modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-08 19:17:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-08 18:30:19
| card
stringlengths 11
1.01M
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|---|---|---|---|---|---|---|---|---|---|
ciCic/decisionTransformer
|
ciCic
| 2022-09-10T21:09:32Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"decisionTransformer",
"deep reinforcement",
"dataset:edbeeching/decision_transformer_gym_replay",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-09T14:32:45Z |
---
tags:
- decisionTransformer
- deep reinforcement
datasets:
- edbeeching/decision_transformer_gym_replay
license:
- mit
---
### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920
### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0
### Dataset
- edbeeching/decision_transformer_gym_replay
- halfcheetah-expert-v2
|
sd-concepts-library/floral
|
sd-concepts-library
| 2022-09-10T19:43:07Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T17:30:24Z |
---
license: mit
---
### Floral-orchid on Stable Diffusion
This is the `<floral-orchid>` 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 a `style`:
|
MarioWasTaken/TestingPurposes
|
MarioWasTaken
| 2022-09-10T19:28:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-10T19:25:22Z |
//this is a test for now ;)
language:
"List of ISO 639-1 code for your language"
lang1
lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "any valid license identifier"
datasets:
- dataset1
- dataset2
metrics:
metric1
metric2
|
BigSalmon/InformalToFormalLincoln76ParaphraseXL
|
BigSalmon
| 2022-09-10T19:22:01Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-10T19:11:21Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln77Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln76ParaphraseXL")
```
```
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.
***
```
|
sd-concepts-library/yb-anime
|
sd-concepts-library
| 2022-09-10T18:30:00Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T18:29:55Z |
---
license: mit
---
### YB Anime on Stable Diffusion
This is the `<anime-character>` 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`:
|
sd-concepts-library/handstand
|
sd-concepts-library
| 2022-09-10T16:37:36Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T16:37:24Z |
---
license: mit
---
### handstand on Stable Diffusion
This is the `<handstand>` 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`:
|
huggingtweets/apesahoy-dril_gpt2-stefgotbooted
|
huggingtweets
| 2022-09-10T15:01:55Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-10T15:00:58Z |
---
language: en
thumbnail: http://www.huggingtweets.com/apesahoy-dril_gpt2-stefgotbooted/1662822110359/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/1514451221054173189/BWP3wqQj_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1285982491636125701/IW0v36am_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint but Al & Humongous Ape MP & Agree to disagree 🍊 🍊 🍊</div>
<div style="text-align: center; font-size: 14px;">@apesahoy-dril_gpt2-stefgotbooted</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint but Al & Humongous Ape MP & Agree to disagree 🍊 🍊 🍊.
| Data | wint but Al | Humongous Ape MP | Agree to disagree 🍊 🍊 🍊 |
| --- | --- | --- | --- |
| Tweets downloaded | 3247 | 3247 | 3194 |
| Retweets | 49 | 191 | 1674 |
| Short tweets | 57 | 607 | 445 |
| Tweets kept | 3141 | 2449 | 1075 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2eu4r1qp/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 @apesahoy-dril_gpt2-stefgotbooted's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k50hu4q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k50hu4q/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/apesahoy-dril_gpt2-stefgotbooted')
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)
|
Colorful/RTA
|
Colorful
| 2022-09-10T14:56:55Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-05T07:31:32Z |
---
license: mit
---
RTA (RepresentThemAll) is a pre-trained language model for bug reports. It can be fine-tuned on all kinds of automated software maintenance tasks associated with bug reports such as bug report summarization, duplicate bug report detection, bug priority prediction, etc.
|
DelinteNicolas/SDG_classifier_v0.0.3
|
DelinteNicolas
| 2022-09-10T14:56:54Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-10T13:22:11Z |
Fined-tuned BERT trained on 6500 images with warmup, increased epoch and decreased learning rate
|
sd-concepts-library/naf
|
sd-concepts-library
| 2022-09-10T14:46:08Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T14:46:01Z |
---
license: mit
---
### naf on Stable Diffusion
This is the `<nal>` 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 a `style`:
|
Katrzyna/bert-base-cased-finetuned-basil
|
Katrzyna
| 2022-09-10T14:29:50Z | 194 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-10T13:41:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-basil
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-cased-finetuned-basil
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8527 | 1.0 | 800 | 1.4425 |
| 1.4878 | 2.0 | 1600 | 1.2740 |
| 1.3776 | 3.0 | 2400 | 1.2273 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
huggingtweets/apesahoy-daftlimmy-women4wes
|
huggingtweets
| 2022-09-10T14:23:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-10T14:22:18Z |
---
language: en
thumbnail: http://www.huggingtweets.com/apesahoy-daftlimmy-women4wes/1662819834805/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/1392892260099010560/_gYhDAdr_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1489315073055199233/O-Sws7Go_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">twitch.tv/Limmy & Humongous Ape MP & Women for Wes</div>
<div style="text-align: center; font-size: 14px;">@apesahoy-daftlimmy-women4wes</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 twitch.tv/Limmy & Humongous Ape MP & Women for Wes.
| Data | twitch.tv/Limmy | Humongous Ape MP | Women for Wes |
| --- | --- | --- | --- |
| Tweets downloaded | 3246 | 3247 | 1807 |
| Retweets | 411 | 191 | 53 |
| Short tweets | 715 | 607 | 275 |
| Tweets kept | 2120 | 2449 | 1479 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6goa6gdz/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 @apesahoy-daftlimmy-women4wes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ltv5351j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ltv5351j/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/apesahoy-daftlimmy-women4wes')
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)
|
sd-concepts-library/riker-doll
|
sd-concepts-library
| 2022-09-10T13:36:53Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T13:36:35Z |
---
license: mit
---
### Riker Doll on Stable Diffusion
This is the `<rikerdoll>` 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`:
|
Shamus/NLLB-600m-swh_Latn-to-eng_Latn
|
Shamus
| 2022-09-10T12:55:11Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-10T08:44:32Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: NLLB-600m-swh_Latn-to-eng_Latn
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. -->
# NLLB-600m-swh_Latn-to-eng_Latn
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2490
- Bleu: 31.1907
- Gen Len: 34.464
## 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
- gradient_accumulation_steps: 7
- total_train_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 2.8224 | 0.41 | 500 | 2.3121 | 8.4908 | 34.136 |
| 2.1656 | 0.83 | 1000 | 1.9451 | 14.9983 | 33.604 |
| 1.885 | 1.24 | 1500 | 1.7385 | 18.7049 | 33.928 |
| 1.6922 | 1.66 | 2000 | 1.6102 | 21.7399 | 33.648 |
| 1.5693 | 2.07 | 2500 | 1.5175 | 23.2299 | 34.912 |
| 1.4695 | 2.49 | 3000 | 1.4552 | 24.8572 | 32.612 |
| 1.4195 | 2.9 | 3500 | 1.3948 | 26.3956 | 33.56 |
| 1.3413 | 3.32 | 4000 | 1.3564 | 27.2599 | 32.824 |
| 1.3094 | 3.73 | 4500 | 1.3263 | 27.9728 | 33.42 |
| 1.2748 | 4.15 | 5000 | 1.3044 | 28.8956 | 33.56 |
| 1.227 | 4.56 | 5500 | 1.2844 | 29.8314 | 33.552 |
| 1.2255 | 4.97 | 6000 | 1.2692 | 30.4411 | 33.716 |
| 1.191 | 5.39 | 6500 | 1.2611 | 31.1336 | 34.432 |
| 1.1842 | 5.8 | 7000 | 1.2542 | 30.8819 | 33.716 |
| 1.1712 | 6.22 | 7500 | 1.2506 | 31.528 | 33.768 |
| 1.1606 | 6.63 | 8000 | 1.2490 | 31.1907 | 34.464 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/scrap-style
|
sd-concepts-library
| 2022-09-10T12:32:37Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T18:49:07Z |
---
license: mit
---
### scrap-style on Stable Diffusion
This is the `<style-scrap>` 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 a `style`:
|
sd-concepts-library/line-style
|
sd-concepts-library
| 2022-09-10T11:01:53Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T13:16:23Z |
---
license: mit
---
### line-style on Stable Diffusion
This is the `<line-style>` 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 a `style`:
|
Shamus/mbart-large-50-many-to-many-mmt-finetuned-fij_Latn-to-eng_Latn
|
Shamus
| 2022-09-10T09:44:21Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-09T04:09:02Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-large-50-many-to-many-mmt-finetuned-fij_Latn-to-eng_Latn
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. -->
# mbart-large-50-many-to-many-mmt-finetuned-fij_Latn-to-eng_Latn
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9598
- Bleu: 45.0972
- Gen Len: 42.752
## 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
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 2.4054 | 0.49 | 500 | 1.7028 | 24.9597 | 43.04 |
| 1.6855 | 0.98 | 1000 | 1.3701 | 33.3128 | 42.2 |
| 1.4042 | 1.47 | 1500 | 1.2224 | 37.6016 | 43.536 |
| 1.2991 | 1.96 | 2000 | 1.1467 | 40.3541 | 42.428 |
| 1.1819 | 2.45 | 2500 | 1.0950 | 42.2106 | 42.58 |
| 1.1323 | 2.94 | 3000 | 1.0523 | 42.9418 | 42.76 |
| 1.0676 | 3.43 | 3500 | 1.0238 | 43.4974 | 42.684 |
| 1.0404 | 3.93 | 4000 | 1.0082 | 43.6092 | 42.616 |
| 0.9882 | 4.42 | 4500 | 0.9942 | 44.7199 | 42.912 |
| 0.982 | 4.91 | 5000 | 0.9814 | 44.8061 | 42.516 |
| 0.9372 | 5.4 | 5500 | 0.9781 | 44.3808 | 42.476 |
| 0.9382 | 5.89 | 6000 | 0.9675 | 45.0267 | 42.76 |
| 0.915 | 6.38 | 6500 | 0.9659 | 45.0073 | 42.676 |
| 0.9126 | 6.87 | 7000 | 0.9617 | 44.9582 | 42.548 |
| 0.8903 | 7.36 | 7500 | 0.9609 | 44.8713 | 42.724 |
| 0.8873 | 7.85 | 8000 | 0.9598 | 45.0972 | 42.752 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
nghuyong/ernie-1.0-base-zh
|
nghuyong
| 2022-09-10T09:37:26Z | 2,164 | 18 |
transformers
|
[
"transformers",
"pytorch",
"ernie",
"fill-mask",
"zh",
"arxiv:1904.09223",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: zh
---
# ERNIE-1.0
## Introduction
ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,
which is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking.
Experimental results show that ERNIE achieve state-of-the-art results on five Chinese natural language processing tasks including natural language inference,
semantic similarity, named entity recognition, sentiment analysis and question answering.
More detail: https://arxiv.org/abs/1904.09223
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
model = AutoModel.from_pretrained("nghuyong/ernie-1.0-base-zh")
```
## Citation
```bibtex
@article{sun2019ernie,
title={Ernie: Enhanced representation through knowledge integration},
author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua},
journal={arXiv preprint arXiv:1904.09223},
year={2019}
}
```
|
pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar
|
pedramyamini
| 2022-09-10T09:34:12Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T13:05:28Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar
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. -->
# pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5059
- Validation Loss: 0.7437
- Epoch: 4
## 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13370, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5075 | 0.7437 | 0 |
| 0.5074 | 0.7437 | 1 |
| 0.5079 | 0.7437 | 2 |
| 0.5086 | 0.7437 | 3 |
| 0.5059 | 0.7437 | 4 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
nghuyong/ernie-2.0-large-en
|
nghuyong
| 2022-09-10T09:34:12Z | 272 | 8 |
transformers
|
[
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"arxiv:1907.12412",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# ERNIE-2.0-large
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese.
More detail: https://arxiv.org/abs/1907.12412
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-large-en")
model = AutoModel.from_pretrained("nghuyong/ernie-2.0-large-en")
```
## Citation
```bibtex
@article{sun2019ernie20,
title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:1907.12412},
year={2019}
}
```
|
sd-concepts-library/stuffed-penguin-toy
|
sd-concepts-library
| 2022-09-10T09:28:40Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T05:26:08Z |
---
license: mit
---
### stuffed-penguin-toy on Stable Diffusion
This is the `<pengu-toy>` 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`:
|
nghuyong/ernie-health-zh
|
nghuyong
| 2022-09-10T09:13:33Z | 517 | 10 |
transformers
|
[
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2110.07244",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-31T08:43:33Z |
---
language: zh
---
# ernie-health-zh
## Introduction
ERNIE-health is a Chinese biomedical language model pre-trained from in-domain text of de-identified online doctor-patient dialogues, electronic medical records, and textbooks.
More detail:
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-health/
https://arxiv.org/pdf/2110.07244.pdf
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-health-zh| Chinese |Layer:12, Hidden:768, Heads:12|
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo:https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-health/
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-health-zh")
model = AutoModel.from_pretrained("nghuyong/ernie-health-zh")
```
## Citation
```bibtex
@article{wang2021building,
title={Building Chinese Biomedical Language Models via Multi-Level Text Discrimination},
author={Wang, Quan and Dai, Songtai and Xu, Benfeng and Lyu, Yajuan and Zhu, Yong and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:2110.07244},
year={2021}
}
```
|
nghuyong/ernie-3.0-nano-zh
|
nghuyong
| 2022-09-10T09:02:42Z | 284 | 24 |
transformers
|
[
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2107.02137",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-22T09:39:34Z |
---
language: zh
---
# ERNIE-3.0-nano-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo:https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import BertTokenizer, ErnieModel
tokenizer = BertTokenizer.from_pretrained("nghuyong/ernie-3.0-nano-zh")
model = ErnieModel.from_pretrained("nghuyong/ernie-3.0-nano-zh")
```
## Citation
```bibtex
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
```
|
IIIT-L/hing-roberta-finetuned-TRAC-DS
|
IIIT-L
| 2022-09-10T08:59:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-10T08:45:05Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-roberta-finetuned-TRAC-DS
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. -->
# hing-roberta-finetuned-TRAC-DS
This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1610
- Accuracy: 0.7149
- Precision: 0.6921
- Recall: 0.6946
- F1: 0.6932
## 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: 4.8796394086479776e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7229 | 2.0 | 1224 | 0.7178 | 0.6928 | 0.6815 | 0.6990 | 0.6780 |
| 0.3258 | 3.99 | 2448 | 1.1610 | 0.7149 | 0.6921 | 0.6946 | 0.6932 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nghuyong/ernie-3.0-micro-zh
|
nghuyong
| 2022-09-10T08:59:03Z | 252 | 1 |
transformers
|
[
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2107.02137",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-22T09:36:10Z |
---
language: zh
---
# ERNIE-3.0-micro-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo:https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import BertTokenizer, ErnieModel
tokenizer = BertTokenizer.from_pretrained("nghuyong/ernie-3.0-micro-zh")
model = ErnieModel.from_pretrained("nghuyong/ernie-3.0-micro-zh")
```
## Citation
```bibtex
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
```
|
hitachinsk/FGT
|
hitachinsk
| 2022-09-10T08:48:54Z | 0 | 4 | null |
[
"arxiv:2208.06768",
"license:mit",
"region:us"
] | null | 2022-09-10T08:12:06Z |
---
license: mit
---
# [ECCV 2022] Flow-Guided Transformer for Video Inpainting
[](https://github.com/hitachinsk/FGT/blob/main/LICENSE)
### [[Paper](https://arxiv.org/abs/2208.06768)] / [[Codes](https://github.com/hitachinsk/FGT)] / [[Demo](https://youtu.be/BC32n-NncPs)] / [[Project page](https://hitachinsk.github.io/publication/2022-10-01-Flow-Guided-Transformer-for-Video-Inpainting)]
This repository hosts the pretrained models of the following paper:
> **Flow-Guided Transformer for Video Inpainting**<br>
> [Kaidong Zhang](https://hitachinsk.github.io/), [Jingjing Fu](https://www.microsoft.com/en-us/research/people/jifu/) and [Dong Liu](http://staff.ustc.edu.cn/~dongeliu/)<br>
> European Conference on Computer Vision (**ECCV**), 2022<br>
## Details
There are three models in this repository, here are the details.
- `lafc.pth.tar`: The pretrained model of "Local Aggregation Flow Completion Network", which accepts a sequence of corrupted optical flows, and outputs the completed flows.
- `lafc_single.pth.tar`: The pretrained model of the single flow completion version of "Local Aggregation Flow Completion Network", it accepts **one** corrupted flow, and outputs **one** completed flow. (Only for the training of the FGT model)
- `fgt.pth.tar`: The pretrained model of "Flow Guided Transformer", which receives a sequence of corrupted frames and completed optical flows, and outputs the completed frames.
Besides the pretrained weights, we also provide the configuration files of these pretrained models.
- `LAFC_config.yaml`: The configuration file of `lafc.pth.tar`
- `LAFC_single_config.yaml`: The configuration file of `lafc_single.pth.tar`
- `FGT_config.yaml`: The configuration file of `fgt.pth.tar`
## Deployment
Download this repository to the base directory of the codes (please download that at the github page), and run "bash deploy.sh" to form the models and the cofiguration files.
After the step above, you can skip the step 1~3 in the `quick start` section in the github page and run the object removal demo directly.
|
sd-concepts-library/mycat
|
sd-concepts-library
| 2022-09-10T07:57:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T07:57:35Z |
---
license: mit
---
### mycat on Stable Diffusion
This is the `<mycat>` 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`:
|
Sebabrata/lmv2-g-bnkstm-994-doc-09-10
|
Sebabrata
| 2022-09-10T06:25:50Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-10T03:53:08Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-bnkstm-994-doc-09-10
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. -->
# lmv2-g-bnkstm-994-doc-09-10
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0926
- Account Number Precision: 0.8889
- Account Number Recall: 0.9014
- Account Number F1: 0.8951
- Account Number Number: 142
- Bank Name Precision: 0.7993
- Bank Name Recall: 0.8484
- Bank Name F1: 0.8231
- Bank Name Number: 277
- Cust Address Precision: 0.8563
- Cust Address Recall: 0.8827
- Cust Address F1: 0.8693
- Cust Address Number: 162
- Cust Name Precision: 0.9181
- Cust Name Recall: 0.9290
- Cust Name F1: 0.9235
- Cust Name Number: 169
- Ending Balance Precision: 0.7706
- Ending Balance Recall: 0.7892
- Ending Balance F1: 0.7798
- Ending Balance Number: 166
- Starting Balance Precision: 0.9051
- Starting Balance Recall: 0.8720
- Starting Balance F1: 0.8882
- Starting Balance Number: 164
- Statement Date Precision: 0.8817
- Statement Date Recall: 0.8765
- Statement Date F1: 0.8791
- Statement Date Number: 170
- Overall Precision: 0.8531
- Overall Recall: 0.8688
- Overall F1: 0.8609
- Overall Accuracy: 0.9850
## 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Account Number Precision | Account Number Recall | Account Number F1 | Account Number Number | Bank Name Precision | Bank Name Recall | Bank Name F1 | Bank Name Number | Cust Address Precision | Cust Address Recall | Cust Address F1 | Cust Address Number | Cust Name Precision | Cust Name Recall | Cust Name F1 | Cust Name Number | Ending Balance Precision | Ending Balance Recall | Ending Balance F1 | Ending Balance Number | Starting Balance Precision | Starting Balance Recall | Starting Balance F1 | Starting Balance Number | Statement Date Precision | Statement Date Recall | Statement Date F1 | Statement Date Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7648 | 1.0 | 795 | 0.2550 | 0.8514 | 0.4437 | 0.5833 | 142 | 0.6229 | 0.5307 | 0.5731 | 277 | 0.5650 | 0.7778 | 0.6545 | 162 | 0.6682 | 0.8698 | 0.7558 | 169 | 0.0 | 0.0 | 0.0 | 166 | 0.0 | 0.0 | 0.0 | 164 | 0.6040 | 0.3588 | 0.4502 | 170 | 0.6370 | 0.4352 | 0.5171 | 0.9623 |
| 0.1725 | 2.0 | 1590 | 0.1128 | 0.6067 | 0.7606 | 0.675 | 142 | 0.7294 | 0.7978 | 0.7621 | 277 | 0.8150 | 0.8704 | 0.8418 | 162 | 0.8966 | 0.9231 | 0.9096 | 169 | 0.7786 | 0.6566 | 0.7124 | 166 | 0.7576 | 0.7622 | 0.7599 | 164 | 0.8509 | 0.8059 | 0.8278 | 170 | 0.7705 | 0.7976 | 0.7838 | 0.9816 |
| 0.0877 | 3.0 | 2385 | 0.0877 | 0.7857 | 0.9296 | 0.8516 | 142 | 0.7872 | 0.8014 | 0.7943 | 277 | 0.7709 | 0.8519 | 0.8094 | 162 | 0.8827 | 0.9349 | 0.9080 | 169 | 0.7673 | 0.7349 | 0.7508 | 166 | 0.8313 | 0.8415 | 0.8364 | 164 | 0.7716 | 0.8941 | 0.8283 | 170 | 0.7985 | 0.8496 | 0.8233 | 0.9830 |
| 0.0564 | 4.0 | 3180 | 0.0826 | 0.8503 | 0.8803 | 0.8651 | 142 | 0.7566 | 0.8303 | 0.7917 | 277 | 0.7895 | 0.8333 | 0.8108 | 162 | 0.8824 | 0.8876 | 0.8850 | 169 | 0.7049 | 0.7771 | 0.7393 | 166 | 0.7717 | 0.8659 | 0.8161 | 164 | 0.8363 | 0.8412 | 0.8387 | 170 | 0.7925 | 0.8432 | 0.8171 | 0.9828 |
| 0.0402 | 5.0 | 3975 | 0.0889 | 0.8815 | 0.8380 | 0.8592 | 142 | 0.7758 | 0.7870 | 0.7814 | 277 | 0.8266 | 0.8827 | 0.8537 | 162 | 0.8983 | 0.9408 | 0.9191 | 169 | 0.6378 | 0.7108 | 0.6724 | 166 | 0.8707 | 0.7805 | 0.8232 | 164 | 0.8508 | 0.9059 | 0.8775 | 170 | 0.8124 | 0.8312 | 0.8217 | 0.9837 |
| 0.0332 | 6.0 | 4770 | 0.0864 | 0.7778 | 0.9366 | 0.8498 | 142 | 0.8175 | 0.8412 | 0.8292 | 277 | 0.8704 | 0.8704 | 0.8704 | 162 | 0.9167 | 0.9112 | 0.9139 | 169 | 0.7702 | 0.7470 | 0.7584 | 166 | 0.8424 | 0.8476 | 0.8450 | 164 | 0.8728 | 0.8882 | 0.8805 | 170 | 0.8366 | 0.86 | 0.8481 | 0.9846 |
| 0.0285 | 7.0 | 5565 | 0.0858 | 0.7516 | 0.8310 | 0.7893 | 142 | 0.8156 | 0.8303 | 0.8229 | 277 | 0.8373 | 0.8580 | 0.8476 | 162 | 0.9133 | 0.9349 | 0.9240 | 169 | 0.8288 | 0.7289 | 0.7756 | 166 | 0.8144 | 0.8293 | 0.8218 | 164 | 0.8353 | 0.8353 | 0.8353 | 170 | 0.8279 | 0.8352 | 0.8315 | 0.9840 |
| 0.027 | 8.0 | 6360 | 0.1033 | 0.8841 | 0.8592 | 0.8714 | 142 | 0.7695 | 0.8556 | 0.8103 | 277 | 0.7816 | 0.8395 | 0.8095 | 162 | 0.9075 | 0.9290 | 0.9181 | 169 | 0.8538 | 0.6687 | 0.75 | 166 | 0.8861 | 0.8537 | 0.8696 | 164 | 0.8492 | 0.8941 | 0.8711 | 170 | 0.8373 | 0.844 | 0.8406 | 0.9837 |
| 0.0237 | 9.0 | 7155 | 0.0922 | 0.8792 | 0.9225 | 0.9003 | 142 | 0.8262 | 0.8412 | 0.8336 | 277 | 0.8421 | 0.8889 | 0.8649 | 162 | 0.8983 | 0.9408 | 0.9191 | 169 | 0.8113 | 0.7771 | 0.7938 | 166 | 0.7641 | 0.9085 | 0.8301 | 164 | 0.8466 | 0.8765 | 0.8613 | 170 | 0.8358 | 0.8752 | 0.8550 | 0.9850 |
| 0.023 | 10.0 | 7950 | 0.0935 | 0.8493 | 0.8732 | 0.8611 | 142 | 0.7848 | 0.8556 | 0.8187 | 277 | 0.8246 | 0.8704 | 0.8468 | 162 | 0.9080 | 0.9349 | 0.9213 | 169 | 0.8133 | 0.7349 | 0.7722 | 166 | 0.8867 | 0.8110 | 0.8471 | 164 | 0.8735 | 0.8529 | 0.8631 | 170 | 0.8419 | 0.848 | 0.8450 | 0.9841 |
| 0.0197 | 11.0 | 8745 | 0.0926 | 0.8889 | 0.9014 | 0.8951 | 142 | 0.7993 | 0.8484 | 0.8231 | 277 | 0.8563 | 0.8827 | 0.8693 | 162 | 0.9181 | 0.9290 | 0.9235 | 169 | 0.7706 | 0.7892 | 0.7798 | 166 | 0.9051 | 0.8720 | 0.8882 | 164 | 0.8817 | 0.8765 | 0.8791 | 170 | 0.8531 | 0.8688 | 0.8609 | 0.9850 |
| 0.0193 | 12.0 | 9540 | 0.1035 | 0.7514 | 0.9366 | 0.8339 | 142 | 0.8127 | 0.8773 | 0.8438 | 277 | 0.8103 | 0.8704 | 0.8393 | 162 | 0.9405 | 0.9349 | 0.9377 | 169 | 0.6983 | 0.7530 | 0.7246 | 166 | 0.8011 | 0.8841 | 0.8406 | 164 | 0.8462 | 0.9059 | 0.8750 | 170 | 0.8081 | 0.8792 | 0.8421 | 0.9836 |
| 0.0166 | 13.0 | 10335 | 0.1077 | 0.8889 | 0.8451 | 0.8664 | 142 | 0.8062 | 0.8412 | 0.8233 | 277 | 0.7953 | 0.8395 | 0.8168 | 162 | 0.8786 | 0.8994 | 0.8889 | 169 | 0.8069 | 0.7048 | 0.7524 | 166 | 0.8167 | 0.8963 | 0.8547 | 164 | 0.8671 | 0.8824 | 0.8746 | 170 | 0.8333 | 0.844 | 0.8386 | 0.9836 |
| 0.016 | 14.0 | 11130 | 0.1247 | 0.8521 | 0.8521 | 0.8521 | 142 | 0.8456 | 0.8303 | 0.8379 | 277 | 0.8050 | 0.7901 | 0.7975 | 162 | 0.9167 | 0.9112 | 0.9139 | 169 | 0.8392 | 0.7229 | 0.7767 | 166 | 0.8521 | 0.8780 | 0.8649 | 164 | 0.9262 | 0.8118 | 0.8652 | 170 | 0.8611 | 0.828 | 0.8442 | 0.9836 |
| 0.0153 | 15.0 | 11925 | 0.1030 | 0.8280 | 0.9155 | 0.8696 | 142 | 0.7637 | 0.8051 | 0.7838 | 277 | 0.8452 | 0.8765 | 0.8606 | 162 | 0.9337 | 0.9172 | 0.9254 | 169 | 0.7551 | 0.6687 | 0.7093 | 166 | 0.8616 | 0.8354 | 0.8483 | 164 | 0.8287 | 0.8824 | 0.8547 | 170 | 0.8252 | 0.8384 | 0.8317 | 0.9834 |
| 0.0139 | 16.0 | 12720 | 0.0920 | 0.8075 | 0.9155 | 0.8581 | 142 | 0.7735 | 0.8628 | 0.8157 | 277 | 0.7663 | 0.8704 | 0.8150 | 162 | 0.8870 | 0.9290 | 0.9075 | 169 | 0.7647 | 0.7831 | 0.7738 | 166 | 0.8571 | 0.8780 | 0.8675 | 164 | 0.6630 | 0.7176 | 0.6893 | 170 | 0.7857 | 0.8504 | 0.8167 | 0.9832 |
| 0.0124 | 17.0 | 13515 | 0.1057 | 0.8013 | 0.8521 | 0.8259 | 142 | 0.8087 | 0.8087 | 0.8087 | 277 | 0.7663 | 0.8704 | 0.8150 | 162 | 0.9186 | 0.9349 | 0.9267 | 169 | 0.8322 | 0.7169 | 0.7702 | 166 | 0.8563 | 0.8720 | 0.8640 | 164 | 0.8603 | 0.9059 | 0.8825 | 170 | 0.8327 | 0.848 | 0.8403 | 0.9829 |
| 0.0135 | 18.0 | 14310 | 0.1001 | 0.8323 | 0.9085 | 0.8687 | 142 | 0.8363 | 0.8484 | 0.8423 | 277 | 0.8494 | 0.8704 | 0.8598 | 162 | 0.8462 | 0.9112 | 0.8775 | 169 | 0.7925 | 0.7590 | 0.7754 | 166 | 0.8286 | 0.8841 | 0.8555 | 164 | 0.8686 | 0.8941 | 0.8812 | 170 | 0.8368 | 0.8656 | 0.8510 | 0.9839 |
| 0.0125 | 19.0 | 15105 | 0.1200 | 0.8562 | 0.8803 | 0.8681 | 142 | 0.8 | 0.8520 | 0.8252 | 277 | 0.7705 | 0.8704 | 0.8174 | 162 | 0.8864 | 0.9231 | 0.9043 | 169 | 0.7716 | 0.7530 | 0.7622 | 166 | 0.8642 | 0.8537 | 0.8589 | 164 | 0.85 | 0.9 | 0.8743 | 170 | 0.8252 | 0.8608 | 0.8426 | 0.9843 |
| 0.0098 | 20.0 | 15900 | 0.1097 | 0.8993 | 0.8803 | 0.8897 | 142 | 0.7933 | 0.8592 | 0.8250 | 277 | 0.8144 | 0.8395 | 0.8267 | 162 | 0.8641 | 0.9408 | 0.9008 | 169 | 0.82 | 0.7410 | 0.7785 | 166 | 0.8704 | 0.8598 | 0.8650 | 164 | 0.8876 | 0.8824 | 0.8850 | 170 | 0.8434 | 0.8576 | 0.8505 | 0.9846 |
| 0.0128 | 21.0 | 16695 | 0.1090 | 0.8993 | 0.8803 | 0.8897 | 142 | 0.8294 | 0.8773 | 0.8526 | 277 | 0.8107 | 0.8457 | 0.8278 | 162 | 0.8678 | 0.8935 | 0.8805 | 169 | 0.8133 | 0.7349 | 0.7722 | 166 | 0.8218 | 0.8720 | 0.8462 | 164 | 0.8889 | 0.8471 | 0.8675 | 170 | 0.8446 | 0.852 | 0.8483 | 0.9838 |
| 0.01 | 22.0 | 17490 | 0.1280 | 0.9 | 0.8239 | 0.8603 | 142 | 0.7848 | 0.8556 | 0.8187 | 277 | 0.8057 | 0.8704 | 0.8368 | 162 | 0.8674 | 0.9290 | 0.8971 | 169 | 0.7595 | 0.7229 | 0.7407 | 166 | 0.8412 | 0.8720 | 0.8563 | 164 | 0.7989 | 0.8882 | 0.8412 | 170 | 0.8169 | 0.8528 | 0.8344 | 0.9832 |
| 0.0096 | 23.0 | 18285 | 0.1023 | 0.8889 | 0.9014 | 0.8951 | 142 | 0.8041 | 0.8448 | 0.8239 | 277 | 0.8253 | 0.8457 | 0.8354 | 162 | 0.8415 | 0.9112 | 0.875 | 169 | 0.7683 | 0.7590 | 0.7636 | 166 | 0.8118 | 0.8415 | 0.8263 | 164 | 0.7979 | 0.8824 | 0.8380 | 170 | 0.8170 | 0.8536 | 0.8349 | 0.9843 |
| 0.0088 | 24.0 | 19080 | 0.1172 | 0.8649 | 0.9014 | 0.8828 | 142 | 0.8298 | 0.8448 | 0.8372 | 277 | 0.7816 | 0.8395 | 0.8095 | 162 | 0.8674 | 0.9290 | 0.8971 | 169 | 0.7257 | 0.7651 | 0.7449 | 166 | 0.8136 | 0.8780 | 0.8446 | 164 | 0.8229 | 0.8471 | 0.8348 | 170 | 0.8155 | 0.856 | 0.8353 | 0.9829 |
| 0.0083 | 25.0 | 19875 | 0.1090 | 0.7401 | 0.9225 | 0.8213 | 142 | 0.8363 | 0.8484 | 0.8423 | 277 | 0.8057 | 0.8704 | 0.8368 | 162 | 0.8889 | 0.8994 | 0.8941 | 169 | 0.8176 | 0.7289 | 0.7707 | 166 | 0.7609 | 0.8537 | 0.8046 | 164 | 0.8488 | 0.8588 | 0.8538 | 170 | 0.8150 | 0.8528 | 0.8335 | 0.9830 |
| 0.0105 | 26.0 | 20670 | 0.1191 | 0.7241 | 0.8873 | 0.7975 | 142 | 0.7468 | 0.8412 | 0.7912 | 277 | 0.8161 | 0.8765 | 0.8452 | 162 | 0.8254 | 0.9231 | 0.8715 | 169 | 0.7384 | 0.7651 | 0.7515 | 166 | 0.8333 | 0.8537 | 0.8434 | 164 | 0.8378 | 0.9118 | 0.8732 | 170 | 0.7853 | 0.8632 | 0.8224 | 0.9814 |
| 0.0103 | 27.0 | 21465 | 0.1125 | 0.8378 | 0.8732 | 0.8552 | 142 | 0.8566 | 0.8628 | 0.8597 | 277 | 0.8046 | 0.8642 | 0.8333 | 162 | 0.8764 | 0.9231 | 0.8991 | 169 | 0.8289 | 0.7590 | 0.7925 | 166 | 0.8466 | 0.8415 | 0.8440 | 164 | 0.8929 | 0.8824 | 0.8876 | 170 | 0.8502 | 0.8584 | 0.8543 | 0.9847 |
| 0.0081 | 28.0 | 22260 | 0.1301 | 0.8601 | 0.8662 | 0.8632 | 142 | 0.8489 | 0.8520 | 0.8505 | 277 | 0.8225 | 0.8580 | 0.8399 | 162 | 0.8870 | 0.9290 | 0.9075 | 169 | 0.8067 | 0.7289 | 0.7658 | 166 | 0.8625 | 0.8415 | 0.8519 | 164 | 0.8613 | 0.8765 | 0.8688 | 170 | 0.8504 | 0.8504 | 0.8504 | 0.9850 |
| 0.0079 | 29.0 | 23055 | 0.1458 | 0.9104 | 0.8592 | 0.8841 | 142 | 0.8185 | 0.8303 | 0.8244 | 277 | 0.7730 | 0.7778 | 0.7754 | 162 | 0.8191 | 0.9112 | 0.8627 | 169 | 0.8013 | 0.7530 | 0.7764 | 166 | 0.8304 | 0.8659 | 0.8478 | 164 | 0.8941 | 0.8941 | 0.8941 | 170 | 0.8321 | 0.8408 | 0.8365 | 0.9834 |
| 0.0084 | 30.0 | 23850 | 0.1264 | 0.8435 | 0.8732 | 0.8581 | 142 | 0.8328 | 0.8628 | 0.8475 | 277 | 0.8256 | 0.8765 | 0.8503 | 162 | 0.9023 | 0.9290 | 0.9155 | 169 | 0.8531 | 0.7349 | 0.7896 | 166 | 0.8598 | 0.8598 | 0.8598 | 164 | 0.8757 | 0.8706 | 0.8732 | 170 | 0.8543 | 0.8584 | 0.8563 | 0.9848 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Osmodin/Neon_Lights
|
Osmodin
| 2022-09-10T04:07:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-10T02:17:19Z |
Custom Disco Diffusion model trained in Visions of Chaos using neon lights and signs
To use, select "custom_512x_512" for your diffusion model and point to the model .PT file under "custom_path"
|
sd-concepts-library/lego-astronaut
|
sd-concepts-library
| 2022-09-10T03:42:17Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T03:42:10Z |
---
license: mit
---
### Lego astronaut on Stable Diffusion
This is the `<lego-astronaut>` 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`:
|
skr1125/distilbert-base-uncased-distilled-clinc
|
skr1125
| 2022-09-10T02:38:56Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-10T02:29:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9429032258064516
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2002
- Accuracy: 0.9429
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6851 | 1.0 | 318 | 1.1283 | 0.7252 |
| 0.8727 | 2.0 | 636 | 0.5507 | 0.8658 |
| 0.4565 | 3.0 | 954 | 0.3243 | 0.9155 |
| 0.2876 | 4.0 | 1272 | 0.2476 | 0.9342 |
| 0.2253 | 5.0 | 1590 | 0.2237 | 0.94 |
| 0.1993 | 6.0 | 1908 | 0.2124 | 0.9413 |
| 0.186 | 7.0 | 2226 | 0.2055 | 0.9423 |
| 0.1782 | 8.0 | 2544 | 0.2030 | 0.9432 |
| 0.1746 | 9.0 | 2862 | 0.2015 | 0.9426 |
| 0.1717 | 10.0 | 3180 | 0.2002 | 0.9429 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
skr1125/distilbert-base-uncased-finetuned-clinc
|
skr1125
| 2022-09-10T01:29:16Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T19:25:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.917741935483871
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7710
- Accuracy: 0.9177
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2892 | 1.0 | 318 | 3.2830 | 0.7432 |
| 2.627 | 2.0 | 636 | 1.8728 | 0.8403 |
| 1.5429 | 3.0 | 954 | 1.1554 | 0.8910 |
| 1.0089 | 4.0 | 1272 | 0.8530 | 0.9129 |
| 0.7938 | 5.0 | 1590 | 0.7710 | 0.9177 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
sd-concepts-library/depthmap
|
sd-concepts-library
| 2022-09-10T01:21:41Z | 0 | 74 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T01:21:28Z |
---
license: mit
---
### Depthmap on Stable Diffusion
This is the `<depthmap>` 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`:
|
sd-concepts-library/borderlands
|
sd-concepts-library
| 2022-09-10T01:20:32Z | 0 | 15 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-10T01:20:17Z |
---
license: mit
---
### borderlands on Stable Diffusion
This is the `<borderlands>` 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 a `style`:
|
crumb/pruned-waifu-diffusion
|
crumb
| 2022-09-09T21:29:16Z | 0 | 14 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
text-to-image
| 2022-09-09T19:18:43Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: bigscience-bloom-rail-1.0
inference: false
---
https://huggingface.co/hakurei/waifu-diffusion
This is just the EMA version of the model. Anything other than the model required for inference has been removed. This decreases the file size by ~3 gigabytes and allows less time to be spent downloading.
|
sd-concepts-library/smw-map
|
sd-concepts-library
| 2022-09-09T21:22:42Z | 0 | 17 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T21:22:36Z |
---
license: mit
---
### smw map on Stable Diffusion
This is the `<smw-map>` 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 a `style`:
|
pinot/wav2vec2-large-xls-r-300m-j-phoneme-colab-3
|
pinot
| 2022-09-09T21:18:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-09T10:43:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_10_0
model-index:
- name: wav2vec2-large-xls-r-300m-j-phoneme-colab-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-j-phoneme-colab-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6478
- Wer: 0.3336
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 1.0 | 397 | 1.0586 | 0.9425 |
| No log | 2.0 | 794 | 0.5773 | 0.5847 |
| 1.9827 | 3.0 | 1191 | 0.5243 | 0.4882 |
| 1.9827 | 4.0 | 1588 | 0.4735 | 0.4624 |
| 1.9827 | 5.0 | 1985 | 0.4967 | 0.4789 |
| 0.6004 | 6.0 | 2382 | 0.4703 | 0.4246 |
| 0.6004 | 7.0 | 2779 | 0.4555 | 0.4194 |
| 0.4911 | 8.0 | 3176 | 0.4692 | 0.4284 |
| 0.4911 | 9.0 | 3573 | 0.4589 | 0.3997 |
| 0.4911 | 10.0 | 3970 | 0.4988 | 0.4286 |
| 0.4275 | 11.0 | 4367 | 0.4851 | 0.4153 |
| 0.4275 | 12.0 | 4764 | 0.5020 | 0.4039 |
| 0.3784 | 13.0 | 5161 | 0.5491 | 0.4169 |
| 0.3784 | 14.0 | 5558 | 0.5211 | 0.4080 |
| 0.3784 | 15.0 | 5955 | 0.5124 | 0.3950 |
| 0.3362 | 16.0 | 6352 | 0.5121 | 0.3909 |
| 0.3362 | 17.0 | 6749 | 0.5503 | 0.3728 |
| 0.3046 | 18.0 | 7146 | 0.5363 | 0.3915 |
| 0.3046 | 19.0 | 7543 | 0.6112 | 0.4076 |
| 0.3046 | 20.0 | 7940 | 0.5884 | 0.3755 |
| 0.2785 | 21.0 | 8337 | 0.5639 | 0.3793 |
| 0.2785 | 22.0 | 8734 | 0.6246 | 0.3742 |
| 0.2513 | 23.0 | 9131 | 0.6014 | 0.3714 |
| 0.2513 | 24.0 | 9528 | 0.6195 | 0.3697 |
| 0.2513 | 25.0 | 9925 | 0.6004 | 0.3729 |
| 0.2296 | 26.0 | 10322 | 0.5793 | 0.3585 |
| 0.2296 | 27.0 | 10719 | 0.6178 | 0.3628 |
| 0.2114 | 28.0 | 11116 | 0.5974 | 0.3507 |
| 0.2114 | 29.0 | 11513 | 0.6056 | 0.3432 |
| 0.2114 | 30.0 | 11910 | 0.6190 | 0.3536 |
| 0.1944 | 31.0 | 12307 | 0.6293 | 0.3550 |
| 0.1944 | 32.0 | 12704 | 0.6236 | 0.3535 |
| 0.1777 | 33.0 | 13101 | 0.6456 | 0.3503 |
| 0.1777 | 34.0 | 13498 | 0.6629 | 0.3444 |
| 0.1777 | 35.0 | 13895 | 0.6585 | 0.3432 |
| 0.1644 | 36.0 | 14292 | 0.6528 | 0.3455 |
| 0.1644 | 37.0 | 14689 | 0.6460 | 0.3437 |
| 0.1521 | 38.0 | 15086 | 0.6441 | 0.3360 |
| 0.1521 | 39.0 | 15483 | 0.6531 | 0.3350 |
| 0.1521 | 40.0 | 15880 | 0.6478 | 0.3336 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
matnun/ddpm-butterflies-128
|
matnun
| 2022-09-09T21:16:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-09T20:08:56Z |
---
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/matnun/ddpm-butterflies-128/tensorboard?#scalars)
|
erickfm/neutrally
|
erickfm
| 2022-09-09T19:48:22Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WNC",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-15T03:49:30Z |
---
language:
- en
license: apache-2.0
datasets:
- WNC
metrics:
- accuracy
---
This model is a fine-tuned checkpoint of [T5-base](https://huggingface.co/t5-base). Fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model achieves state of the art (SOTA) performance with a **BLEU score of 94.08** and an **accuracy of 48.37** on a test split of the WNC, narrowly beating out previous SOTA work from [Pryzant et al](https://nlp.stanford.edu/pubs/pryzant2020bias.pdf).
For more details about BLEU, see this [wiki](https://en.wikipedia.org/wiki/BLEU). <br>
For more details about this project visit our [web app](https://apps-summer22.ischool.berkeley.edu/neutrally/).
|
domenicrosati/deberta-v3-large-finetuned-syndag-multiclass-remove-google-scielo
|
domenicrosati
| 2022-09-09T19:48:03Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T10:37:53Z |
---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-syndag-multiclass-remove-google-scielo
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. -->
# deberta-v3-large-finetuned-syndag-multiclass-remove-google-scielo
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0214
- F1: 0.9967
- Precision: 0.9967
- Recall: 0.9967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.0169 | 1.0 | 10771 | 0.0258 | 0.9943 | 0.9943 | 0.9943 |
| 0.0122 | 2.0 | 21542 | 0.0235 | 0.9956 | 0.9956 | 0.9956 |
| 0.0111 | 3.0 | 32313 | 0.0219 | 0.9964 | 0.9964 | 0.9964 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
sd-concepts-library/tela-lenca
|
sd-concepts-library
| 2022-09-09T19:00:27Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T19:00:20Z |
---
license: mit
---
### tela lenca on Stable Diffusion
This is the `<tela-lenca>` 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`:
|
kalmuraee/tokens
|
kalmuraee
| 2022-09-09T18:55:22Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-23T00:35:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: tokens
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. -->
# tokens
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9811
- Wer: 0.4608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.5212 | 0.59 | 400 | 3.3776 | 1.0 |
| 2.4798 | 1.18 | 800 | 1.0697 | 0.7740 |
| 1.0057 | 1.77 | 1200 | 0.7077 | 0.6487 |
| 0.7731 | 2.36 | 1600 | 0.6113 | 0.5883 |
| 0.6917 | 2.94 | 2000 | 0.5618 | 0.5573 |
| 0.5844 | 3.53 | 2400 | 0.5610 | 0.5532 |
| 0.5606 | 4.12 | 2800 | 0.5584 | 0.5484 |
| 0.4973 | 4.71 | 3200 | 0.5466 | 0.5333 |
| 0.4721 | 5.3 | 3600 | 0.5495 | 0.5178 |
| 0.4439 | 5.89 | 4000 | 0.5667 | 0.5237 |
| 0.3965 | 6.48 | 4400 | 0.5865 | 0.5322 |
| 0.3876 | 7.07 | 4800 | 0.6099 | 0.5135 |
| 0.3407 | 7.66 | 5200 | 0.5891 | 0.5228 |
| 0.33 | 8.25 | 5600 | 0.6135 | 0.5072 |
| 0.3032 | 8.84 | 6000 | 0.6004 | 0.5028 |
| 0.2706 | 9.43 | 6400 | 0.6321 | 0.4991 |
| 0.2709 | 10.01 | 6800 | 0.6541 | 0.5051 |
| 0.2373 | 10.6 | 7200 | 0.6613 | 0.5119 |
| 0.2284 | 11.19 | 7600 | 0.6798 | 0.5086 |
| 0.212 | 11.78 | 8000 | 0.6509 | 0.4910 |
| 0.1983 | 12.37 | 8400 | 0.7018 | 0.5043 |
| 0.1947 | 12.96 | 8800 | 0.6826 | 0.4965 |
| 0.1717 | 13.55 | 9200 | 0.7056 | 0.4828 |
| 0.1741 | 14.14 | 9600 | 0.7544 | 0.5060 |
| 0.1626 | 14.73 | 10000 | 0.7331 | 0.4915 |
| 0.1529 | 15.32 | 10400 | 0.7518 | 0.4772 |
| 0.1504 | 15.91 | 10800 | 0.7362 | 0.4732 |
| 0.1401 | 16.49 | 11200 | 0.7179 | 0.4769 |
| 0.1335 | 17.08 | 11600 | 0.7716 | 0.4826 |
| 0.1185 | 17.67 | 12000 | 0.7465 | 0.4798 |
| 0.1182 | 18.26 | 12400 | 0.8105 | 0.4733 |
| 0.1135 | 18.85 | 12800 | 0.7693 | 0.4743 |
| 0.1098 | 19.44 | 13200 | 0.8362 | 0.4888 |
| 0.1023 | 20.03 | 13600 | 0.8427 | 0.4768 |
| 0.1003 | 20.62 | 14000 | 0.8079 | 0.4741 |
| 0.0936 | 21.21 | 14400 | 0.8551 | 0.4651 |
| 0.0875 | 21.8 | 14800 | 0.8462 | 0.4712 |
| 0.0843 | 22.39 | 15200 | 0.9177 | 0.4782 |
| 0.0846 | 22.97 | 15600 | 0.8618 | 0.4735 |
| 0.08 | 23.56 | 16000 | 0.9017 | 0.4687 |
| 0.0789 | 24.15 | 16400 | 0.9034 | 0.4659 |
| 0.0717 | 24.74 | 16800 | 0.9690 | 0.4734 |
| 0.0714 | 25.33 | 17200 | 0.9395 | 0.4677 |
| 0.0699 | 25.92 | 17600 | 0.9222 | 0.4608 |
| 0.0658 | 26.51 | 18000 | 0.9222 | 0.4621 |
| 0.0612 | 27.1 | 18400 | 0.9691 | 0.4586 |
| 0.0583 | 27.69 | 18800 | 0.9647 | 0.4581 |
| 0.0596 | 28.28 | 19200 | 0.9820 | 0.4614 |
| 0.056 | 28.87 | 19600 | 0.9795 | 0.4596 |
| 0.055 | 29.45 | 20000 | 0.9811 | 0.4608 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
svo2/roberta-finetuned-timeentities2
|
svo2
| 2022-09-09T18:49:41Z | 82 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-06T18:19:59Z |
---
license: cc-by-4.0
tags:
- generated_from_keras_callback
model-index:
- name: skandaonsolve/roberta-finetuned-timeentities2
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. -->
# skandaonsolve/roberta-finetuned-timeentities2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0243
- Epoch: 9
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.5654 | 0 |
| 0.3787 | 1 |
| 0.2795 | 2 |
| 0.2002 | 3 |
| 0.1281 | 4 |
| 0.0848 | 5 |
| 0.0596 | 6 |
| 0.0422 | 7 |
| 0.0332 | 8 |
| 0.0243 | 9 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
jquinley/distilbert-amazon-shoe-reviews
|
jquinley
| 2022-09-09T18:34:51Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T18:18:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-amazon-shoe-reviews
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-amazon-shoe-reviews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/mtl-longsky
|
sd-concepts-library
| 2022-09-09T18:26:29Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T18:26:22Z |
---
license: mit
---
### mtl-longsky on Stable Diffusion
This is the `<mtl-longsky>` 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`:
|
BigSalmon/InformalToFormalLincoln76Paraphrase
|
BigSalmon
| 2022-09-09T18:19:31Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-06T21:35:15Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln75Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln75Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/GPT_NEOInformalToFormal
```
```
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:
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
original: chrome extensions [MASK] the ability to accomplish everyday tasks.
infill: chrome extensions ( expedite / streamline / facilitate ) the ability to accomplish everyday tasks.
***
original: democracy is way of organizing a society in which the supreme power is [MASK] the people.
infill: original: democracy is way of organizing a society in which the supreme power is ( vested in / exercised by / delegated to / wielded by ) the people.
***
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- 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:
```
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]
```
antonyms also work very well.
```
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.
```
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:
```
|
tavakolih/all-MiniLM-L6-v2-pubmed
|
tavakolih
| 2022-09-09T18:02:53Z | 60 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"dataset:pubmed",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-09T18:02:38Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- pubmed
---
# tavakolih/all-MiniLM-L6-v2-pubmed
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('tavakolih/all-MiniLM-L6-v2-pubmed')
embeddings = model.encode(sentences)
print(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=tavakolih/all-MiniLM-L6-v2-pubmed)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 625 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"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": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
svo2/roberta-finetuned-timeentities
|
svo2
| 2022-09-09T17:45:32Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-08-17T19:32:11Z |
---
license: cc-by-4.0
tags:
- generated_from_keras_callback
model-index:
- name: skandaonsolve/roberta-finetuned-timeentities
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. -->
# skandaonsolve/roberta-finetuned-timeentities
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0239
- Epoch: 9
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3030, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.5969 | 0 |
| 0.3778 | 1 |
| 0.2669 | 2 |
| 0.1882 | 3 |
| 0.1249 | 4 |
| 0.0864 | 5 |
| 0.0566 | 6 |
| 0.0417 | 7 |
| 0.0345 | 8 |
| 0.0239 | 9 |
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
autonomous019/distilbert_ell3
|
autonomous019
| 2022-09-09T17:39:30Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:bsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T11:55:39Z |
---
license: bsd
---
a lightweight solution for the Kaggle ELL competition using distilbert
Info about the Kaggle ELL competition: <a href="https://www.kaggle.com/competitions/feedback-prize-english-language-learning/code">https://www.kaggle.com/competitions/feedback-prize-english-language-learning/code</a>
|
cholling/distilbert-amazon-shoe-reviews
|
cholling
| 2022-09-09T17:33:31Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T17:32:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews
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-amazon-shoe-reviews
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:
- Loss: 0.9532
- Accuracy: 0.5779
- F1: [0.62616119 0.46456105 0.50993865 0.55755123 0.734375 ]
- Precision: [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507]
- Recall: [0.6247495 0.46237624 0.52983172 0.53313982 0.74488753]
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 0.9713 | 1.0 | 2813 | 0.9532 | 0.5779 | [0.62616119 0.46456105 0.50993865 0.55755123 0.734375 ] | [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507] | [0.6247495 0.46237624 0.52983172 0.53313982 0.74488753] |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mgoudarz/xlm-roberta-base-finetuned-panx-en
|
mgoudarz
| 2022-09-09T17:18:38Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-07T15:14:49Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: train
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7032474804031354
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- F1: 0.7032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1504 | 1.0 | 50 | 0.5992 | 0.4786 |
| 0.5147 | 2.0 | 100 | 0.4307 | 0.6468 |
| 0.3717 | 3.0 | 150 | 0.3932 | 0.7032 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
mgoudarz/xlm-roberta-base-finetuned-panx-it
|
mgoudarz
| 2022-09-09T17:10:55Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-07T14:58:15Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: train
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8245828245828245
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2401
- F1: 0.8246
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8187 | 1.0 | 70 | 0.3325 | 0.7337 |
| 0.2829 | 2.0 | 140 | 0.2554 | 0.8003 |
| 0.1894 | 3.0 | 210 | 0.2401 | 0.8246 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/dong-ho2
|
sd-concepts-library
| 2022-09-09T17:10:25Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T16:44:48Z |
---
license: mit
---
### dong ho2 on Stable Diffusion
This is the `<dong-ho-2>` 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 a `style`:
|
mgoudarz/xlm-roberta-base-finetuned-panx-fr
|
mgoudarz
| 2022-09-09T17:03:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-07T14:39:22Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8299296953465015
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2848
- F1: 0.8299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 |
| 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 |
| 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/ruan-jia
|
sd-concepts-library
| 2022-09-09T16:51:18Z | 0 | 23 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T16:51:11Z |
---
license: mit
---
### Ruan Jia on Stable Diffusion
This is the `<ruan-jia>` 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 a `style`:
|
slarionne/q-FrozenLake-dumb
|
slarionne
| 2022-09-09T16:40:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-09T16:00:20Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-dumb
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="slarionne/q-FrozenLake-dumb", 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"])
```
|
sd-concepts-library/kojima-ayami
|
sd-concepts-library
| 2022-09-09T16:20:40Z | 0 | 11 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T16:20:28Z |
---
license: mit
---
### KOJIMA Ayami on Stable Diffusion
This is the `<KOJIMA>` 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 a `style`:
|
has-abi/extended_distilBERT-finetuned-resumes-sections
|
has-abi
| 2022-09-09T16:12:23Z | 138 | 12 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T10:36:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: extended_distilBERT-finetuned-resumes-sections
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. -->
# extended_distilBERT-finetuned-resumes-sections
This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0321
- F1: 0.9735
- Roc Auc: 0.9850
- Accuracy: 0.9715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.0283 | 1.0 | 2213 | 0.0247 | 0.9610 | 0.9763 | 0.9539 |
| 0.0153 | 2.0 | 4426 | 0.0223 | 0.9634 | 0.9789 | 0.9593 |
| 0.01 | 3.0 | 6639 | 0.0199 | 0.9702 | 0.9835 | 0.9675 |
| 0.0073 | 4.0 | 8852 | 0.0218 | 0.9710 | 0.9838 | 0.9690 |
| 0.0063 | 5.0 | 11065 | 0.0244 | 0.9706 | 0.9835 | 0.9684 |
| 0.0037 | 6.0 | 13278 | 0.0251 | 0.9700 | 0.9833 | 0.9684 |
| 0.004 | 7.0 | 15491 | 0.0273 | 0.9712 | 0.9837 | 0.9693 |
| 0.003 | 8.0 | 17704 | 0.0266 | 0.9719 | 0.9841 | 0.9695 |
| 0.0027 | 9.0 | 19917 | 0.0294 | 0.9697 | 0.9831 | 0.9679 |
| 0.0014 | 10.0 | 22130 | 0.0275 | 0.9714 | 0.9844 | 0.9690 |
| 0.0016 | 11.0 | 24343 | 0.0299 | 0.9714 | 0.9839 | 0.9697 |
| 0.0013 | 12.0 | 26556 | 0.0297 | 0.9719 | 0.9852 | 0.9697 |
| 0.0006 | 13.0 | 28769 | 0.0312 | 0.9711 | 0.9843 | 0.9697 |
| 0.0004 | 14.0 | 30982 | 0.0305 | 0.9731 | 0.9849 | 0.9720 |
| 0.0004 | 15.0 | 33195 | 0.0312 | 0.9723 | 0.9845 | 0.9704 |
| 0.0005 | 16.0 | 35408 | 0.0331 | 0.9716 | 0.9843 | 0.9697 |
| 0.0006 | 17.0 | 37621 | 0.0321 | 0.9735 | 0.9850 | 0.9715 |
| 0.0004 | 18.0 | 39834 | 0.0322 | 0.9731 | 0.9850 | 0.9711 |
| 0.0003 | 19.0 | 42047 | 0.0332 | 0.9722 | 0.9847 | 0.9706 |
| 0.0004 | 20.0 | 44260 | 0.0334 | 0.9720 | 0.9846 | 0.9704 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
orhanxakarsu/turkishPoe-generation
|
orhanxakarsu
| 2022-09-09T15:59:51Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-09T13:42:34Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: orhanxakarsu/turkishPoe-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. -->
# orhanxakarsu/turkishPoe-generation
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.5336
- Validation Loss: 6.4577
- Epoch: 5
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2485, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3461 | 6.4576 | 0 |
| 6.5336 | 6.4578 | 1 |
| 6.5337 | 6.4578 | 2 |
| 6.5334 | 6.4575 | 3 |
| 6.5335 | 6.4574 | 4 |
| 6.5336 | 6.4577 | 5 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DominikB/autotrain-person-classifier-1401653210
|
DominikB
| 2022-09-09T15:34:30Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"vision",
"image-classification",
"dataset:DominikB/autotrain-data-person-classifier",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-09T14:15:55Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- DominikB/autotrain-data-person-classifier
widget:
- src: https://100-pics.net/images/answers/de/schauspieler/schauspieler_22135_191026.jpeg
example_title: Jack Black 1
- src: https://assets.rebelmouse.io/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjE1MTE5NS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTcxNzUyMDE1MX0.JN64_PUw8Ldz5QZ5DV9ZGZ5VgO6x9nEFqhGFvc6sKMY/img.jpg?width=1200&height=600&coordinates=0%2C408%2C0%2C408
example_title: Jack Black 2
- src: https://nationaltoday.com/wp-content/uploads/2022/05/107-Johnny-Depp.jpg
example_title: Johnny Depp 1
- src: https://de.web.img2.acsta.net/newsv7/22/09/08/09/10/3547575.jpg
example_title: Johnny Depp 2
co2_eq_emissions:
emissions: 0.0143182831771501
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1401653210
- CO2 Emissions (in grams): 0.0143
## Validation Metrics
- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
|
sd-concepts-library/moeb-style
|
sd-concepts-library
| 2022-09-09T15:31:33Z | 0 | 29 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T15:31:27Z |
---
license: mit
---
### Moeb Style on Stable Diffusion
This is the `<moe-bius>` 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 a `style`:
|
KoboldAI/OPT-13B-Erebus
|
KoboldAI
| 2022-09-09T13:54:35Z | 7,227 | 235 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-09-09T09:11:05Z |
---
language: en
license: other
commercial: no
inference: false
---
# OPT 13B - Erebus
## Model description
This is the second generation of the original Shinen made by Mr. Seeker. The full dataset consists of 6 different sources, all surrounding the "Adult" theme. The name "Erebus" comes from the greek mythology, also named "darkness". This is in line with Shin'en, or "deep abyss". For inquiries, please contact the KoboldAI community. **Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training data
The data can be divided in 6 different datasets:
- Literotica (everything with 4.5/5 or higher)
- Sexstories (everything with 90 or higher)
- Dataset-G (private dataset of X-rated stories)
- Doc's Lab (all stories)
- Pike Dataset (novels with "adult" rating)
- SoFurry (collection of various animals)
The dataset uses `[Genre: <comma-separated list of genres>]` for tagging.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/OPT-13B-Erebus')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
```
## Limitations and biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). **Warning: This model has a very strong NSFW bias!**
### License
OPT-13B is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
### BibTeX entry and citation info
```
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
IIIT-L/xlm-roberta-base-finetuned-non-code-mixed-DS
|
IIIT-L
| 2022-09-09T13:47:31Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T13:24:29Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-finetuned-non-code-mixed-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-non-code-mixed-DS
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1771
- Accuracy: 0.6365
- Precision: 0.6252
- Recall: 0.6242
- F1: 0.6242
## 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: 1.6820964947491663e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9475 | 2.0 | 926 | 0.8620 | 0.6278 | 0.6197 | 0.6042 | 0.6081 |
| 0.6661 | 3.99 | 1852 | 0.9578 | 0.6451 | 0.6356 | 0.6281 | 0.6301 |
| 0.4457 | 5.99 | 2778 | 1.1771 | 0.6365 | 0.6252 | 0.6242 | 0.6242 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sd-concepts-library/venice
|
sd-concepts-library
| 2022-09-09T13:42:09Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T13:42:04Z |
---
license: mit
---
### venice on Stable Diffusion
This is the `<venice>` 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`:
|
sd-concepts-library/zdenek-art
|
sd-concepts-library
| 2022-09-09T12:37:14Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T12:37:01Z |
---
license: mit
---
### zdenek art on Stable Diffusion
This is the `<zdenek-artwork>` 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`:
|
dhruv0808/autotrain-ad_detection_ver_1-1395053127
|
dhruv0808
| 2022-09-09T12:35:54Z | 223 | 1 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"vision",
"image-classification",
"dataset:dhruv0808/autotrain-data-ad_detection_ver_1",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-09T12:33:49Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- dhruv0808/autotrain-data-ad_detection_ver_1
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.009652698067986935
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1395053127
- CO2 Emissions (in grams): 0.0097
## Validation Metrics
- Loss: 0.178
- Accuracy: 0.941
- Precision: 0.947
- Recall: 0.947
- AUC: 0.974
- F1: 0.947
|
Felix92/doctr-dummy-tf-sar-resnet31
|
Felix92
| 2022-09-09T12:28:56Z | 1 | 0 |
transformers
|
[
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-09-09T12:28:47Z |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
Felix92/doctr-dummy-torch-sar-resnet31
|
Felix92
| 2022-09-09T11:59:17Z | 304 | 0 |
transformers
|
[
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-09-09T11:59:07Z |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
DonatoFrancioso/NLP2122_FranciosoDonato
|
DonatoFrancioso
| 2022-09-09T09:52:49Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T09:06:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: NLP2122_FranciosoDonato
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. -->
# NLP2122_FranciosoDonato
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:
- Loss: 0.8885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.21.3
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
mgoudarz/xlm-roberta-base-finetuned-panx-all
|
mgoudarz
| 2022-09-09T09:52:06Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-09T09:28:52Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1757
- F1: 0.8513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2986 | 1.0 | 835 | 0.1939 | 0.8077 |
| 0.1547 | 2.0 | 1670 | 0.1813 | 0.8351 |
| 0.1003 | 3.0 | 2505 | 0.1757 | 0.8513 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/fergal-cat
|
sd-concepts-library
| 2022-09-09T09:37:29Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T09:37:22Z |
---
license: mit
---
### fergal_cat on Stable Diffusion
This is the `<fergal-cat>` 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`:
|
sd-concepts-library/orangejacket
|
sd-concepts-library
| 2022-09-09T08:53:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T08:53:32Z |
---
license: mit
---
### <orangejacket> on Stable Diffusion
This is the `<orangejacket>` 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 `style`:
|
Sebabrata/lmv2-g-recp-992-doc-09-09
|
Sebabrata
| 2022-09-09T08:23:00Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-09T05:55:50Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-recp-992-doc-09-09
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. -->
# lmv2-g-recp-992-doc-09-09
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2241
- Purchase Time Precision: 0.872
- Purchase Time Recall: 0.8516
- Purchase Time F1: 0.8617
- Purchase Time Number: 128
- Receipt Date Precision: 0.8713
- Receipt Date Recall: 0.8817
- Receipt Date F1: 0.8765
- Receipt Date Number: 169
- Sub Total Precision: 0.8211
- Sub Total Recall: 0.7091
- Sub Total F1: 0.7610
- Sub Total Number: 110
- Supplier Address Precision: 0.7009
- Supplier Address Recall: 0.7193
- Supplier Address F1: 0.7100
- Supplier Address Number: 114
- Supplier Name Precision: 0.7442
- Supplier Name Recall: 0.7191
- Supplier Name F1: 0.7314
- Supplier Name Number: 267
- Tip Amount Precision: 0.6667
- Tip Amount Recall: 1.0
- Tip Amount F1: 0.8
- Tip Amount Number: 2
- Total Precision: 0.8436
- Total Recall: 0.8251
- Total F1: 0.8343
- Total Number: 183
- Total Tax Amount Precision: 0.8361
- Total Tax Amount Recall: 0.7846
- Total Tax Amount F1: 0.8095
- Total Tax Amount Number: 65
- Overall Precision: 0.8067
- Overall Recall: 0.7842
- Overall F1: 0.7953
- Overall Accuracy: 0.9728
## 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Purchase Time Precision | Purchase Time Recall | Purchase Time F1 | Purchase Time Number | Receipt Date Precision | Receipt Date Recall | Receipt Date F1 | Receipt Date Number | Sub Total Precision | Sub Total Recall | Sub Total F1 | Sub Total Number | Supplier Address Precision | Supplier Address Recall | Supplier Address F1 | Supplier Address Number | Supplier Name Precision | Supplier Name Recall | Supplier Name F1 | Supplier Name Number | Tip Amount Precision | Tip Amount Recall | Tip Amount F1 | Tip Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Total Tax Amount Precision | Total Tax Amount Recall | Total Tax Amount F1 | Total Tax Amount Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.9017 | 1.0 | 793 | 0.3748 | 0.0 | 0.0 | 0.0 | 128 | 0.5 | 0.0710 | 0.1244 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.4632 | 0.5526 | 0.504 | 114 | 0.3724 | 0.2022 | 0.2621 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7387 | 0.4481 | 0.5578 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.4637 | 0.2033 | 0.2827 | 0.9330 |
| 0.2651 | 2.0 | 1586 | 0.2025 | 0.8 | 0.8438 | 0.8213 | 128 | 0.8274 | 0.8225 | 0.8249 | 169 | 0.4 | 0.0182 | 0.0348 | 110 | 0.5329 | 0.7105 | 0.6090 | 114 | 0.5886 | 0.6592 | 0.6219 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.5720 | 0.8470 | 0.6828 | 183 | 1.0 | 0.0308 | 0.0597 | 65 | 0.6424 | 0.6387 | 0.6406 | 0.9624 |
| 0.1403 | 3.0 | 2379 | 0.1585 | 0.8248 | 0.8828 | 0.8528 | 128 | 0.7897 | 0.9112 | 0.8462 | 169 | 0.7054 | 0.7182 | 0.7117 | 110 | 0.5931 | 0.7544 | 0.6641 | 114 | 0.6288 | 0.6217 | 0.6252 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7877 | 0.7705 | 0.7790 | 183 | 0.8276 | 0.7385 | 0.7805 | 65 | 0.7220 | 0.7582 | 0.7397 | 0.9683 |
| 0.0935 | 4.0 | 3172 | 0.1771 | 0.7891 | 0.7891 | 0.7891 | 128 | 0.6474 | 0.7278 | 0.6852 | 169 | 0.8205 | 0.5818 | 0.6809 | 110 | 0.6074 | 0.7193 | 0.6586 | 114 | 0.6548 | 0.6891 | 0.6715 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8476 | 0.7596 | 0.8012 | 183 | 0.75 | 0.2308 | 0.3529 | 65 | 0.7108 | 0.6821 | 0.6962 | 0.9648 |
| 0.0684 | 5.0 | 3965 | 0.1552 | 0.9237 | 0.8516 | 0.8862 | 128 | 0.8362 | 0.8757 | 0.8555 | 169 | 0.7629 | 0.6727 | 0.7150 | 110 | 0.6029 | 0.7193 | 0.6560 | 114 | 0.7167 | 0.6442 | 0.6785 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8128 | 0.8306 | 0.8216 | 183 | 0.7937 | 0.7692 | 0.7813 | 65 | 0.7731 | 0.7582 | 0.7656 | 0.9696 |
| 0.0491 | 6.0 | 4758 | 0.1702 | 0.8760 | 0.8828 | 0.8794 | 128 | 0.8352 | 0.8698 | 0.8522 | 169 | 0.8056 | 0.7909 | 0.7982 | 110 | 0.5894 | 0.7807 | 0.6717 | 114 | 0.6844 | 0.6742 | 0.6792 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8778 | 0.8634 | 0.8705 | 183 | 0.9074 | 0.7538 | 0.8235 | 65 | 0.7757 | 0.7929 | 0.7842 | 0.9703 |
| 0.0472 | 7.0 | 5551 | 0.2037 | 0.8952 | 0.8672 | 0.8810 | 128 | 0.8876 | 0.8876 | 0.8876 | 169 | 0.8 | 0.7273 | 0.7619 | 110 | 0.6557 | 0.7018 | 0.6780 | 114 | 0.7953 | 0.6404 | 0.7095 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8795 | 0.7978 | 0.8367 | 183 | 0.9394 | 0.4769 | 0.6327 | 65 | 0.8278 | 0.7408 | 0.7819 | 0.9701 |
| 0.0361 | 8.0 | 6344 | 0.1862 | 0.875 | 0.8203 | 0.8468 | 128 | 0.7978 | 0.8402 | 0.8184 | 169 | 0.7739 | 0.8091 | 0.7911 | 110 | 0.6512 | 0.7368 | 0.6914 | 114 | 0.6906 | 0.6854 | 0.6880 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8486 | 0.8579 | 0.8533 | 183 | 0.6780 | 0.6154 | 0.6452 | 65 | 0.7612 | 0.7707 | 0.7659 | 0.9701 |
| 0.0318 | 9.0 | 7137 | 0.1889 | 0.9 | 0.8438 | 0.8710 | 128 | 0.8743 | 0.8639 | 0.8690 | 169 | 0.875 | 0.6364 | 0.7368 | 110 | 0.6417 | 0.6754 | 0.6581 | 114 | 0.6914 | 0.6966 | 0.6940 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7833 | 0.8689 | 0.8238 | 183 | 0.7797 | 0.7077 | 0.7419 | 65 | 0.7772 | 0.7630 | 0.7701 | 0.9697 |
| 0.3481 | 10.0 | 7930 | 0.7581 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
| 0.7157 | 11.0 | 8723 | 0.7634 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
| 0.7136 | 12.0 | 9516 | 0.7611 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
| 0.1095 | 13.0 | 10309 | 0.1744 | 0.8284 | 0.8672 | 0.8473 | 128 | 0.8531 | 0.8935 | 0.8728 | 169 | 0.7717 | 0.6455 | 0.7030 | 110 | 0.5662 | 0.6754 | 0.6160 | 114 | 0.6424 | 0.6929 | 0.6667 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8211 | 0.8525 | 0.8365 | 183 | 0.8214 | 0.7077 | 0.7603 | 65 | 0.7428 | 0.7678 | 0.7551 | 0.9698 |
| 0.0316 | 14.0 | 11102 | 0.1812 | 0.8943 | 0.8594 | 0.8765 | 128 | 0.8409 | 0.8757 | 0.8580 | 169 | 0.8415 | 0.6273 | 0.7188 | 110 | 0.5714 | 0.6667 | 0.6154 | 114 | 0.6279 | 0.7079 | 0.6655 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8256 | 0.8798 | 0.8519 | 183 | 0.8136 | 0.7385 | 0.7742 | 65 | 0.7495 | 0.7726 | 0.7609 | 0.9703 |
| 0.0226 | 15.0 | 11895 | 0.2132 | 0.8843 | 0.8359 | 0.8594 | 128 | 0.8476 | 0.8225 | 0.8348 | 169 | 0.7525 | 0.6909 | 0.7204 | 110 | 0.5804 | 0.7281 | 0.6459 | 114 | 0.6679 | 0.6929 | 0.6801 | 267 | 0.2 | 0.5 | 0.2857 | 2 | 0.8571 | 0.8525 | 0.8548 | 183 | 0.4835 | 0.6769 | 0.5641 | 65 | 0.7297 | 0.7620 | 0.7455 | 0.9672 |
| 0.0241 | 16.0 | 12688 | 0.1962 | 0.8984 | 0.8984 | 0.8984 | 128 | 0.8613 | 0.8817 | 0.8713 | 169 | 0.6615 | 0.7818 | 0.7167 | 110 | 0.6 | 0.7368 | 0.6614 | 114 | 0.6431 | 0.7154 | 0.6773 | 267 | 0.0833 | 0.5 | 0.1429 | 2 | 0.8795 | 0.7978 | 0.8367 | 183 | 0.7727 | 0.7846 | 0.7786 | 65 | 0.7401 | 0.7929 | 0.7656 | 0.9709 |
| 0.0155 | 17.0 | 13481 | 0.1995 | 0.8906 | 0.8906 | 0.8906 | 128 | 0.8678 | 0.8935 | 0.8805 | 169 | 0.7438 | 0.8182 | 0.7792 | 110 | 0.6042 | 0.7632 | 0.6744 | 114 | 0.6193 | 0.7678 | 0.6856 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8325 | 0.8689 | 0.8503 | 183 | 0.8644 | 0.7846 | 0.8226 | 65 | 0.7467 | 0.8266 | 0.7846 | 0.9696 |
| 0.0165 | 18.0 | 14274 | 0.2402 | 0.8966 | 0.8125 | 0.8525 | 128 | 0.8293 | 0.8047 | 0.8168 | 169 | 0.8118 | 0.6273 | 0.7077 | 110 | 0.5766 | 0.6930 | 0.6295 | 114 | 0.7220 | 0.6517 | 0.6850 | 267 | 1.0 | 1.0 | 1.0 | 2 | 0.8603 | 0.8415 | 0.8508 | 183 | 0.7826 | 0.5538 | 0.6486 | 65 | 0.7773 | 0.7264 | 0.7510 | 0.9683 |
| 0.0721 | 19.0 | 15067 | 0.2718 | 0.3506 | 0.6328 | 0.4513 | 128 | 0.7268 | 0.7870 | 0.7557 | 169 | 0.7742 | 0.4364 | 0.5581 | 110 | 0.5271 | 0.5965 | 0.5597 | 114 | 0.5294 | 0.5056 | 0.5172 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7526 | 0.7978 | 0.7745 | 183 | 0.7414 | 0.6615 | 0.6992 | 65 | 0.5881 | 0.6301 | 0.6084 | 0.9564 |
| 0.0136 | 20.0 | 15860 | 0.2213 | 0.8651 | 0.8516 | 0.8583 | 128 | 0.8555 | 0.8757 | 0.8655 | 169 | 0.8191 | 0.7 | 0.7549 | 110 | 0.6103 | 0.7281 | 0.664 | 114 | 0.6977 | 0.6742 | 0.6857 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8571 | 0.8197 | 0.8380 | 183 | 0.7656 | 0.7538 | 0.7597 | 65 | 0.7760 | 0.7678 | 0.7719 | 0.9697 |
| 0.0111 | 21.0 | 16653 | 0.2241 | 0.872 | 0.8516 | 0.8617 | 128 | 0.8713 | 0.8817 | 0.8765 | 169 | 0.8211 | 0.7091 | 0.7610 | 110 | 0.7009 | 0.7193 | 0.7100 | 114 | 0.7442 | 0.7191 | 0.7314 | 267 | 0.6667 | 1.0 | 0.8 | 2 | 0.8436 | 0.8251 | 0.8343 | 183 | 0.8361 | 0.7846 | 0.8095 | 65 | 0.8067 | 0.7842 | 0.7953 | 0.9728 |
| 0.011 | 22.0 | 17446 | 0.2206 | 0.7770 | 0.8984 | 0.8333 | 128 | 0.8270 | 0.9053 | 0.8644 | 169 | 0.8586 | 0.7727 | 0.8134 | 110 | 0.5985 | 0.6930 | 0.6423 | 114 | 0.6618 | 0.6742 | 0.6679 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8870 | 0.8579 | 0.8722 | 183 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.7579 | 0.7900 | 0.7736 | 0.9697 |
| 0.0104 | 23.0 | 18239 | 0.2571 | 0.9310 | 0.8438 | 0.8852 | 128 | 0.875 | 0.8698 | 0.8724 | 169 | 0.8316 | 0.7182 | 0.7707 | 110 | 0.6417 | 0.6754 | 0.6581 | 114 | 0.7386 | 0.6667 | 0.7008 | 267 | 0.1429 | 0.5 | 0.2222 | 2 | 0.8579 | 0.8579 | 0.8579 | 183 | 0.7812 | 0.7692 | 0.7752 | 65 | 0.8018 | 0.7678 | 0.7844 | 0.9705 |
| 0.0132 | 24.0 | 19032 | 0.2252 | 0.8810 | 0.8672 | 0.8740 | 128 | 0.8297 | 0.8935 | 0.8604 | 169 | 0.7607 | 0.8091 | 0.7841 | 110 | 0.6074 | 0.7193 | 0.6586 | 114 | 0.6578 | 0.7416 | 0.6972 | 267 | 0.3333 | 1.0 | 0.5 | 2 | 0.8659 | 0.8470 | 0.8564 | 183 | 0.7966 | 0.7231 | 0.7581 | 65 | 0.7557 | 0.8044 | 0.7793 | 0.9717 |
| 0.0114 | 25.0 | 19825 | 0.2303 | 0.8917 | 0.8359 | 0.8629 | 128 | 0.8947 | 0.9053 | 0.9000 | 169 | 0.8144 | 0.7182 | 0.7633 | 110 | 0.6296 | 0.7456 | 0.6827 | 114 | 0.6937 | 0.7041 | 0.6989 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8533 | 0.8579 | 0.8556 | 183 | 0.8913 | 0.6308 | 0.7387 | 65 | 0.7912 | 0.7813 | 0.7862 | 0.9705 |
| 0.0121 | 26.0 | 20618 | 0.2485 | 0.8810 | 0.8672 | 0.8740 | 128 | 0.8793 | 0.9053 | 0.8921 | 169 | 0.8667 | 0.7091 | 0.7800 | 110 | 0.5926 | 0.7018 | 0.6426 | 114 | 0.7446 | 0.6442 | 0.6908 | 267 | 0.25 | 0.5 | 0.3333 | 2 | 0.8361 | 0.8361 | 0.8361 | 183 | 0.7581 | 0.7231 | 0.7402 | 65 | 0.7910 | 0.7659 | 0.7783 | 0.9705 |
| 0.0124 | 27.0 | 21411 | 0.2280 | 0.8504 | 0.8438 | 0.8471 | 128 | 0.8391 | 0.8639 | 0.8513 | 169 | 0.8119 | 0.7455 | 0.7773 | 110 | 0.6435 | 0.6491 | 0.6463 | 114 | 0.6259 | 0.6891 | 0.6560 | 267 | 0.4 | 1.0 | 0.5714 | 2 | 0.8548 | 0.8689 | 0.8618 | 183 | 0.8627 | 0.6769 | 0.7586 | 65 | 0.7588 | 0.7697 | 0.7642 | 0.9702 |
| 0.0111 | 28.0 | 22204 | 0.2728 | 0.8917 | 0.8359 | 0.8629 | 128 | 0.8704 | 0.8343 | 0.8520 | 169 | 0.9059 | 0.7 | 0.7897 | 110 | 0.5833 | 0.6754 | 0.6260 | 114 | 0.6618 | 0.6816 | 0.6716 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8713 | 0.8142 | 0.8418 | 183 | 0.8837 | 0.5846 | 0.7037 | 65 | 0.7806 | 0.7437 | 0.7617 | 0.9692 |
| 0.0079 | 29.0 | 22997 | 0.2596 | 0.8661 | 0.8594 | 0.8627 | 128 | 0.8817 | 0.8817 | 0.8817 | 169 | 0.7436 | 0.7909 | 0.7665 | 110 | 0.616 | 0.6754 | 0.6444 | 114 | 0.6794 | 0.6667 | 0.6730 | 267 | 1.0 | 1.0 | 1.0 | 2 | 0.8681 | 0.8634 | 0.8658 | 183 | 0.8727 | 0.7385 | 0.8 | 65 | 0.7786 | 0.7794 | 0.7790 | 0.9705 |
| 0.0076 | 30.0 | 23790 | 0.2476 | 0.8088 | 0.8594 | 0.8333 | 128 | 0.8889 | 0.8994 | 0.8941 | 169 | 0.7909 | 0.7909 | 0.7909 | 110 | 0.6397 | 0.7632 | 0.6960 | 114 | 0.6727 | 0.6929 | 0.6827 | 267 | 0.3333 | 1.0 | 0.5 | 2 | 0.8641 | 0.8689 | 0.8665 | 183 | 0.6512 | 0.8615 | 0.7417 | 65 | 0.7591 | 0.8073 | 0.7824 | 0.9705 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
xinhui/distilbert-base-uncased-finetuned-imdb
|
xinhui
| 2022-09-09T08:22:33Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-09T08:11:19Z |
---
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: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Rongjiehuang/ProDiff
|
Rongjiehuang
| 2022-09-09T06:21:25Z | 0 | 7 | null |
[
"text-to-speech",
"neural-vocoder",
"diffusion probabilistic model",
"dataset:LJSpeech",
"arxiv:2204.09934",
"arxiv:2207.06389",
"license:other",
"region:us"
] |
text-to-speech
| 2022-09-08T07:25:21Z |
---
license: other
tags:
- text-to-speech
- neural-vocoder
- diffusion probabilistic model
inference: false
datasets:
- LJSpeech
extra_gated_prompt: |-
One more step before getting this model.
This model is open access and available to all, with a license further specifying rights and usage.
Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
# ProDiff and FastDiff Model Card
## Key Features
- **Extremely-Fast** diffusion text-to-speech synthesis pipeline for potential **industrial deployment**.
- **Tutorial and code base** for speech diffusion models.
- More **supported diffusion mechanism** (e.g., guided diffusion) will be available.
## Model Details
- **Model type:** Diffusion-based text-to-speech generation model
- **Language(s):** English
- **Model Description:** A conditional diffusion probabilistic model capable of generating high fidelity speech efficiently.
- **Resources for more information:** [FastDiff GitHub Repository](https://github.com/Rongjiehuang/FastDiff), [FastDiff Paper](https://arxiv.org/abs/2204.09934). [ProDiff GitHub Repository](https://github.com/Rongjiehuang/ProDiff), [ProDiff Paper](https://arxiv.org/abs/2207.06389).
- **Cite as:**
@inproceedings{huang2022prodiff,
title={ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech},
author={Huang, Rongjie and Zhao, Zhou and Liu, Huadai and Liu, Jinglin and Cui, Chenye and Ren, Yi},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
year={2022}
@inproceedings{huang2022fastdiff,
title={FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis},
author={Huang, Rongjie and Lam, Max WY and Wang, Jun and Su, Dan and Yu, Dong and Ren, Yi and Zhao, Zhou},
booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
year={2022}
-
*This model card was written based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
farleyknight/cnn_dailymail-summarization-t5-small-2022-09-08
|
farleyknight
| 2022-09-09T05:47:09Z | 37 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-08T12:06:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: cnn_dailymail-summarization-t5-small-2022-09-08
results:
- task:
name: Summarization
type: summarization
dataset:
name: cnn_dailymail 3.0.0
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 41.4235
---
<!-- 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. -->
# cnn_dailymail-summarization-t5-small-2022-09-08
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6455
- Rouge1: 41.4235
- Rouge2: 19.0263
- Rougel: 29.2892
- Rougelsum: 38.6338
- Gen Len: 73.7273
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8435 | 0.28 | 10000 | 1.6998 | 24.3321 | 11.599 | 20.1028 | 22.9562 | 18.9997 |
| 1.8464 | 0.56 | 20000 | 1.6814 | 24.4483 | 11.6789 | 20.1798 | 23.0508 | 18.9996 |
| 1.8332 | 0.84 | 30000 | 1.6738 | 24.5531 | 11.7949 | 20.2834 | 23.1588 | 18.9994 |
| 1.8054 | 1.11 | 40000 | 1.6636 | 24.6194 | 11.843 | 20.3375 | 23.2259 | 18.9991 |
| 1.7958 | 1.39 | 50000 | 1.6597 | 24.5017 | 11.7755 | 20.2439 | 23.1148 | 18.9998 |
| 1.8095 | 1.67 | 60000 | 1.6546 | 24.5126 | 11.8043 | 20.2603 | 23.1175 | 18.9999 |
| 1.8127 | 1.95 | 70000 | 1.6521 | 24.4845 | 11.8136 | 20.2557 | 23.1089 | 18.9999 |
| 1.7952 | 2.23 | 80000 | 1.6488 | 24.6217 | 11.8877 | 20.3555 | 23.2514 | 18.9996 |
| 1.7863 | 2.51 | 90000 | 1.6477 | 24.5616 | 11.8489 | 20.3021 | 23.1754 | 18.9996 |
| 1.7824 | 2.79 | 100000 | 1.6464 | 24.5852 | 11.8531 | 20.3172 | 23.2089 | 18.9998 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
isyslab/NeuroPred-PLM
|
isyslab
| 2022-09-09T05:32:25Z | 0 | 2 | null |
[
"region:us"
] | null | 2022-09-09T05:18:21Z |
## NeuroPred-PLM: an interpretable and robust model for prediction of neuropeptides by protein language model
[](https://pypi.org/project/NeuroPredPLM/) [](https://pypi.org/project/NeuroPredPLM/) [](./LICENSE) 
### Requirements
To install requirements:
```
# latest version
pip install git+https://github.com/ISYSLAB-HUST/NeuroPred-PLM.git
# stable version
pip install NeuroPredPLM
```
### Usage [<img src="https://colab.research.google.com/assets/colab-badge.svg">](https://colab.research.google.com/github/ISYSLAB-HUST/NeuroPred-PLM/blob/master/notebook/NeuroPred_PLM_test.ipynb)
```
import torch
from NeuroPredPLM.predict import predict
data = [
("peptide_1", "IGLRLPNMLKF"),
("peptide_2", "QAAQFKVWSASELVD"),
("peptide_3","LRSPKMMHKSGCFGRRLDRIGSLSGLGCNVLRKY")
]
device = "cuda" if torch.cuda.is_available() else "cpu"
neuropeptide_pred = predict(data,device)
# {peptide_id:[Type:int(1->neuropeptide,0->non-neuropeptide), attention score:nd.array]}
```
### License
Released under the [MIT license](LICENSE).
### Contact
If you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at wanglei94@hust.edu.cn.
|
sd-concepts-library/koko-dog
|
sd-concepts-library
| 2022-09-09T04:50:02Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T04:49:51Z |
---
license: mit
---
### Koko Dog on Stable Diffusion
This is the `<koko-dog>` 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`:
|
misterruffian/Artistic-Illustration-Diffusion-Model
|
misterruffian
| 2022-09-09T04:02:44Z | 0 | 1 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-09-07T13:15:31Z |
---
license: cc-by-4.0
---
Install Instructions
1. Download Model into Google Drive > AI > DiscoDiffusion > Models
2. Add path '/content/drive/MyDrive/AI/DiscoDiffusion/Models/AIDM_130k_v01.pt' to Disco Diffusion Step 2 > Custom Model > Custom Path
3. In Custom Model Settings add the following code below
4. Run All
Custom Model Settings
---
#@markdown ####**Custom Model Settings:**
if diffusion_model == 'custom':
model_config.update({
'attention_resolutions': '32, 16, 8',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'image_size': 512,
'learn_sigma': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_heads': 4,
'num_res_blocks': 2,
'resblock_updown': True,
'use_checkpoint': use_checkpoint,
'use_fp16': True,
'use_scale_shift_norm': True,
})
|
huggingtweets/emmanuelmacron
|
huggingtweets
| 2022-09-09T03:06:06Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/emmanuelmacron/1662692761917/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/1550535324501164032/0lTW_4tj_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">Emmanuel Macron</div>
<div style="text-align: center; font-size: 14px;">@emmanuelmacron</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 Emmanuel Macron.
| Data | Emmanuel Macron |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 175 |
| Short tweets | 68 |
| Tweets kept | 3007 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/304usdvs/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 @emmanuelmacron's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g4j2z3e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g4j2z3e/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/emmanuelmacron')
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)
|
Jasmine8596/distilbert-finetuned-imdb
|
Jasmine8596
| 2022-09-09T02:41:29Z | 70 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-08T23:25:43Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jasmine8596/distilbert-finetuned-imdb
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. -->
# Jasmine8596/distilbert-finetuned-imdb
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:
- Train Loss: 2.8423
- Validation Loss: 2.6128
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8423 | 2.6128 | 0 |
### Framework versions
- Transformers 4.22.0.dev0
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
whaleloops/longt5-tglobal-large-16384-pubmed-10k_steps
|
whaleloops
| 2022-09-09T02:23:40Z | 114 | 2 |
transformers
|
[
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"biomedical",
"text summarization",
"en",
"dataset:ccdv/pubmed-summarization",
"arxiv:2112.07916",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-09T01:36:53Z |
---
language: en
tags:
- biomedical
- text summarization
datasets:
- ccdv/pubmed-summarization
license: apache-2.0
---
## Introduction
[Google's LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf).
This is an unofficial longt5-large-16384-pubmed-10k_steps checkpoint. I.e., this is a large configuration of the LongT5 model with a transient-global attention fine-tuned on [pubmed summarization dataset](https://huggingface.co/datasets/ccdv/pubmed-summarization) for 10,000 training steps.
## Results and Fine-tuning Details
The fine-tuned model achieves the following results on the evaluation set using `beam_search=1` and without any specific calibration of generation parameters are presented below, altogether with the results from the original paper (the original scores are higher, very likely due to a higher number of training steps).
| Metric | Score | Score (original paper)
| --- | --- | --- |
| Rouge-1 | 49.11 | 49.98 |
| Rouge-2 | 23.66 | 24.69 |
| Rouge-L | 31.19 | x |
| Rouge-Lsum | 45.87 | 46.46 |
Following previous [setup](https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps/), the training parameters follow the ones specified in the paper. We accumulated batch size to 128 examples and used `Adafactor` optimizer with a constant learning rate `0.001`. The training took about 14 days on 2 A100 GPUs.
The only specific adjustment, I made for the training, was to pad very short input articles (less than 16 tokens) to at least 16 tokens as this sequences do not contribute to gradient creation in the *transient-global* attention, which resulted in training crashes when DDP used.
## Usage
```python
LONG_ARTICLE = """"anxiety affects quality of life in those living
with parkinson 's disease ( pd ) more so than
overall cognitive status , motor deficits , apathy
, and depression [ 13 ] . although anxiety and
depression are often related and coexist in pd
patients , recent research suggests that anxiety
rather than depression is the most prominent and
prevalent mood disorder in pd [ 5 , 6 ] . yet ,
our current understanding of anxiety and its
impact on cognition in pd , as well as its neural
basis and best treatment practices , remains
meager and lags far behind that of depression .
overall , neuropsychiatric symptoms in pd have
been shown to be negatively associated with
cognitive performance . for example , higher
depression scores have been correlated with lower
scores on the mini - mental state exam ( mmse ) [
8 , 9 ] as well as tests of memory and executive
functions ( e.g. , attention ) [ 1014 ] . likewise
, apathy and anhedonia in pd patients have been
associated with executive dysfunction [ 10 , 1523
] . however , few studies have specifically
investigated the relationship between anxiety and
cognition in pd . one study showed a strong
negative relationship between anxiety ( both state
and trait ) and overall cognitive performance (
measured by the total of the repeatable battery
for the assessment of neuropsychological status
index ) within a sample of 27 pd patients .
furthermore , trait anxiety was negatively
associated with each of the cognitive domains
assessed by the rbans ( i.e. , immediate memory ,
visuospatial construction , language , attention ,
and delayed memory ) . two further studies have
examined whether anxiety differentially affects
cognition in patients with left - sided dominant
pd ( lpd ) versus right - sided dominant pd ( rpd
) ; however , their findings were inconsistent .
the first study found that working memory
performance was worse in lpd patients with anxiety
compared to rpd patients with anxiety , whereas
the second study reported that , in lpd , apathy
but not anxiety was associated with performance on
nonverbally mediated executive functions and
visuospatial tasks ( e.g. , tmt - b , wms - iii
spatial span ) , while in rpd , anxiety but not
apathy significantly correlated with performance
on verbally mediated tasks ( e.g. , clock reading
test and boston naming test ) . furthermore ,
anxiety was significantly correlated with
neuropsychological measures of attention and
executive and visuospatial functions . taken
together , it is evident that there are limited
and inconsistent findings describing the
relationship between anxiety and cognition in pd
and more specifically how anxiety might influence
particular domains of cognition such as attention
and memory and executive functioning . it is also
striking that , to date , no study has examined
the influence of anxiety on cognition in pd by
directly comparing groups of pd patients with and
without anxiety while excluding depression . given
that research on healthy young adults suggests
that anxiety reduces processing capacity and
impairs processing efficiency , especially in the
central executive and attentional systems of
working memory [ 26 , 27 ] , we hypothesized that
pd patients with anxiety would show impairments in
attentional set - shifting and working memory
compared to pd patients without anxiety .
furthermore , since previous work , albeit limited
, has focused on the influence of symptom
laterality on anxiety and cognition , we also
explored this relationship . seventeen pd patients
with anxiety and thirty - three pd patients
without anxiety were included in this study ( see
table 1 ) . the cross - sectional data from these
participants was taken from a patient database
that has been compiled over the past 8 years (
since 2008 ) at the parkinson 's disease research
clinic at the brain and mind centre , university
of sydney . inclusion criteria involved a
diagnosis of idiopathic pd according to the united
kingdom parkinson 's disease society brain bank
criteria and were confirmed by a neurologist (
sjgl ) . patients also had to have an adequate
proficiency in english and have completed a full
neuropsychological assessment . ten patients in
this study ( 5 pd with anxiety ; 5 pd without
anxiety ) were taking psychotropic drugs ( i.e. ,
benzodiazepine or selective serotonin reuptake
inhibitor ) . patients were also excluded if they
had other neurological disorders , psychiatric
disorders other than affective disorders ( such as
anxiety ) , or if they reported a score greater
than six on the depression subscale of the
hospital anxiety and depression scale ( hads ) .
thus , all participants who scored within a
depressed ( hads - d > 6 ) range were excluded
from this study , in attempt to examine a refined
sample of pd patients with and without anxiety in
order to determine the independent effect of
anxiety on cognition . this research was approved
by the human research ethics committee of the
university of sydney , and written informed
consent was obtained from all participants . self
- reported hads was used to assess anxiety in pd
and has been previously shown to be a useful
measure of clinical anxiety in pd . a cut - off
score of > 8 on the anxiety subscale of the hads (
hads - a ) was used to identify pd cases with
anxiety ( pda+ ) , while a cut - off score of < 6
on the hads - a was used to identify pd cases
without anxiety ( pda ) . this criterion was more
stringent than usual ( > 7 cut - off score ) , in
effort to create distinct patient groups . the
neurological evaluation rated participants
according to hoehn and yahr ( h&y ) stages and
assessed their motor symptoms using part iii of
the revised mds task force unified parkinson 's
disease rating scale ( updrs ) . in a similar way
this was determined by calculating a total left
and right score from rigidity items 3035 ,
voluntary movement items 3643 , and tremor items
5057 from the mds - updrs part iii ( see table 1 )
. processing speed was assessed using the trail
making test , part a ( tmt - a , z - score ) .
attentional set - shifting was measured using the
trail making test , part b ( tmt - b , z - score )
. working memory was assessed using the digit span
forward and backward subtest of the wechsler
memory scale - iii ( raw scores ) . language was
assessed with semantic and phonemic verbal fluency
via the controlled oral word associated test (
cowat animals and letters , z - score ) . the
ability to retain learned verbal memory was
assessed using the logical memory subtest from the
wechsler memory scale - iii ( lm - i z - score ,
lm - ii z - score , % lm retention z - score ) .
the mini - mental state examination ( mmse )
demographic , clinical , and neuropsychological
variables were compared between the two groups
with the independent t - test or mann whitney u
test , depending on whether the variable met
parametric assumptions . chi - square tests were
used to examine gender and symptom laterality
differences between groups . all analyses employed
an alpha level of p < 0.05 and were two - tailed .
spearman correlations were performed separately in
each group to examine associations between anxiety
and/or depression ratings and cognitive functions
. as expected , the pda+ group reported
significant greater levels of anxiety on the hads
- a ( u = 0 , p < 0.001 ) and higher total score
on the hads ( u = 1 , p < 0.001 ) compared to the
pda group ( table 1 ) . groups were matched in age
( t(48 ) = 1.31 , p = 0.20 ) , disease duration (
u = 259 , p = 0.66 ) , updrs - iii score ( u =
250.5 , p = 0.65 ) , h&y ( u = 245 , p = 0.43 ) ,
ledd ( u = 159.5 , p = 0.80 ) , and depression (
hads - d ) ( u = 190.5 , p = 0.06 ) . additionally
, all groups were matched in the distribution of
gender ( = 0.098 , p = 0.75 ) and side - affected
( = 0.765 , p = 0.38 ) . there were no group
differences for tmt - a performance ( u = 256 , p
= 0.62 ) ( table 2 ) ; however , the pda+ group
had worse performance on the trail making test
part b ( t(46 ) = 2.03 , p = 0.048 ) compared to
the pda group ( figure 1 ) . the pda+ group also
demonstrated significantly worse performance on
the digit span forward subtest ( t(48 ) = 2.22 , p
= 0.031 ) and backward subtest ( u = 190.5 , p =
0.016 ) compared to the pda group ( figures 2(a )
and 2(b ) ) . neither semantic verbal fluency (
t(47 ) = 0.70 , p = 0.49 ) nor phonemic verbal
fluency ( t(47 ) = 0.39 , p = 0.70 ) differed
between groups . logical memory i immediate recall
test ( u = 176 , p = 0.059 ) showed a trend that
the pda+ group had worse new verbal learning and
immediate recall abilities than the pda group .
however , logical memory ii test performance ( u =
219 , p = 0.204 ) and logical memory % retention (
u = 242.5 , p = 0.434 ) did not differ between
groups . there were also no differences between
groups in global cognition ( mmse ) ( u = 222.5 ,
p = 0.23 ) . participants were split into lpd and
rpd , and then further group differences were
examined between pda+ and pda. importantly , the
groups remained matched in age , disease duration
, updrs - iii , dde , h&y stage , and depression
but remained significantly different on self -
reported anxiety . lpda+ demonstrated worse
performance on the digit span forward test ( t(19
) = 2.29 , p = 0.033 ) compared to lpda , whereas
rpda+ demonstrated worse performance on the digit
span backward test ( u = 36.5 , p = 0.006 ) , lm -
i immediate recall ( u = 37.5 , p = 0.008 ) , and
lm - ii ( u = 45.0 , p = 0.021 ) but not lm %
retention ( u = 75.5 , p = 0.39 ) compared to
rpda. this study is the first to directly compare
cognition between pd patients with and without
anxiety . the findings confirmed our hypothesis
that anxiety negatively influences attentional set
- shifting and working memory in pd . more
specifically , we found that pd patients with
anxiety were more impaired on the trail making
test part b which assessed attentional set -
shifting , on both digit span tests which assessed
working memory and attention , and to a lesser
extent on the logical memory test which assessed
memory and new verbal learning compared to pd
patients without anxiety . taken together , these
findings suggest that anxiety in pd may reduce
processing capacity and impair processing
efficiency , especially in the central executive
and attentional systems of working memory in a
similar way as seen in young healthy adults [ 26 ,
27 ] . although the neurobiology of anxiety in pd
remains unknown , many researchers have postulated
that anxiety disorders are related to
neurochemical changes that occur during the early
, premotor stages of pd - related degeneration [
37 , 38 ] such as nigrostriatal dopamine depletion
, as well as cell loss within serotonergic and
noradrenergic brainstem nuclei ( i.e. , raphe
nuclei and locus coeruleus , resp . , which
provide massive inputs to corticolimbic regions )
. over time , chronic dysregulation of
adrenocortical and catecholamine functions can
lead to hippocampal damage as well as
dysfunctional prefrontal neural circuitries [ 39 ,
40 ] , which play a key role in memory and
attention . recent functional neuroimaging work
has suggested that enhanced hippocampal activation
during executive functioning and working memory
tasks may represent compensatory processes for
impaired frontostriatal functions in pd patients
compared to controls . therefore , chronic stress
from anxiety , for example , may disrupt
compensatory processes in pd patients and explain
the cognitive impairments specifically in working
memory and attention seen in pd patients with
anxiety . it has also been suggested that
hyperactivation within the putamen may reflect a
compensatory striatal mechanism to maintain normal
working memory performance in pd patients ;
however , losing this compensatory activation has
been shown to contribute to poor working memory
performance . anxiety in mild pd has been linked
to reduced putamen dopamine uptake which becomes
more extensive as the disease progresses . this
further supports the notion that anxiety may
disrupt compensatory striatal mechanisms as well ,
providing another possible explanation for the
cognitive impairments observed in pd patients with
anxiety in this study . noradrenergic and
serotonergic systems should also be considered
when trying to explain the mechanisms by which
anxiety may influence cognition in pd . although
these neurotransmitter systems are relatively
understudied in pd cognition , treating the
noradrenergic and serotonergic systems has shown
beneficial effects on cognition in pd . selective
serotonin reuptake inhibitor , citalopram , was
shown to improve response inhibition deficits in
pd , while noradrenaline reuptake blocker ,
atomoxetine , has been recently reported to have
promising effects on cognition in pd [ 45 , 46 ] .
overall , very few neuroimaging studies have been
conducted in pd in order to understand the neural
correlates of pd anxiety and its underlying neural
pathology . future research should focus on
relating anatomical changes and neurochemical
changes to neural activation in order to gain a
clearer understanding on how these pathologies
affect anxiety in pd . to further understand how
anxiety and cognitive dysfunction are related ,
future research should focus on using advanced
structural and function imaging techniques to
explain both cognitive and neural breakdowns that
are associated with anxiety in pd patients .
research has indicated that those with amnestic
mild cognitive impairment who have more
neuropsychiatric symptoms have a greater risk of
developing dementia compared to those with fewer
neuropsychiatric symptoms . future studies should
also examine whether treating neuropsychiatric
symptoms might impact the progression of cognitive
decline and improve cognitive impairments in pd
patients . previous studies have used pd symptom
laterality as a window to infer asymmetrical
dysfunction of neural circuits . for example , lpd
patients have greater inferred right hemisphere
pathology , whereas rpd patients have greater
inferred left hemisphere pathology . thus ,
cognitive domains predominantly subserved by the
left hemisphere ( e.g. , verbally mediated tasks
of executive function and verbal memory ) might be
hypothesized to be more affected in rpd than lpd ;
however , this remains controversial . it has also
been suggested that since anxiety is a common
feature of left hemisphere involvement [ 48 , 49 ]
, cognitive domains subserved by the left
hemisphere may also be more strongly related to
anxiety . results from this study showed selective
verbal memory deficits in rpd patients with
anxiety compared to rpd without anxiety , whereas
lpd patients with anxiety had greater attentional
/ working memory deficits compared to lpd without
anxiety . although these results align with
previous research , interpretations of these
findings should be made with caution due to the
small sample size in the lpd comparison
specifically . recent work has suggested that the
hads questionnaire may underestimate the burden of
anxiety related symptomology and therefore be a
less sensitive measure of anxiety in pd [ 30 , 50
] . in addition , our small sample size also
limited the statistical power for detecting
significant findings . based on these limitations
, our findings are likely conservative and
underrepresent the true impact anxiety has on
cognition in pd . additionally , the current study
employed a very brief neuropsychological
assessment including one or two tests for each
cognitive domain . future studies are encouraged
to collect a more complex and comprehensive
battery from a larger sample of pd participants in
order to better understand the role anxiety plays
on cognition in pd . another limitation of this
study was the absence of diagnostic interviews to
characterize participants ' psychiatric symptoms
and specify the type of anxiety disorders included
in this study . future studies should perform
diagnostic interviews with participants ( e.g. ,
using dsm - v criteria ) rather than relying on
self - reported measures to group participants ,
in order to better understand whether the type of
anxiety disorder ( e.g. , social anxiety , phobias
, panic disorders , and generalized anxiety )
influences cognitive performance differently in pd
. one advantage the hads questionnaire provided
over other anxiety scales was that it assessed
both anxiety and depression simultaneously and
allowed us to control for coexisting depression .
although there was a trend that the pda+ group
self - reported higher levels of depression than
the pda group , all participants included in the
study scored < 6 on the depression subscale of the
hads . controlling for depression while assessing
anxiety has been identified as a key shortcoming
in the majority of recent work . considering many
previous studies have investigated the influence
of depression on cognition in pd without
accounting for the presence of anxiety and the
inconsistent findings reported to date , we
recommend that future research should try to
disentangle the influence of anxiety versus
depression on cognitive impairments in pd .
considering the growing number of clinical trials
for treating depression , there are few if any for
the treatment of anxiety in pd . anxiety is a key
contributor to decreased quality of life in pd and
greatly requires better treatment options .
moreover , anxiety has been suggested to play a
key role in freezing of gait ( fog ) , which is
also related to attentional set - shifting [ 52 ,
53 ] . future research should examine the link
between anxiety , set - shifting , and fog , in
order to determine whether treating anxiety might
be a potential therapy for improving fog ."""
import torch
from transformers import AutoTokenizer, LongT5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda")
model = LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps", return_dict_in_generate=True).to("cuda")
sequences = model.generate(input_ids).sequences
summary = tokenizer.batch_decode(sequences)
```
|
sd-concepts-library/johnny-silverhand
|
sd-concepts-library
| 2022-09-09T02:15:43Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T02:15:36Z |
---
license: mit
---
### Johnny Silverhand on Stable Diffusion
This is the `<johnny-silverhand>` 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`:
|
UmberH/distilbert-base-uncased-finetuned-cola
|
UmberH
| 2022-09-09T01:53:53Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T20:21:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5456062114587601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8381
- Matthews Correlation: 0.5456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5245 | 1.0 | 535 | 0.5432 | 0.4249 |
| 0.3514 | 2.0 | 1070 | 0.5075 | 0.4874 |
| 0.2368 | 3.0 | 1605 | 0.5554 | 0.5403 |
| 0.1712 | 4.0 | 2140 | 0.7780 | 0.5246 |
| 0.1254 | 5.0 | 2675 | 0.8381 | 0.5456 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/cecilio-g
|
sd-concepts-library
| 2022-09-09T00:35:42Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T00:35:36Z |
---
license: mit
---
### Cecilio G on Stable Diffusion
This is the `<cecilio-g>` 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`:
|
sd-concepts-library/bonzi-monkey
|
sd-concepts-library
| 2022-09-09T00:03:11Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T00:03:05Z |
---
license: mit
---
### bonzi monkey on Stable Diffusion
This is the `<bonzi>` 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`:
|
sd-concepts-library/shrunken-head
|
sd-concepts-library
| 2022-09-08T22:23:57Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T22:23:46Z |
---
license: mit
---
### shrunken head on Stable Diffusion
This is the `<shrunken-head>` 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 a `style`:
|
aware-ai/m-ctc-t-german
|
aware-ai
| 2022-09-08T22:01:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mctct",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-07T18:18:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: m-ctc-t-german
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m-ctc-t-german
This model is a fine-tuned version of [speechbrain/m-ctc-t-large](https://huggingface.co/speechbrain/m-ctc-t-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 14.0387
- Wer: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 448
- eval_batch_size: 448
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 896
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1883 | 1.0 | 511 | 3.5192 | 1.0 |
| 3.1097 | 2.0 | 1022 | 11.0713 | 1.0000 |
| 3.0541 | 3.0 | 1533 | 14.0387 | 1.0000 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
IIIT-L/xlm-roberta-base-finetuned-combined-DS
|
IIIT-L
| 2022-09-08T21:22:20Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T20:48:41Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-finetuned-combined-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-combined-DS
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0232
- Accuracy: 0.6362
- Precision: 0.6193
- Recall: 0.6204
- F1: 0.6160
## 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: 4.1187640010910775e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- 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 | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0408 | 1.0 | 711 | 1.0206 | 0.5723 | 0.5597 | 0.5122 | 0.4897 |
| 0.9224 | 2.0 | 1422 | 0.9092 | 0.5695 | 0.5745 | 0.5610 | 0.5572 |
| 0.8395 | 3.0 | 2133 | 0.8878 | 0.6088 | 0.6083 | 0.6071 | 0.5981 |
| 0.7418 | 3.99 | 2844 | 0.8828 | 0.6088 | 0.6009 | 0.6068 | 0.5936 |
| 0.6484 | 4.99 | 3555 | 0.9636 | 0.6355 | 0.6235 | 0.6252 | 0.6184 |
| 0.5644 | 5.99 | 4266 | 1.0232 | 0.6362 | 0.6193 | 0.6204 | 0.6160 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
GItaf/gpt2-gpt2-finetuned-mbti-0909
|
GItaf
| 2022-09-08T20:36:02Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T17:02:09Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-finetuned-mbti-0909
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. -->
# gpt2-gpt2-finetuned-mbti-0909
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.9795
- eval_runtime: 44.8441
- eval_samples_per_second: 38.69
- eval_steps_per_second: 4.839
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
GItaf/bert2bert-no-cross-attn-decoder
|
GItaf
| 2022-09-08T20:26:21Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-05T08:11:45Z |
---
tags:
- generated_from_trainer
- text-generation
widget:
parameters:
- max_new_tokens = 100
model-index:
- name: bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
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-bert-base-uncased-finetuned-mbti-0909
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0549
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2244 | 1.0 | 1735 | 5.7788 |
| 4.8483 | 2.0 | 3470 | 5.7647 |
| 4.7578 | 3.0 | 5205 | 5.9016 |
| 4.5606 | 4.0 | 6940 | 5.9895 |
| 4.4314 | 5.0 | 8675 | 6.0549 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/line-art
|
sd-concepts-library
| 2022-09-08T19:30:01Z | 0 | 47 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T19:29:47Z |
---
license: mit
---
### Line Art on Stable Diffusion
This is the `<line-art>` 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 a `style`:
Images via Freepik.com
|
ighita/ddpm-butterflies-128
|
ighita
| 2022-09-08T19:17:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-06T10:19:48Z |
---
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/ighita/ddpm-butterflies-128/tensorboard?#scalars)
|
sd-concepts-library/malika-favre-art-style
|
sd-concepts-library
| 2022-09-08T19:08:58Z | 0 | 28 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T19:08:53Z |
---
license: mit
---
### Malika Favre Art Style on Stable Diffusion
This is the `<malika-favre>` 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 a `style`:
|
sd-concepts-library/art-brut
|
sd-concepts-library
| 2022-09-08T18:40:33Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T18:40:22Z |
---
license: mit
---
### art brut on Stable Diffusion
This is the `<art-brut>` 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 a `style`:
|
orhanxakarsu/turkisPoes-ds-mini-model
|
orhanxakarsu
| 2022-09-08T18:24:22Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T16:21:13Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: orhanxakarsu/turkisPoes-ds-mini-model
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. -->
# orhanxakarsu/turkisPoes-ds-mini-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.8299
- Epoch: 2
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4904, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 7.5194 | 0 |
| 6.8297 | 1 |
| 6.8299 | 2 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/apulian-rooster-v0-1
|
sd-concepts-library
| 2022-09-08T17:31:44Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T16:14:06Z |
---
license: mit
---
### apulian-rooster-v0.1 on Stable Diffusion
--
# Inspired by the design of the Galletto (rooster) typical of ceramics and pottery made in Grottaglie, Puglia (Italy).
This is the `<apulian-rooster-v0.1>` 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 a `style`:
|
bhorine/ddpm-butterflies-128
|
bhorine
| 2022-09-08T17:27:54Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-08T16:15:52Z |
---
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/bhorine/ddpm-butterflies-128/tensorboard?#scalars)
|
huggingtweets/piemadd
|
huggingtweets
| 2022-09-08T16:20:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T16:16:57Z |
---
language: en
thumbnail: http://www.huggingtweets.com/piemadd/1662653961299/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/1521050682983424003/yERaHagV_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">Piero Maddaleni 2027</div>
<div style="text-align: center; font-size: 14px;">@piemadd</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 Piero Maddaleni 2027.
| Data | Piero Maddaleni 2027 |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 322 |
| Short tweets | 540 |
| Tweets kept | 2380 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jem4xdn0/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 @piemadd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6e8s7bst) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6e8s7bst/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/piemadd')
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)
|
Guruji108/xlm-roberta-base-finetuned-panx-de
|
Guruji108
| 2022-09-08T16:00:40Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-05T17:49:47Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.863677639046538
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1343
- F1: 0.8637
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 |
| 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 |
| 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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