1.3863 | 0.276 |
| 1.3868 | 391.0 | 121210 | 1.3863 | 0.276 |
| 1.3866 | 392.0 | 121520 | 1.3863 | 0.256 |
| 1.3866 | 393.0 | 121830 | 1.3863 | 0.274 |
| 1.3873 | 394.0 | 122140 | 1.3863 | 0.286 |
| 1.3873 | 395.0 | 122450 | 1.3863 | 0.276 |
| 1.387 | 396.0 | 122760 | 1.3863 | 0.234 |
| 1.3866 | 397.0 | 123070 | 1.3863 | 0.266 |
| 1.3866 | 398.0 | 123380 | 1.3863 | 0.294 |
| 1.3868 | 399.0 | 123690 | 1.3863 | 0.254 |
| 1.3864 | 400.0 | 124000 | 1.3863 | 0.288 |
| 1.3864 | 401.0 | 124310 | 1.3863 | 0.26 |
| 1.3864 | 402.0 | 124620 | 1.3863 | 0.256 |
| 1.3864 | 403.0 | 124930 | 1.3863 | 0.25 |
| 1.3864 | 404.0 | 125240 | 1.3863 | 0.276 |
| 1.3867 | 405.0 | 125550 | 1.3863 | 0.258 |
| 1.3867 | 406.0 | 125860 | 1.3863 | 0.262 |
| 1.3867 | 407.0 | 126170 | 1.3863 | 0.278 |
| 1.3867 | 408.0 | 126480 | 1.3863 | 0.278 |
| 1.387 | 409.0 | 126790 | 1.3863 | 0.272 |
| 1.3865 | 410.0 | 127100 | 1.3863 | 0.28 |
| 1.3865 | 411.0 | 127410 | 1.3863 | 0.288 |
| 1.386 | 412.0 | 127720 | 1.3863 | 0.266 |
| 1.3867 | 413.0 | 128030 | 1.3863 | 0.252 |
| 1.3867 | 414.0 | 128340 | 1.3863 | 0.266 |
| 1.3865 | 415.0 | 128650 | 1.3863 | 0.264 |
| 1.3865 | 416.0 | 128960 | 1.3863 | 0.262 |
| 1.3867 | 417.0 | 129270 | 1.3863 | 0.28 |
| 1.3869 | 418.0 | 129580 | 1.3863 | 0.284 |
| 1.3869 | 419.0 | 129890 | 1.3863 | 0.264 |
| 1.3866 | 420.0 | 130200 | 1.3863 | 0.266 |
| 1.3869 | 421.0 | 130510 | 1.3863 | 0.274 |
| 1.3869 | 422.0 | 130820 | 1.3863 | 0.3 |
| 1.3865 | 423.0 | 131130 | 1.3863 | 0.266 |
| 1.3865 | 424.0 | 131440 | 1.3863 | 0.286 |
| 1.3872 | 425.0 | 131750 | 1.3863 | 0.264 |
| 1.3865 | 426.0 | 132060 | 1.3863 | 0.278 |
| 1.3865 | 427.0 | 132370 | 1.3863 | 0.27 |
| 1.3864 | 428.0 | 132680 | 1.3863 | 0.268 |
| 1.3864 | 429.0 | 132990 | 1.3863 | 0.304 |
| 1.3865 | 430.0 | 133300 | 1.3863 | 0.278 |
| 1.3865 | 431.0 | 133610 | 1.3863 | 0.278 |
| 1.3865 | 432.0 | 133920 | 1.3863 | 0.276 |
| 1.3867 | 433.0 | 134230 | 1.3863 | 0.286 |
| 1.3863 | 434.0 | 134540 | 1.3863 | 0.27 |
| 1.3863 | 435.0 | 134850 | 1.3863 | 0.28 |
| 1.3865 | 436.0 | 135160 | 1.3863 | 0.258 |
| 1.3865 | 437.0 | 135470 | 1.3863 | 0.248 |
| 1.3874 | 438.0 | 135780 | 1.3863 | 0.27 |
| 1.387 | 439.0 | 136090 | 1.3863 | 0.272 |
| 1.387 | 440.0 | 136400 | 1.3863 | 0.28 |
| 1.3869 | 441.0 | 136710 | 1.3863 | 0.28 |
| 1.3862 | 442.0 | 137020 | 1.3863 | 0.266 |
| 1.3862 | 443.0 | 137330 | 1.3863 | 0.282 |
| 1.3862 | 444.0 | 137640 | 1.3863 | 0.26 |
| 1.3862 | 445.0 | 137950 | 1.3863 | 0.288 |
| 1.3862 | 446.0 | 138260 | 1.3863 | 0.232 |
| 1.387 | 447.0 | 138570 | 1.3863 | 0.262 |
| 1.387 | 448.0 | 138880 | 1.3863 | 0.254 |
| 1.3865 | 449.0 | 139190 | 1.3863 | 0.29 |
| 1.3871 | 450.0 | 139500 | 1.3863 | 0.276 |
| 1.3871 | 451.0 | 139810 | 1.3863 | 0.26 |
| 1.3868 | 452.0 | 140120 | 1.3863 | 0.272 |
| 1.3868 | 453.0 | 140430 | 1.3863 | 0.268 |
| 1.3864 | 454.0 | 140740 | 1.3863 | 0.244 |
| 1.387 | 455.0 | 141050 | 1.3863 | 0.252 |
| 1.387 | 456.0 | 141360 | 1.3863 | 0.268 |
| 1.3871 | 457.0 | 141670 | 1.3863 | 0.278 |
| 1.3871 | 458.0 | 141980 | 1.3863 | 0.276 |
| 1.3873 | 459.0 | 142290 | 1.3863 | 0.27 |
| 1.3866 | 460.0 | 142600 | 1.3863 | 0.252 |
| 1.3866 | 461.0 | 142910 | 1.3863 | 0.266 |
| 1.3866 | 462.0 | 143220 | 1.3863 | 0.294 |
| 1.3862 | 463.0 | 143530 | 1.3863 | 0.254 |
| 1.3862 | 464.0 | 143840 | 1.3863 | 0.268 |
| 1.387 | 465.0 | 144150 | 1.3863 | 0.266 |
| 1.387 | 466.0 | 144460 | 1.3863 | 0.27 |
| 1.3869 | 467.0 | 144770 | 1.3863 | 0.254 |
| 1.3861 | 468.0 | 145080 | 1.3863 | 0.274 |
| 1.3861 | 469.0 | 145390 | 1.3863 | 0.26 |
| 1.3861 | 470.0 | 145700 | 1.3863 | 0.274 |
| 1.387 | 471.0 | 146010 | 1.3863 | 0.268 |
| 1.387 | 472.0 | 146320 | 1.3863 | 0.26 |
| 1.3872 | 473.0 | 146630 | 1.3863 | 0.274 |
| 1.3872 | 474.0 | 146940 | 1.3863 | 0.282 |
| 1.387 | 475.0 | 147250 | 1.3863 | 0.252 |
| 1.387 | 476.0 | 147560 | 1.3863 | 0.268 |
| 1.387 | 477.0 | 147870 | 1.3863 | 0.266 |
| 1.3865 | 478.0 | 148180 | 1.3863 | 0.258 |
| 1.3865 | 479.0 | 148490 | 1.3863 | 0.268 |
| 1.3865 | 480.0 | 148800 | 1.3863 | 0.268 |
| 1.3869 | 481.0 | 149110 | 1.3863 | 0.266 |
| 1.3869 | 482.0 | 149420 | 1.3863 | 0.278 |
| 1.387 | 483.0 | 149730 | 1.3863 | 0.292 |
| 1.3866 | 484.0 | 150040 | 1.3863 | 0.256 |
| 1.3866 | 485.0 | 150350 | 1.3863 | 0.278 |
| 1.3866 | 486.0 | 150660 | 1.3863 | 0.28 |
| 1.3866 | 487.0 | 150970 | 1.3863 | 0.262 |
| 1.3871 | 488.0 | 151280 | 1.3863 | 0.28 |
| 1.3864 | 489.0 | 151590 | 1.3863 | 0.278 |
| 1.3864 | 490.0 | 151900 | 1.3863 | 0.244 |
| 1.3867 | 491.0 | 152210 | 1.3863 | 0.29 |
| 1.3869 | 492.0 | 152520 | 1.3863 | 0.26 |
| 1.3869 | 493.0 | 152830 | 1.3863 | 0.274 |
| 1.3863 | 494.0 | 153140 | 1.3863 | 0.274 |
| 1.3863 | 495.0 | 153450 | 1.3863 | 0.27 |
| 1.3871 | 496.0 | 153760 | 1.3863 | 0.234 |
| 1.3868 | 497.0 | 154070 | 1.3863 | 0.246 |
| 1.3868 | 498.0 | 154380 | 1.3863 | 0.286 |
| 1.3867 | 499.0 | 154690 | 1.3863 | 0.274 |
| 1.3865 | 500.0 | 155000 | 1.3863 | 0.274 |
| 1.3865 | 501.0 | 155310 | 1.3863 | 0.282 |
| 1.3868 | 502.0 | 155620 | 1.3863 | 0.276 |
| 1.3868 | 503.0 | 155930 | 1.3863 | 0.266 |
| 1.3865 | 504.0 | 156240 | 1.3863 | 0.276 |
| 1.3864 | 505.0 | 156550 | 1.3863 | 0.292 |
| 1.3864 | 506.0 | 156860 | 1.3863 | 0.276 |
| 1.3872 | 507.0 | 157170 | 1.3863 | 0.292 |
| 1.3872 | 508.0 | 157480 | 1.3863 | 0.29 |
| 1.3861 | 509.0 | 157790 | 1.3863 | 0.274 |
| 1.3867 | 510.0 | 158100 | 1.3863 | 0.3 |
| 1.3867 | 511.0 | 158410 | 1.3863 | 0.276 |
| 1.3865 | 512.0 | 158720 | 1.3863 | 0.28 |
| 1.3861 | 513.0 | 159030 | 1.3863 | 0.276 |
| 1.3861 | 514.0 | 159340 | 1.3863 | 0.278 |
| 1.3869 | 515.0 | 159650 | 1.3863 | 0.274 |
| 1.3869 | 516.0 | 159960 | 1.3863 | 0.276 |
| 1.3868 | 517.0 | 160270 | 1.3863 | 0.294 |
| 1.3863 | 518.0 | 160580 | 1.3863 | 0.266 |
| 1.3863 | 519.0 | 160890 | 1.3863 | 0.29 |
| 1.3861 | 520.0 | 161200 | 1.3863 | 0.276 |
| 1.3869 | 521.0 | 161510 | 1.3863 | 0.27 |
| 1.3869 | 522.0 | 161820 | 1.3863 | 0.268 |
| 1.3866 | 523.0 | 162130 | 1.3863 | 0.278 |
| 1.3866 | 524.0 | 162440 | 1.3863 | 0.28 |
| 1.3866 | 525.0 | 162750 | 1.3863 | 0.272 |
| 1.3866 | 526.0 | 163060 | 1.3863 | 0.268 |
| 1.3866 | 527.0 | 163370 | 1.3863 | 0.278 |
| 1.3869 | 528.0 | 163680 | 1.3863 | 0.278 |
| 1.3869 | 529.0 | 163990 | 1.3863 | 0.27 |
| 1.3869 | 530.0 | 164300 | 1.3863 | 0.256 |
| 1.3868 | 531.0 | 164610 | 1.3863 | 0.258 |
| 1.3868 | 532.0 | 164920 | 1.3863 | 0.274 |
| 1.3865 | 533.0 | 165230 | 1.3863 | 0.276 |
| 1.3871 | 534.0 | 165540 | 1.3863 | 0.276 |
| 1.3871 | 535.0 | 165850 | 1.3863 | 0.278 |
| 1.3859 | 536.0 | 166160 | 1.3863 | 0.238 |
| 1.3859 | 537.0 | 166470 | 1.3863 | 0.278 |
| 1.3861 | 538.0 | 166780 | 1.3863 | 0.268 |
| 1.386 | 539.0 | 167090 | 1.3863 | 0.264 |
| 1.386 | 540.0 | 167400 | 1.3863 | 0.234 |
| 1.3865 | 541.0 | 167710 | 1.3863 | 0.262 |
| 1.3866 | 542.0 | 168020 | 1.3863 | 0.268 |
| 1.3866 | 543.0 | 168330 | 1.3863 | 0.276 |
| 1.3871 | 544.0 | 168640 | 1.3863 | 0.274 |
| 1.3871 | 545.0 | 168950 | 1.3863 | 0.268 |
| 1.3867 | 546.0 | 169260 | 1.3863 | 0.27 |
| 1.387 | 547.0 | 169570 | 1.3863 | 0.276 |
| 1.387 | 548.0 | 169880 | 1.3863 | 0.278 |
| 1.3861 | 549.0 | 170190 | 1.3863 | 0.28 |
| 1.3865 | 550.0 | 170500 | 1.3863 | 0.278 |
| 1.3865 | 551.0 | 170810 | 1.3863 | 0.272 |
| 1.387 | 552.0 | 171120 | 1.3863 | 0.278 |
| 1.387 | 553.0 | 171430 | 1.3863 | 0.276 |
| 1.387 | 554.0 | 171740 | 1.3863 | 0.28 |
| 1.3865 | 555.0 | 172050 | 1.3863 | 0.244 |
| 1.3865 | 556.0 | 172360 | 1.3863 | 0.276 |
| 1.3867 | 557.0 | 172670 | 1.3863 | 0.274 |
| 1.3867 | 558.0 | 172980 | 1.3863 | 0.274 |
| 1.387 | 559.0 | 173290 | 1.3863 | 0.276 |
| 1.3869 | 560.0 | 173600 | 1.3863 | 0.278 |
| 1.3869 | 561.0 | 173910 | 1.3863 | 0.272 |
| 1.3868 | 562.0 | 174220 | 1.3863 | 0.272 |
| 1.3865 | 563.0 | 174530 | 1.3863 | 0.278 |
| 1.3865 | 564.0 | 174840 | 1.3863 | 0.286 |
| 1.3864 | 565.0 | 175150 | 1.3863 | 0.276 |
| 1.3864 | 566.0 | 175460 | 1.3863 | 0.268 |
| 1.3862 | 567.0 | 175770 | 1.3863 | 0.276 |
| 1.3865 | 568.0 | 176080 | 1.3863 | 0.274 |
| 1.3865 | 569.0 | 176390 | 1.3863 | 0.278 |
| 1.3866 | 570.0 | 176700 | 1.3863 | 0.276 |
| 1.3868 | 571.0 | 177010 | 1.3863 | 0.28 |
| 1.3868 | 572.0 | 177320 | 1.3863 | 0.276 |
| 1.3866 | 573.0 | 177630 | 1.3863 | 0.276 |
| 1.3866 | 574.0 | 177940 | 1.3863 | 0.27 |
| 1.3868 | 575.0 | 178250 | 1.3863 | 0.238 |
| 1.3869 | 576.0 | 178560 | 1.3863 | 0.272 |
| 1.3869 | 577.0 | 178870 | 1.3863 | 0.276 |
| 1.3867 | 578.0 | 179180 | 1.3863 | 0.276 |
| 1.3867 | 579.0 | 179490 | 1.3863 | 0.28 |
| 1.3863 | 580.0 | 179800 | 1.3863 | 0.272 |
| 1.3863 | 581.0 | 180110 | 1.3863 | 0.246 |
| 1.3863 | 582.0 | 180420 | 1.3863 | 0.276 |
| 1.3865 | 583.0 | 180730 | 1.3863 | 0.278 |
| 1.3868 | 584.0 | 181040 | 1.3863 | 0.28 |
| 1.3868 | 585.0 | 181350 | 1.3863 | 0.276 |
| 1.387 | 586.0 | 181660 | 1.3863 | 0.284 |
| 1.387 | 587.0 | 181970 | 1.3863 | 0.266 |
| 1.387 | 588.0 | 182280 | 1.3863 | 0.276 |
| 1.3865 | 589.0 | 182590 | 1.3863 | 0.278 |
| 1.3865 | 590.0 | 182900 | 1.3863 | 0.262 |
| 1.3867 | 591.0 | 183210 | 1.3863 | 0.278 |
| 1.3868 | 592.0 | 183520 | 1.3863 | 0.292 |
| 1.3868 | 593.0 | 183830 | 1.3863 | 0.276 |
| 1.3866 | 594.0 | 184140 | 1.3863 | 0.282 |
| 1.3866 | 595.0 | 184450 | 1.3863 | 0.28 |
| 1.3868 | 596.0 | 184760 | 1.3863 | 0.276 |
| 1.3869 | 597.0 | 185070 | 1.3863 | 0.278 |
| 1.3869 | 598.0 | 185380 | 1.3863 | 0.278 |
| 1.3865 | 599.0 | 185690 | 1.3863 | 0.276 |
| 1.3869 | 600.0 | 186000 | 1.3863 | 0.264 |
| 1.3869 | 601.0 | 186310 | 1.3863 | 0.272 |
| 1.3864 | 602.0 | 186620 | 1.3863 | 0.276 |
| 1.3864 | 603.0 | 186930 | 1.3863 | 0.282 |
| 1.3867 | 604.0 | 187240 | 1.3863 | 0.252 |
| 1.3869 | 605.0 | 187550 | 1.3863 | 0.274 |
| 1.3869 | 606.0 | 187860 | 1.3863 | 0.286 |
| 1.3868 | 607.0 | 188170 | 1.3863 | 0.298 |
| 1.3868 | 608.0 | 188480 | 1.3863 | 0.264 |
| 1.3859 | 609.0 | 188790 | 1.3863 | 0.274 |
| 1.3867 | 610.0 | 189100 | 1.3863 | 0.274 |
| 1.3867 | 611.0 | 189410 | 1.3863 | 0.276 |
| 1.387 | 612.0 | 189720 | 1.3863 | 0.274 |
| 1.387 | 613.0 | 190030 | 1.3863 | 0.276 |
| 1.387 | 614.0 | 190340 | 1.3863 | 0.274 |
| 1.3867 | 615.0 | 190650 | 1.3863 | 0.29 |
| 1.3867 | 616.0 | 190960 | 1.3863 | 0.27 |
| 1.3861 | 617.0 | 191270 | 1.3863 | 0.276 |
| 1.3868 | 618.0 | 191580 | 1.3863 | 0.296 |
| 1.3868 | 619.0 | 191890 | 1.3863 | 0.28 |
| 1.3862 | 620.0 | 192200 | 1.3863 | 0.262 |
| 1.387 | 621.0 | 192510 | 1.3863 | 0.27 |
| 1.387 | 622.0 | 192820 | 1.3863 | 0.276 |
| 1.3867 | 623.0 | 193130 | 1.3863 | 0.282 |
| 1.3867 | 624.0 | 193440 | 1.3863 | 0.278 |
| 1.3867 | 625.0 | 193750 | 1.3863 | 0.274 |
| 1.3863 | 626.0 | 194060 | 1.3863 | 0.278 |
| 1.3863 | 627.0 | 194370 | 1.3863 | 0.282 |
| 1.387 | 628.0 | 194680 | 1.3863 | 0.274 |
| 1.387 | 629.0 | 194990 | 1.3863 | 0.276 |
| 1.3866 | 630.0 | 195300 | 1.3863 | 0.256 |
| 1.3867 | 631.0 | 195610 | 1.3863 | 0.28 |
| 1.3867 | 632.0 | 195920 | 1.3863 | 0.284 |
| 1.3864 | 633.0 | 196230 | 1.3863 | 0.256 |
| 1.3867 | 634.0 | 196540 | 1.3863 | 0.268 |
| 1.3867 | 635.0 | 196850 | 1.3863 | 0.282 |
| 1.3864 | 636.0 | 197160 | 1.3863 | 0.26 |
| 1.3864 | 637.0 | 197470 | 1.3863 | 0.266 |
| 1.3864 | 638.0 | 197780 | 1.3863 | 0.262 |
| 1.3866 | 639.0 | 198090 | 1.3863 | 0.276 |
| 1.3866 | 640.0 | 198400 | 1.3863 | 0.286 |
| 1.3864 | 641.0 | 198710 | 1.3863 | 0.264 |
| 1.3865 | 642.0 | 199020 | 1.3863 | 0.272 |
| 1.3865 | 643.0 | 199330 | 1.3863 | 0.276 |
| 1.3868 | 644.0 | 199640 | 1.3863 | 0.276 |
| 1.3868 | 645.0 | 199950 | 1.3863 | 0.268 |
| 1.3866 | 646.0 | 200260 | 1.3863 | 0.296 |
| 1.3868 | 647.0 | 200570 | 1.3863 | 0.264 |
| 1.3868 | 648.0 | 200880 | 1.3863 | 0.276 |
| 1.3866 | 649.0 | 201190 | 1.3863 | 0.28 |
| 1.3863 | 650.0 | 201500 | 1.3863 | 0.276 |
| 1.3863 | 651.0 | 201810 | 1.3863 | 0.288 |
| 1.3867 | 652.0 | 202120 | 1.3863 | 0.266 |
| 1.3867 | 653.0 | 202430 | 1.3863 | 0.27 |
| 1.3864 | 654.0 | 202740 | 1.3863 | 0.26 |
| 1.3865 | 655.0 | 203050 | 1.3863 | 0.276 |
| 1.3865 | 656.0 | 203360 | 1.3863 | 0.276 |
| 1.3865 | 657.0 | 203670 | 1.3863 | 0.278 |
| 1.3865 | 658.0 | 203980 | 1.3863 | 0.276 |
| 1.3866 | 659.0 | 204290 | 1.3863 | 0.276 |
| 1.3867 | 660.0 | 204600 | 1.3863 | 0.274 |
| 1.3867 | 661.0 | 204910 | 1.3863 | 0.282 |
| 1.3867 | 662.0 | 205220 | 1.3863 | 0.272 |
| 1.3861 | 663.0 | 205530 | 1.3863 | 0.276 |
| 1.3861 | 664.0 | 205840 | 1.3863 | 0.26 |
| 1.3863 | 665.0 | 206150 | 1.3863 | 0.276 |
| 1.3863 | 666.0 | 206460 | 1.3863 | 0.276 |
| 1.3865 | 667.0 | 206770 | 1.3863 | 0.272 |
| 1.3866 | 668.0 | 207080 | 1.3863 | 0.262 |
| 1.3866 | 669.0 | 207390 | 1.3863 | 0.286 |
| 1.3862 | 670.0 | 207700 | 1.3863 | 0.276 |
| 1.3866 | 671.0 | 208010 | 1.3863 | 0.272 |
| 1.3866 | 672.0 | 208320 | 1.3863 | 0.28 |
| 1.3868 | 673.0 | 208630 | 1.3863 | 0.276 |
| 1.3868 | 674.0 | 208940 | 1.3863 | 0.268 |
| 1.3862 | 675.0 | 209250 | 1.3863 | 0.286 |
| 1.386 | 676.0 | 209560 | 1.3863 | 0.284 |
| 1.386 | 677.0 | 209870 | 1.3863 | 0.28 |
| 1.3868 | 678.0 | 210180 | 1.3863 | 0.276 |
| 1.3868 | 679.0 | 210490 | 1.3863 | 0.28 |
| 1.3866 | 680.0 | 210800 | 1.3863 | 0.262 |
| 1.3867 | 681.0 | 211110 | 1.3863 | 0.262 |
| 1.3867 | 682.0 | 211420 | 1.3863 | 0.278 |
| 1.3864 | 683.0 | 211730 | 1.3863 | 0.268 |
| 1.387 | 684.0 | 212040 | 1.3863 | 0.264 |
| 1.387 | 685.0 | 212350 | 1.3863 | 0.264 |
| 1.3868 | 686.0 | 212660 | 1.3863 | 0.252 |
| 1.3868 | 687.0 | 212970 | 1.3863 | 0.276 |
| 1.3867 | 688.0 | 213280 | 1.3863 | 0.27 |
| 1.3863 | 689.0 | 213590 | 1.3863 | 0.246 |
| 1.3863 | 690.0 | 213900 | 1.3863 | 0.272 |
| 1.3866 | 691.0 | 214210 | 1.3863 | 0.276 |
| 1.3864 | 692.0 | 214520 | 1.3863 | 0.282 |
| 1.3864 | 693.0 | 214830 | 1.3863 | 0.282 |
| 1.3865 | 694.0 | 215140 | 1.3863 | 0.294 |
| 1.3865 | 695.0 | 215450 | 1.3863 | 0.274 |
| 1.3866 | 696.0 | 215760 | 1.3863 | 0.276 |
| 1.3868 | 697.0 | 216070 | 1.3863 | 0.272 |
| 1.3868 | 698.0 | 216380 | 1.3863 | 0.28 |
| 1.3863 | 699.0 | 216690 | 1.3863 | 0.276 |
| 1.3865 | 700.0 | 217000 | 1.3863 | 0.278 |
| 1.3865 | 701.0 | 217310 | 1.3863 | 0.276 |
| 1.3864 | 702.0 | 217620 | 1.3863 | 0.276 |
| 1.3864 | 703.0 | 217930 | 1.3863 | 0.278 |
| 1.3863 | 704.0 | 218240 | 1.3863 | 0.28 |
| 1.3867 | 705.0 | 218550 | 1.3863 | 0.256 |
| 1.3867 | 706.0 | 218860 | 1.3863 | 0.276 |
| 1.3862 | 707.0 | 219170 | 1.3863 | 0.244 |
| 1.3862 | 708.0 | 219480 | 1.3863 | 0.266 |
| 1.3861 | 709.0 | 219790 | 1.3863 | 0.272 |
| 1.3864 | 710.0 | 220100 | 1.3863 | 0.278 |
| 1.3864 | 711.0 | 220410 | 1.3863 | 0.272 |
| 1.386 | 712.0 | 220720 | 1.3863 | 0.258 |
| 1.3865 | 713.0 | 221030 | 1.3863 | 0.272 |
| 1.3865 | 714.0 | 221340 | 1.3863 | 0.272 |
| 1.3867 | 715.0 | 221650 | 1.3863 | 0.278 |
| 1.3867 | 716.0 | 221960 | 1.3863 | 0.266 |
| 1.3865 | 717.0 | 222270 | 1.3863 | 0.276 |
| 1.3868 | 718.0 | 222580 | 1.3863 | 0.282 |
| 1.3868 | 719.0 | 222890 | 1.3863 | 0.266 |
| 1.3864 | 720.0 | 223200 | 1.3863 | 0.274 |
| 1.3859 | 721.0 | 223510 | 1.3863 | 0.256 |
| 1.3859 | 722.0 | 223820 | 1.3863 | 0.276 |
| 1.3866 | 723.0 | 224130 | 1.3863 | 0.26 |
| 1.3866 | 724.0 | 224440 | 1.3863 | 0.276 |
| 1.3868 | 725.0 | 224750 | 1.3863 | 0.278 |
| 1.3864 | 726.0 | 225060 | 1.3863 | 0.268 |
| 1.3864 | 727.0 | 225370 | 1.3863 | 0.286 |
| 1.3868 | 728.0 | 225680 | 1.3863 | 0.274 |
| 1.3868 | 729.0 | 225990 | 1.3863 | 0.26 |
| 1.3865 | 730.0 | 226300 | 1.3863 | 0.278 |
| 1.3866 | 731.0 | 226610 | 1.3863 | 0.266 |
| 1.3866 | 732.0 | 226920 | 1.3863 | 0.276 |
| 1.387 | 733.0 | 227230 | 1.3863 | 0.27 |
| 1.3868 | 734.0 | 227540 | 1.3863 | 0.266 |
| 1.3868 | 735.0 | 227850 | 1.3863 | 0.268 |
| 1.387 | 736.0 | 228160 | 1.3863 | 0.268 |
| 1.387 | 737.0 | 228470 | 1.3863 | 0.28 |
| 1.3869 | 738.0 | 228780 | 1.3863 | 0.284 |
| 1.3866 | 739.0 | 229090 | 1.3863 | 0.284 |
| 1.3866 | 740.0 | 229400 | 1.3863 | 0.276 |
| 1.3865 | 741.0 | 229710 | 1.3863 | 0.276 |
| 1.3867 | 742.0 | 230020 | 1.3863 | 0.272 |
| 1.3867 | 743.0 | 230330 | 1.3863 | 0.276 |
| 1.3863 | 744.0 | 230640 | 1.3863 | 0.272 |
| 1.3863 | 745.0 | 230950 | 1.3863 | 0.278 |
| 1.3868 | 746.0 | 231260 | 1.3863 | 0.282 |
| 1.3867 | 747.0 | 231570 | 1.3863 | 0.276 |
| 1.3867 | 748.0 | 231880 | 1.3863 | 0.286 |
| 1.3866 | 749.0 | 232190 | 1.3863 | 0.276 |
| 1.3864 | 750.0 | 232500 | 1.3863 | 0.264 |
| 1.3864 | 751.0 | 232810 | 1.3863 | 0.26 |
| 1.3868 | 752.0 | 233120 | 1.3863 | 0.262 |
| 1.3868 | 753.0 | 233430 | 1.3863 | 0.276 |
| 1.3867 | 754.0 | 233740 | 1.3863 | 0.276 |
| 1.3864 | 755.0 | 234050 | 1.3863 | 0.252 |
| 1.3864 | 756.0 | 234360 | 1.3863 | 0.276 |
| 1.3864 | 757.0 | 234670 | 1.3863 | 0.276 |
| 1.3864 | 758.0 | 234980 | 1.3863 | 0.274 |
| 1.3866 | 759.0 | 235290 | 1.3863 | 0.27 |
| 1.3869 | 760.0 | 235600 | 1.3863 | 0.276 |
| 1.3869 | 761.0 | 235910 | 1.3863 | 0.276 |
| 1.3863 | 762.0 | 236220 | 1.3863 | 0.268 |
| 1.3869 | 763.0 | 236530 | 1.3863 | 0.274 |
| 1.3869 | 764.0 | 236840 | 1.3863 | 0.276 |
| 1.3867 | 765.0 | 237150 | 1.3863 | 0.274 |
| 1.3867 | 766.0 | 237460 | 1.3863 | 0.278 |
| 1.3866 | 767.0 | 237770 | 1.3863 | 0.262 |
| 1.3864 | 768.0 | 238080 | 1.3863 | 0.274 |
| 1.3864 | 769.0 | 238390 | 1.3863 | 0.276 |
| 1.3868 | 770.0 | 238700 | 1.3863 | 0.28 |
| 1.3867 | 771.0 | 239010 | 1.3863 | 0.276 |
| 1.3867 | 772.0 | 239320 | 1.3863 | 0.26 |
| 1.3867 | 773.0 | 239630 | 1.3863 | 0.258 |
| 1.3867 | 774.0 | 239940 | 1.3863 | 0.28 |
| 1.3867 | 775.0 | 240250 | 1.3863 | 0.272 |
| 1.3865 | 776.0 | 240560 | 1.3863 | 0.276 |
| 1.3865 | 777.0 | 240870 | 1.3863 | 0.276 |
| 1.3867 | 778.0 | 241180 | 1.3863 | 0.264 |
| 1.3867 | 779.0 | 241490 | 1.3863 | 0.27 |
| 1.3867 | 780.0 | 241800 | 1.3863 | 0.276 |
| 1.3863 | 781.0 | 242110 | 1.3863 | 0.276 |
| 1.3863 | 782.0 | 242420 | 1.3863 | 0.276 |
| 1.3861 | 783.0 | 242730 | 1.3863 | 0.276 |
| 1.3865 | 784.0 | 243040 | 1.3863 | 0.274 |
| 1.3865 | 785.0 | 243350 | 1.3863 | 0.262 |
| 1.3866 | 786.0 | 243660 | 1.3863 | 0.26 |
| 1.3866 | 787.0 | 243970 | 1.3863 | 0.276 |
| 1.3865 | 788.0 | 244280 | 1.3863 | 0.306 |
| 1.3866 | 789.0 | 244590 | 1.3863 | 0.276 |
| 1.3866 | 790.0 | 244900 | 1.3863 | 0.256 |
| 1.3861 | 791.0 | 245210 | 1.3863 | 0.278 |
| 1.3869 | 792.0 | 245520 | 1.3863 | 0.25 |
| 1.3869 | 793.0 | 245830 | 1.3863 | 0.28 |
| 1.3864 | 794.0 | 246140 | 1.3863 | 0.286 |
| 1.3864 | 795.0 | 246450 | 1.3863 | 0.276 |
| 1.3865 | 796.0 | 246760 | 1.3863 | 0.286 |
| 1.3864 | 797.0 | 247070 | 1.3863 | 0.276 |
| 1.3864 | 798.0 | 247380 | 1.3863 | 0.276 |
| 1.3868 | 799.0 | 247690 | 1.3863 | 0.254 |
| 1.3864 | 800.0 | 248000 | 1.3863 | 0.26 |
| 1.3864 | 801.0 | 248310 | 1.3863 | 0.276 |
| 1.3864 | 802.0 | 248620 | 1.3863 | 0.284 |
| 1.3864 | 803.0 | 248930 | 1.3863 | 0.276 |
| 1.3863 | 804.0 | 249240 | 1.3863 | 0.272 |
| 1.3867 | 805.0 | 249550 | 1.3863 | 0.27 |
| 1.3867 | 806.0 | 249860 | 1.3863 | 0.276 |
| 1.3866 | 807.0 | 250170 | 1.3863 | 0.254 |
| 1.3866 | 808.0 | 250480 | 1.3863 | 0.272 |
| 1.3863 | 809.0 | 250790 | 1.3863 | 0.276 |
| 1.3864 | 810.0 | 251100 | 1.3863 | 0.276 |
| 1.3864 | 811.0 | 251410 | 1.3863 | 0.276 |
| 1.3863 | 812.0 | 251720 | 1.3863 | 0.276 |
| 1.3866 | 813.0 | 252030 | 1.3863 | 0.272 |
| 1.3866 | 814.0 | 252340 | 1.3863 | 0.27 |
| 1.3865 | 815.0 | 252650 | 1.3863 | 0.284 |
| 1.3865 | 816.0 | 252960 | 1.3863 | 0.276 |
| 1.3867 | 817.0 | 253270 | 1.3863 | 0.276 |
| 1.3863 | 818.0 | 253580 | 1.3863 | 0.28 |
| 1.3863 | 819.0 | 253890 | 1.3863 | 0.29 |
| 1.3864 | 820.0 | 254200 | 1.3863 | 0.272 |
| 1.386 | 821.0 | 254510 | 1.3863 | 0.276 |
| 1.386 | 822.0 | 254820 | 1.3863 | 0.274 |
| 1.3859 | 823.0 | 255130 | 1.3863 | 0.26 |
| 1.3859 | 824.0 | 255440 | 1.3863 | 0.276 |
| 1.3864 | 825.0 | 255750 | 1.3863 | 0.276 |
| 1.3862 | 826.0 | 256060 | 1.3863 | 0.278 |
| 1.3862 | 827.0 | 256370 | 1.3863 | 0.276 |
| 1.3869 | 828.0 | 256680 | 1.3863 | 0.284 |
| 1.3869 | 829.0 | 256990 | 1.3863 | 0.258 |
| 1.3862 | 830.0 | 257300 | 1.3863 | 0.292 |
| 1.3864 | 831.0 | 257610 | 1.3863 | 0.276 |
| 1.3864 | 832.0 | 257920 | 1.3863 | 0.264 |
| 1.3865 | 833.0 | 258230 | 1.3863 | 0.276 |
| 1.3864 | 834.0 | 258540 | 1.3863 | 0.284 |
| 1.3864 | 835.0 | 258850 | 1.3863 | 0.268 |
| 1.3866 | 836.0 | 259160 | 1.3863 | 0.288 |
| 1.3866 | 837.0 | 259470 | 1.3863 | 0.276 |
| 1.3859 | 838.0 | 259780 | 1.3863 | 0.274 |
| 1.3863 | 839.0 | 260090 | 1.3863 | 0.252 |
| 1.3863 | 840.0 | 260400 | 1.3863 | 0.282 |
| 1.3863 | 841.0 | 260710 | 1.3863 | 0.276 |
| 1.3864 | 842.0 | 261020 | 1.3863 | 0.266 |
| 1.3864 | 843.0 | 261330 | 1.3863 | 0.282 |
| 1.3862 | 844.0 | 261640 | 1.3863 | 0.276 |
| 1.3862 | 845.0 | 261950 | 1.3863 | 0.274 |
| 1.3866 | 846.0 | 262260 | 1.3863 | 0.276 |
| 1.3863 | 847.0 | 262570 | 1.3863 | 0.242 |
| 1.3863 | 848.0 | 262880 | 1.3863 | 0.28 |
| 1.3868 | 849.0 | 263190 | 1.3863 | 0.276 |
| 1.3866 | 850.0 | 263500 | 1.3863 | 0.278 |
| 1.3866 | 851.0 | 263810 | 1.3863 | 0.278 |
| 1.3865 | 852.0 | 264120 | 1.3863 | 0.27 |
| 1.3865 | 853.0 | 264430 | 1.3863 | 0.276 |
| 1.3865 | 854.0 | 264740 | 1.3863 | 0.246 |
| 1.3865 | 855.0 | 265050 | 1.3863 | 0.276 |
| 1.3865 | 856.0 | 265360 | 1.3863 | 0.276 |
| 1.3862 | 857.0 | 265670 | 1.3863 | 0.28 |
| 1.3862 | 858.0 | 265980 | 1.3863 | 0.264 |
| 1.386 | 859.0 | 266290 | 1.3863 | 0.274 |
| 1.3858 | 860.0 | 266600 | 1.3863 | 0.272 |
| 1.3858 | 861.0 | 266910 | 1.3863 | 0.272 |
| 1.3868 | 862.0 | 267220 | 1.3863 | 0.276 |
| 1.3863 | 863.0 | 267530 | 1.3863 | 0.264 |
| 1.3863 | 864.0 | 267840 | 1.3863 | 0.272 |
| 1.3866 | 865.0 | 268150 | 1.3863 | 0.254 |
| 1.3866 | 866.0 | 268460 | 1.3863 | 0.278 |
| 1.3861 | 867.0 | 268770 | 1.3863 | 0.276 |
| 1.3863 | 868.0 | 269080 | 1.3863 | 0.272 |
| 1.3863 | 869.0 | 269390 | 1.3863 | 0.264 |
| 1.3866 | 870.0 | 269700 | 1.3863 | 0.276 |
| 1.3865 | 871.0 | 270010 | 1.3863 | 0.276 |
| 1.3865 | 872.0 | 270320 | 1.3863 | 0.276 |
| 1.3864 | 873.0 | 270630 | 1.3863 | 0.266 |
| 1.3864 | 874.0 | 270940 | 1.3863 | 0.274 |
| 1.3862 | 875.0 | 271250 | 1.3863 | 0.276 |
| 1.3865 | 876.0 | 271560 | 1.3863 | 0.266 |
| 1.3865 | 877.0 | 271870 | 1.3863 | 0.28 |
| 1.3868 | 878.0 | 272180 | 1.3863 | 0.276 |
| 1.3868 | 879.0 | 272490 | 1.3863 | 0.276 |
| 1.3865 | 880.0 | 272800 | 1.3863 | 0.276 |
| 1.3862 | 881.0 | 273110 | 1.3863 | 0.276 |
| 1.3862 | 882.0 | 273420 | 1.3863 | 0.276 |
| 1.3866 | 883.0 | 273730 | 1.3863 | 0.286 |
| 1.3865 | 884.0 | 274040 | 1.3863 | 0.258 |
| 1.3865 | 885.0 | 274350 | 1.3863 | 0.272 |
| 1.3863 | 886.0 | 274660 | 1.3863 | 0.276 |
| 1.3863 | 887.0 | 274970 | 1.3863 | 0.276 |
| 1.3866 | 888.0 | 275280 | 1.3863 | 0.276 |
| 1.3863 | 889.0 | 275590 | 1.3863 | 0.276 |
| 1.3863 | 890.0 | 275900 | 1.3863 | 0.276 |
| 1.3864 | 891.0 | 276210 | 1.3863 | 0.27 |
| 1.3864 | 892.0 | 276520 | 1.3863 | 0.266 |
| 1.3864 | 893.0 | 276830 | 1.3863 | 0.276 |
| 1.3864 | 894.0 | 277140 | 1.3863 | 0.276 |
| 1.3864 | 895.0 | 277450 | 1.3863 | 0.282 |
| 1.3866 | 896.0 | 277760 | 1.3863 | 0.27 |
| 1.3868 | 897.0 | 278070 | 1.3863 | 0.28 |
| 1.3868 | 898.0 | 278380 | 1.3863 | 0.288 |
| 1.3863 | 899.0 | 278690 | 1.3863 | 0.27 |
| 1.3865 | 900.0 | 279000 | 1.3863 | 0.284 |
| 1.3865 | 901.0 | 279310 | 1.3863 | 0.276 |
| 1.3867 | 902.0 | 279620 | 1.3863 | 0.272 |
| 1.3867 | 903.0 | 279930 | 1.3863 | 0.276 |
| 1.3864 | 904.0 | 280240 | 1.3863 | 0.304 |
| 1.3865 | 905.0 | 280550 | 1.3863 | 0.278 |
| 1.3865 | 906.0 | 280860 | 1.3863 | 0.276 |
| 1.3862 | 907.0 | 281170 | 1.3863 | 0.276 |
| 1.3862 | 908.0 | 281480 | 1.3863 | 0.27 |
| 1.3865 | 909.0 | 281790 | 1.3863 | 0.266 |
| 1.3863 | 910.0 | 282100 | 1.3863 | 0.276 |
| 1.3863 | 911.0 | 282410 | 1.3863 | 0.256 |
| 1.3865 | 912.0 | 282720 | 1.3863 | 0.262 |
| 1.3866 | 913.0 | 283030 | 1.3863 | 0.268 |
| 1.3866 | 914.0 | 283340 | 1.3863 | 0.288 |
| 1.386 | 915.0 | 283650 | 1.3863 | 0.276 |
| 1.386 | 916.0 | 283960 | 1.3863 | 0.272 |
| 1.3862 | 917.0 | 284270 | 1.3863 | 0.276 |
| 1.3863 | 918.0 | 284580 | 1.3863 | 0.276 |
| 1.3863 | 919.0 | 284890 | 1.3863 | 0.266 |
| 1.3863 | 920.0 | 285200 | 1.3863 | 0.252 |
| 1.3862 | 921.0 | 285510 | 1.3863 | 0.276 |
| 1.3862 | 922.0 | 285820 | 1.3863 | 0.252 |
| 1.3863 | 923.0 | 286130 | 1.3863 | 0.268 |
| 1.3863 | 924.0 | 286440 | 1.3863 | 0.276 |
| 1.3866 | 925.0 | 286750 | 1.3863 | 0.272 |
| 1.3867 | 926.0 | 287060 | 1.3863 | 0.272 |
| 1.3867 | 927.0 | 287370 | 1.3863 | 0.276 |
| 1.3869 | 928.0 | 287680 | 1.3863 | 0.276 |
| 1.3869 | 929.0 | 287990 | 1.3863 | 0.276 |
| 1.3864 | 930.0 | 288300 | 1.3863 | 0.266 |
| 1.3862 | 931.0 | 288610 | 1.3863 | 0.258 |
| 1.3862 | 932.0 | 288920 | 1.3863 | 0.276 |
| 1.3864 | 933.0 | 289230 | 1.3863 | 0.27 |
| 1.3861 | 934.0 | 289540 | 1.3863 | 0.276 |
| 1.3861 | 935.0 | 289850 | 1.3863 | 0.274 |
| 1.3869 | 936.0 | 290160 | 1.3863 | 0.274 |
| 1.3869 | 937.0 | 290470 | 1.3863 | 0.276 |
| 1.3861 | 938.0 | 290780 | 1.3863 | 0.252 |
| 1.386 | 939.0 | 291090 | 1.3863 | 0.276 |
| 1.386 | 940.0 | 291400 | 1.3863 | 0.266 |
| 1.3865 | 941.0 | 291710 | 1.3863 | 0.274 |
| 1.3862 | 942.0 | 292020 | 1.3863 | 0.274 |
| 1.3862 | 943.0 | 292330 | 1.3863 | 0.26 |
| 1.3863 | 944.0 | 292640 | 1.3863 | 0.268 |
| 1.3863 | 945.0 | 292950 | 1.3863 | 0.274 |
| 1.3868 | 946.0 | 293260 | 1.3863 | 0.276 |
| 1.3865 | 947.0 | 293570 | 1.3863 | 0.256 |
| 1.3865 | 948.0 | 293880 | 1.3863 | 0.296 |
| 1.3863 | 949.0 | 294190 | 1.3863 | 0.276 |
| 1.3865 | 950.0 | 294500 | 1.3863 | 0.272 |
| 1.3865 | 951.0 | 294810 | 1.3863 | 0.276 |
| 1.3868 | 952.0 | 295120 | 1.3863 | 0.276 |
| 1.3868 | 953.0 | 295430 | 1.3863 | 0.282 |
| 1.3862 | 954.0 | 295740 | 1.3863 | 0.264 |
| 1.3864 | 955.0 | 296050 | 1.3863 | 0.278 |
| 1.3864 | 956.0 | 296360 | 1.3863 | 0.276 |
| 1.3864 | 957.0 | 296670 | 1.3863 | 0.274 |
| 1.3864 | 958.0 | 296980 | 1.3863 | 0.28 |
| 1.3855 | 959.0 | 297290 | 1.3863 | 0.25 |
| 1.3859 | 960.0 | 297600 | 1.3863 | 0.27 |
| 1.3859 | 961.0 | 297910 | 1.3863 | 0.276 |
| 1.3863 | 962.0 | 298220 | 1.3863 | 0.278 |
| 1.3867 | 963.0 | 298530 | 1.3863 | 0.278 |
| 1.3867 | 964.0 | 298840 | 1.3863 | 0.264 |
| 1.3863 | 965.0 | 299150 | 1.3863 | 0.278 |
| 1.3863 | 966.0 | 299460 | 1.3863 | 0.268 |
| 1.3865 | 967.0 | 299770 | 1.3863 | 0.276 |
| 1.3866 | 968.0 | 300080 | 1.3863 | 0.272 |
| 1.3866 | 969.0 | 300390 | 1.3863 | 0.278 |
| 1.3869 | 970.0 | 300700 | 1.3863 | 0.278 |
| 1.3868 | 971.0 | 301010 | 1.3863 | 0.276 |
| 1.3868 | 972.0 | 301320 | 1.3863 | 0.276 |
| 1.3865 | 973.0 | 301630 | 1.3863 | 0.284 |
| 1.3865 | 974.0 | 301940 | 1.3863 | 0.266 |
| 1.3862 | 975.0 | 302250 | 1.3863 | 0.288 |
| 1.3864 | 976.0 | 302560 | 1.3863 | 0.276 |
| 1.3864 | 977.0 | 302870 | 1.3863 | 0.266 |
| 1.3859 | 978.0 | 303180 | 1.3863 | 0.276 |
| 1.3859 | 979.0 | 303490 | 1.3863 | 0.27 |
| 1.3861 | 980.0 | 303800 | 1.3863 | 0.272 |
| 1.3864 | 981.0 | 304110 | 1.3863 | 0.278 |
| 1.3864 | 982.0 | 304420 | 1.3863 | 0.278 |
| 1.3866 | 983.0 | 304730 | 1.3863 | 0.262 |
| 1.3862 | 984.0 | 305040 | 1.3863 | 0.276 |
| 1.3862 | 985.0 | 305350 | 1.3863 | 0.268 |
| 1.3862 | 986.0 | 305660 | 1.3863 | 0.278 |
| 1.3862 | 987.0 | 305970 | 1.3863 | 0.274 |
| 1.3862 | 988.0 | 306280 | 1.3863 | 0.264 |
| 1.3867 | 989.0 | 306590 | 1.3863 | 0.276 |
| 1.3867 | 990.0 | 306900 | 1.3863 | 0.272 |
| 1.3869 | 991.0 | 307210 | 1.3863 | 0.276 |
| 1.3863 | 992.0 | 307520 | 1.3863 | 0.276 |
| 1.3863 | 993.0 | 307830 | 1.3863 | 0.262 |
| 1.3865 | 994.0 | 308140 | 1.3863 | 0.28 |
| 1.3865 | 995.0 | 308450 | 1.3863 | 0.286 |
| 1.3868 | 996.0 | 308760 | 1.3863 | 0.29 |
| 1.3866 | 997.0 | 309070 | 1.3863 | 0.268 |
| 1.3866 | 998.0 | 309380 | 1.3863 | 0.276 |
| 1.3864 | 999.0 | 309690 | 1.3863 | 0.282 |
| 1.3861 | 1000.0 | 310000 | 1.3863 | 0.268 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
hakurei/waifu-diffusion-v1-3
|
hakurei
| 2022-10-08T16:19:55Z | 0 | 606 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-09-28T03:04:20Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Waifu Diffusion v1.3
Waifu Diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning.
- [Float 16 EMA Pruned](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-float16.ckpt)
- [Float 32 EMA Pruned](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-float32.ckpt)
- [Float 32 Full Weights](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-full.ckpt)
- [Float 32 Full Weights + Optimizer Weights (For Training)](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-full-opt.ckpt)
## Model Description
The model originally used for fine-tuning is [Stable Diffusion 1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en). The current model has been fine-tuned with a learning rate of 5.0e-6 for 10 epochs on 680k anime-styled images.
[See here for an in-depth overview of Waifu Diffusion 1.3.](https://gist.github.com/harubaru/f727cedacae336d1f7877c4bbe2196e1)
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
## Team Members and Acknowledgements
This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
- [Anthony Mercurio](https://github.com/harubaru)
- [Salt](https://github.com/sALTaccount/)
- [Cafe](https://twitter.com/cafeai_labs)
In order to reach us, you can join our [Discord server](https://discord.gg/touhouai).
[](https://discord.gg/touhouai)
|
joelearn22/q-Taxi-v3
|
joelearn22
| 2022-10-08T15:59:02Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-08T15:58:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="joelearn22/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
joelearn22/q-FrozenLake-v1-4x4-noSlippery
|
joelearn22
| 2022-10-08T15:55:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-08T15:35:33Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.76 +/- 0.43
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="joelearn22/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
anas-awadalla/bart-base-few-shot-k-128-finetuned-squad-seq2seq-seed-2
|
anas-awadalla
| 2022-10-08T15:39:09Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-04T22:53:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-128-finetuned-squad-seq2seq-seed-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-base-few-shot-k-128-finetuned-squad-seq2seq-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-finetuned-squad-infilling-lr-5e-6-decay-001
|
anas-awadalla
| 2022-10-08T15:00:52Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T12:31:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-5e-6-decay-001
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-finetuned-squad-infilling-lr-5e-6-decay-001
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
lafayetteprocessservers/Process-Server-in-Slidell
|
lafayetteprocessservers
| 2022-10-08T14:48:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-10-08T14:47:46Z |
Are you looking for <a href="https://lafayette-process-servers.com/process-server/">process server in Slidell</a>? You are at the right place.
Our service is very user friendly. To begin your order, you can simply click the button labeled “Upload Forms.” This will take you to a screen that will allow you to select the type of service you want, including options for rush service.
|
anas-awadalla/bart-large-finetuned-squad-infilling-lr-3e-5-decay-001
|
anas-awadalla
| 2022-10-08T12:27:21Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T09:58:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-3e-5-decay-001
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-finetuned-squad-infilling-lr-3e-5-decay-001
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
rohit1998/all-MiniLM-L6-v2-quora
|
rohit1998
| 2022-10-08T10:00:37Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:quora",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-06T20:16:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- quora
metrics:
- accuracy
- f1
model-index:
- name: all-MiniLM-L6-v2-quora
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: quora
type: quora
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8150291876916989
- name: F1
type: f1
value: 0.794526570313788
---
<!-- 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. -->
# all-MiniLM-L6-v2-quora
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the quora dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0865
- Accuracy: 0.8150
- F1: 0.7945
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.087 | 1.0 | 11371 | 0.0829 | 0.4143 | 0.5535 |
| 0.0794 | 2.0 | 22742 | 0.0783 | 0.6017 | 0.6458 |
| 0.0606 | 3.0 | 34113 | 0.0756 | 0.3631 | 0.5327 |
| 0.05 | 4.0 | 45484 | 0.0781 | 0.4475 | 0.5679 |
| 0.0448 | 5.0 | 56855 | 0.0789 | 0.6856 | 0.6975 |
| 0.0372 | 6.0 | 68226 | 0.0761 | 0.3922 | 0.5443 |
| 0.033 | 7.0 | 79597 | 0.0786 | 0.7586 | 0.7494 |
| 0.032 | 8.0 | 90968 | 0.0780 | 0.5011 | 0.5927 |
| 0.0229 | 9.0 | 102339 | 0.0819 | 0.7513 | 0.7439 |
| 0.0198 | 10.0 | 113710 | 0.0840 | 0.5522 | 0.6185 |
| 0.0169 | 11.0 | 125081 | 0.0821 | 0.7959 | 0.7785 |
| 0.0199 | 12.0 | 136452 | 0.0807 | 0.8353 | 0.8118 |
| 0.0118 | 13.0 | 147823 | 0.0819 | 0.8418 | 0.8176 |
| 0.0123 | 14.0 | 159194 | 0.0816 | 0.7577 | 0.7487 |
| 0.0093 | 15.0 | 170565 | 0.0856 | 0.7934 | 0.7765 |
| 0.0124 | 16.0 | 181936 | 0.0843 | 0.8484 | 0.8241 |
| 0.008 | 17.0 | 193307 | 0.0838 | 0.7998 | 0.7818 |
| 0.0106 | 18.0 | 204678 | 0.0872 | 0.8245 | 0.8027 |
| 0.0066 | 19.0 | 216049 | 0.0857 | 0.8122 | 0.7922 |
| 0.0059 | 20.0 | 227420 | 0.0865 | 0.8150 | 0.7945 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu116
- Datasets 2.5.1
- Tokenizers 0.12.1
|
anas-awadalla/bart-large-finetuned-squad-infilling-lr-3e-5-decay-01
|
anas-awadalla
| 2022-10-08T09:54:28Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T07:27:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-3e-5-decay-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-squad-infilling-lr-3e-5-decay-01
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
nancy-zwx/t5-base-medium-title-generation
|
nancy-zwx
| 2022-10-08T09:49:27Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T09:48:43Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-medium-title-generation
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-base-medium-title-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.12.1
|
karuniaperjuangan/smsa-distilbert-indo
|
karuniaperjuangan
| 2022-10-08T07:39:00Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"id",
"dataset:indonlu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-08T03:08:03Z |
---
license: mit
datasets:
- indonlu
language: id
widget:
- text: "Produk ini masih harus diperbaiki"
---
|
richeung1/layoutlm-funsd
|
richeung1
| 2022-10-08T07:07:12Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-07T11:36:55Z |
---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6843
- Answer: {'precision': 0.6938997821350763, 'recall': 0.7873918417799752, 'f1': 0.7376954255935148, 'number': 809}
- Header: {'precision': 0.27941176470588236, 'recall': 0.31932773109243695, 'f1': 0.2980392156862745, 'number': 119}
- Question: {'precision': 0.7749562171628721, 'recall': 0.8309859154929577, 'f1': 0.8019936565473492, 'number': 1065}
- Overall Precision: 0.7104
- Overall Recall: 0.7827
- Overall F1: 0.7448
- Overall Accuracy: 0.8076
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8035 | 1.0 | 10 | 1.6086 | {'precision': 0.007142857142857143, 'recall': 0.003708281829419036, 'f1': 0.004882017900732303, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.07957559681697612, 'recall': 0.028169014084507043, 'f1': 0.04160887656033287, 'number': 1065} | 0.0414 | 0.0166 | 0.0237 | 0.3175 |
| 1.4936 | 2.0 | 20 | 1.2735 | {'precision': 0.279126213592233, 'recall': 0.4264524103831891, 'f1': 0.3374083129584352, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4406651549508692, 'recall': 0.5474178403755868, 'f1': 0.48827470686767166, 'number': 1065} | 0.3626 | 0.4656 | 0.4077 | 0.6074 |
| 1.1259 | 3.0 | 30 | 0.9718 | {'precision': 0.47892074198988194, 'recall': 0.7021013597033374, 'f1': 0.569423558897243, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5904121110176619, 'recall': 0.6591549295774648, 'f1': 0.6228926353149955, 'number': 1065} | 0.5336 | 0.6372 | 0.5808 | 0.6760 |
| 0.8568 | 4.0 | 40 | 0.8421 | {'precision': 0.5595126522961574, 'recall': 0.7379480840543882, 'f1': 0.6364605543710021, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6669595782073814, 'recall': 0.7126760563380282, 'f1': 0.6890603722197005, 'number': 1065} | 0.6059 | 0.6804 | 0.6410 | 0.7310 |
| 0.7275 | 5.0 | 50 | 0.7430 | {'precision': 0.6401673640167364, 'recall': 0.7564894932014833, 'f1': 0.693484419263456, 'number': 809} | {'precision': 0.12643678160919541, 'recall': 0.09243697478991597, 'f1': 0.10679611650485439, 'number': 119} | {'precision': 0.6825, 'recall': 0.7690140845070422, 'f1': 0.7231788079470199, 'number': 1065} | 0.6429 | 0.7235 | 0.6808 | 0.7727 |
| 0.6109 | 6.0 | 60 | 0.6965 | {'precision': 0.6494736842105263, 'recall': 0.7626699629171817, 'f1': 0.7015349630471859, 'number': 809} | {'precision': 0.125, 'recall': 0.09243697478991597, 'f1': 0.10628019323671498, 'number': 119} | {'precision': 0.6780415430267063, 'recall': 0.8582159624413146, 'f1': 0.7575631993369251, 'number': 1065} | 0.6463 | 0.7737 | 0.7043 | 0.7862 |
| 0.5341 | 7.0 | 70 | 0.6816 | {'precision': 0.6745945945945946, 'recall': 0.7713226205191595, 'f1': 0.7197231833910035, 'number': 809} | {'precision': 0.22727272727272727, 'recall': 0.21008403361344538, 'f1': 0.21834061135371177, 'number': 119} | {'precision': 0.7435037720033529, 'recall': 0.8328638497652582, 'f1': 0.7856510186005314, 'number': 1065} | 0.6894 | 0.7707 | 0.7278 | 0.7920 |
| 0.4811 | 8.0 | 80 | 0.6577 | {'precision': 0.6800870511425462, 'recall': 0.7725587144622992, 'f1': 0.7233796296296297, 'number': 809} | {'precision': 0.2072072072072072, 'recall': 0.19327731092436976, 'f1': 0.2, 'number': 119} | {'precision': 0.7440132122213047, 'recall': 0.8460093896713615, 'f1': 0.7917398945518453, 'number': 1065} | 0.6912 | 0.7772 | 0.7317 | 0.7986 |
| 0.4241 | 9.0 | 90 | 0.6586 | {'precision': 0.6898454746136865, 'recall': 0.7725587144622992, 'f1': 0.7288629737609328, 'number': 809} | {'precision': 0.2535211267605634, 'recall': 0.3025210084033613, 'f1': 0.2758620689655173, 'number': 119} | {'precision': 0.751269035532995, 'recall': 0.8338028169014085, 'f1': 0.7903871829105474, 'number': 1065} | 0.6946 | 0.7772 | 0.7336 | 0.7991 |
| 0.3784 | 10.0 | 100 | 0.6511 | {'precision': 0.6879739978331527, 'recall': 0.7849196538936959, 'f1': 0.7332563510392609, 'number': 809} | {'precision': 0.2833333333333333, 'recall': 0.2857142857142857, 'f1': 0.2845188284518828, 'number': 119} | {'precision': 0.7590870667793744, 'recall': 0.8431924882629108, 'f1': 0.7989323843416369, 'number': 1065} | 0.7040 | 0.7863 | 0.7428 | 0.8046 |
| 0.3425 | 11.0 | 110 | 0.6611 | {'precision': 0.6975982532751092, 'recall': 0.7898640296662547, 'f1': 0.7408695652173912, 'number': 809} | {'precision': 0.26865671641791045, 'recall': 0.3025210084033613, 'f1': 0.2845849802371542, 'number': 119} | {'precision': 0.7737162750217581, 'recall': 0.8347417840375587, 'f1': 0.803071364046974, 'number': 1065} | 0.7112 | 0.7847 | 0.7462 | 0.8116 |
| 0.3225 | 12.0 | 120 | 0.6676 | {'precision': 0.6957470010905126, 'recall': 0.788627935723115, 'f1': 0.7392815758980301, 'number': 809} | {'precision': 0.27941176470588236, 'recall': 0.31932773109243695, 'f1': 0.2980392156862745, 'number': 119} | {'precision': 0.7806167400881058, 'recall': 0.831924882629108, 'f1': 0.8054545454545454, 'number': 1065} | 0.7139 | 0.7837 | 0.7472 | 0.8081 |
| 0.302 | 13.0 | 130 | 0.6698 | {'precision': 0.6956043956043956, 'recall': 0.7824474660074165, 'f1': 0.7364746945898778, 'number': 809} | {'precision': 0.2878787878787879, 'recall': 0.31932773109243695, 'f1': 0.302788844621514, 'number': 119} | {'precision': 0.7730434782608696, 'recall': 0.8347417840375587, 'f1': 0.8027088036117382, 'number': 1065} | 0.7117 | 0.7827 | 0.7455 | 0.8133 |
| 0.2915 | 14.0 | 140 | 0.6845 | {'precision': 0.6978260869565217, 'recall': 0.7935723114956736, 'f1': 0.742625795257374, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.31932773109243695, 'f1': 0.30158730158730157, 'number': 119} | {'precision': 0.7771929824561403, 'recall': 0.831924882629108, 'f1': 0.8036281179138323, 'number': 1065} | 0.7141 | 0.7858 | 0.7482 | 0.8052 |
| 0.2872 | 15.0 | 150 | 0.6843 | {'precision': 0.6938997821350763, 'recall': 0.7873918417799752, 'f1': 0.7376954255935148, 'number': 809} | {'precision': 0.27941176470588236, 'recall': 0.31932773109243695, 'f1': 0.2980392156862745, 'number': 119} | {'precision': 0.7749562171628721, 'recall': 0.8309859154929577, 'f1': 0.8019936565473492, 'number': 1065} | 0.7104 | 0.7827 | 0.7448 | 0.8076 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
jmcfadden/swin-tiny-patch4-window7-224-finetuned-eurosat
|
jmcfadden
| 2022-10-08T05:55:35Z | 219 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-08T05:32:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9807407407407407
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Accuracy: 0.9807
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2578 | 1.0 | 190 | 0.1447 | 0.9530 |
| 0.1733 | 2.0 | 380 | 0.0787 | 0.9733 |
| 0.1139 | 3.0 | 570 | 0.0613 | 0.9807 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
yl852/finetuning-sentiment-model
|
yl852
| 2022-10-08T05:33:27Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T02:17:00Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) 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: 0.0002
- 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: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
anas-awadalla/bart-base-finetuned-squad-infilling-lr-3e-5-decay-01
|
anas-awadalla
| 2022-10-08T04:49:24Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-08T04:06:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-finetuned-squad-infilling-lr-3e-5-decay-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-squad-infilling-lr-3e-5-decay-01
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 48
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-large-finetuned-squad-infilling-lr-1e-5-decay-01
|
anas-awadalla
| 2022-10-08T00:15:40Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-07T22:28:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-1e-5-decay-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-squad-infilling-lr-1e-5-decay-01
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
debarghabhattofficial/t5-small-squad-finetuned
|
debarghabhattofficial
| 2022-10-08T00:05:20Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:qg_squad",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-07T21:55:40Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- qg_squad
metrics:
- bleu
model-index:
- name: t5-small-squad-finetuned
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: qg_squad
type: qg_squad
config: qg_squad
split: train
args: qg_squad
metrics:
- name: Bleu
type: bleu
value: 0.18729932526273085
---
<!-- 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-squad-finetuned
This model is a fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) on the qg_squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8668
- Bleu: 0.1873
- Precisions: [0.5110525491352382, 0.245362761211552, 0.15215077757561193, 0.09884530767928974]
- Brevity Penalty: 0.8988
- Length Ratio: 0.9036
- Translation Length: 108527
- Reference Length: 120107
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| 1.9131 | 1.0 | 2367 | 1.8960 | 0.1861 | [0.5106522785325583, 0.24527605096325475, 0.15213089101620028, 0.09826831888082575] | 0.8944 | 0.8996 | 108052 | 120107 |
| 1.849 | 2.0 | 4734 | 1.8806 | 0.1849 | [0.5080246970549962, 0.24224047124755838, 0.1501267012945318, 0.09769844997651479] | 0.8972 | 0.9021 | 108353 | 120107 |
| 1.8168 | 3.0 | 7101 | 1.8727 | 0.1854 | [0.5098220476080425, 0.24339941601352388, 0.15093927730223472, 0.09804485712417446] | 0.8956 | 0.9007 | 108175 | 120107 |
| 1.7923 | 4.0 | 9468 | 1.8700 | 0.1863 | [0.5133830790362698, 0.24615653748790878, 0.15267642711989654, 0.09868749835608512] | 0.8916 | 0.8971 | 107748 | 120107 |
| 1.7748 | 5.0 | 11835 | 1.8689 | 0.1869 | [0.5141749342160318, 0.24699161674176884, 0.1534446643289472, 0.0998958319598096] | 0.8898 | 0.8954 | 107549 | 120107 |
| 1.7587 | 6.0 | 14202 | 1.8698 | 0.1864 | [0.5146328972484753, 0.24659953524399691, 0.1532201031824242, 0.09970271520116271] | 0.8884 | 0.8942 | 107395 | 120107 |
| 1.7468 | 7.0 | 16569 | 1.8680 | 0.1860 | [0.5112671501824734, 0.24460144371064352, 0.15145530742292898, 0.09809866056844169] | 0.8961 | 0.9012 | 108235 | 120107 |
| 1.7378 | 8.0 | 18936 | 1.8670 | 0.1876 | [0.5122261914652045, 0.24610537728997678, 0.15275308797724588, 0.09927828458817849] | 0.8970 | 0.9020 | 108333 | 120107 |
| 1.7312 | 9.0 | 21303 | 1.8676 | 0.1876 | [0.5117292997446746, 0.24587669400218548, 0.15249172858304044, 0.0989853996535511] | 0.8984 | 0.9033 | 108489 | 120107 |
| 1.7271 | 10.0 | 23670 | 1.8668 | 0.1873 | [0.5110525491352382, 0.245362761211552, 0.15215077757561193, 0.09884530767928974] | 0.8988 | 0.9036 | 108527 | 120107 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
juanarturovargas/mt5-small-finetuned-amazon-en-es
|
juanarturovargas
| 2022-10-07T23:26:55Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-05T17:22:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0283
- Rouge1: 17.6736
- Rouge2: 8.5399
- Rougel: 17.4107
- Rougelsum: 17.3637
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.7032 | 1.0 | 1209 | 3.1958 | 16.1227 | 7.4852 | 15.2662 | 15.3552 |
| 3.6502 | 2.0 | 2418 | 3.1103 | 17.2284 | 8.1626 | 16.757 | 16.6583 |
| 3.4365 | 3.0 | 3627 | 3.0698 | 17.2326 | 8.7096 | 17.0961 | 16.9705 |
| 3.312 | 4.0 | 4836 | 3.0324 | 16.9472 | 8.1386 | 16.6025 | 16.6126 |
| 3.2343 | 5.0 | 6045 | 3.0385 | 17.8752 | 8.0578 | 17.4985 | 17.5298 |
| 3.1661 | 6.0 | 7254 | 3.0334 | 17.8822 | 8.5243 | 17.5825 | 17.5242 |
| 3.1305 | 7.0 | 8463 | 3.0289 | 17.8187 | 8.124 | 17.4815 | 17.4688 |
| 3.1039 | 8.0 | 9672 | 3.0283 | 17.6736 | 8.5399 | 17.4107 | 17.3637 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
phgoddard/distilbert-base-uncased-finetuned-emotion
|
phgoddard
| 2022-10-07T22:36:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-07T21:27:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249876505516254
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2182
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.827 | 1.0 | 250 | 0.3159 | 0.9045 | 0.9007 |
| 0.2459 | 2.0 | 500 | 0.2182 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/imnotpeeing-moss_sounds
|
huggingtweets
| 2022-10-07T21:01:30Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-07T13:14:17Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1240964084645969920/AM6v0rHu_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://abs.twimg.com/sticky/default_profile_images/default_profile_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">proverbs for paranoids & Zander</div>
<div style="text-align: center; font-size: 14px;">@imnotpeeing-moss_sounds</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 proverbs for paranoids & Zander.
| Data | proverbs for paranoids | Zander |
| --- | --- | --- |
| Tweets downloaded | 3152 | 693 |
| Retweets | 613 | 0 |
| Short tweets | 403 | 114 |
| Tweets kept | 2136 | 579 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1pr31dk4/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 @imnotpeeing-moss_sounds's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/13cb3qev) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/13cb3qev/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/imnotpeeing-moss_sounds')
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)
|
waifu-research-department/Emilico
|
waifu-research-department
| 2022-10-07T19:10:27Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-07T18:58:13Z |
---
license: mit
---
# Description
Trainer: Trash--Panda
Emilico from Shadows House
# Dataset
>Training: 12 images
# Info
>Model Used: Waifu Diffusion 1.2
>Steps: 1200
>Keyword: emilico (Use this in the prompt)
>Class Phrase: character
>Sample Prompt: An anime illustration of (((emilico))) looking at viewer, high quality, blonde hair, blue eyes, in a cafe, full body.
>Sample Negative Prompt: bad anatomy, disfigured, deformed, malformed, mutant, gross,
disgusting, out of frame, poorly drawn, extra limbs, extra fingers, missing limbs, blurry, out of focus.
|
Umar99/ddpm-butterflies-128
|
Umar99
| 2022-10-07T17:55:27Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-01T08:59:25Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Umar99/ddpm-butterflies-128/tensorboard?#scalars)
|
glissa/finetuning-sentiment-model-3000-samples
|
glissa
| 2022-10-07T16:16:42Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-07T16:00:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8609271523178809
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3172
- Accuracy: 0.86
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
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Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.